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Introduction The priority queue in the data structure is an extension of the “normal” queue. It is an abstract data type that contains a group of items. It is like the “normal” queue except that the dequeuing elements follow a priority order. The priority order dequeues those items first that have the highest priority. Each priority queue in DS comes with its own importance and is essential for handling various task priorities with ease. Their adaptability is key to solving many computer science problems effectively. They are widely used in software development for effectively managing various elements.  They play a vital role in operating systems by prioritizing important tasks, which helps improve system performance. Networks also rely on them to handle data packets, ensuring timely delivery of essential information. Algorithms like Dijkstra’s shortest path algorithm use priority queues to find the most efficient paths.  Additionally, they assist in processing events in simulations based on their importance. They serve as a versatile tool in computer science, aiding in handling various tasks and problems across different applications.  This blog will give you a deeper understanding of the priority queue and its implementation in the C programming language. Read on to learn everything from priority queue example in data structure to the deletion algorithm for priority queue.  What is a Priority Queue? It is an abstract data type that provides a way to maintain the dataset. The “normal” queue follows a pattern of first-in-first-out. It dequeues elements in the same order followed at the time of insertion operation. However, the element order in a priority queue depends on the element’s priority in that queue. The priority queue moves the highest priority elements at the beginning of the priority queue and the lowest priority elements at the back of the priority queue. It supports only those elements that are comparable. Hence, a priority queue in the data structure arranges the elements in either ascending or descending order. You can think of a priority queue as several patients waiting in line at a hospital. Here, the situation of the patient defines the priority order. The patient with the most severe injury would be the first in the queue. What are the Characteristics of a Priority Queue? A priority queue in data structure is a variant of a traditional queue that stands out due to its priority-based organization. Unlike a standard queue, it distinguishes elements by assigning priority values that change how they’re accessed.  The design of a priority queue in DS optimizes the management and processing of elements based on their priorities. Its applications span various fields, including scheduling tasks, handling network data, and algorithmic design, where prioritization is crucial for efficient operations and problem-solving. A queue is termed as a priority queue if it has the following characteristics: Each item has some priority associated with it. Each item in a priority queue is tagged with a priority value, signifying its importance or urgency. This helps distinguish between elements based on specific criteria, like prioritizing critical tasks over those of less urgent ones. An item with the highest priority is moved at the front and deleted first. Unlike standard queues, a priority queue places the highest-priority item at the front. This ensures immediate access and processing of crucial elements and alters the order in which items are handled based on their priority. If two elements share the same priority value, then the priority queue follows the first-in-first-out principle for de queue operation. When multiple items share the same priority, a priority queue follows the ‘first-in-first-out’ principle. This means items with identical priorities are processed in the order they were added, ensuring fairness in their treatment. What are the Types of Priority Queue? A priority queue is of two types: Ascending Order Priority Queue An ascending priority queue arranges elements based on their priority values in ascending order. This means the element with the smallest priority value sits at the front for dequeuing. When inserting new elements, they’re placed according to their priority, maintaining the order.  For dequeuing, the element with the smallest priority (considered the highest priority) is removed first. This example of priority queue is appropriate when handling elements with the lowest priority is a top priority, ensuring that less urgent tasks or data are processed foremost. Descending Order Priority Queue A descending order priority queue arranges elements by their priority values in descending order. The item with the highest priority value takes precedence for dequeuing. New elements are added accordingly, maintaining this order.  During dequeuing, the highest-priority element, holding the utmost significance, is retrieved first. It suits scenarios where handling the most crucial or urgent elements is paramount, ensuring they’re processed promptly.  This queue is beneficial when prioritizing tasks, events, or data with the highest urgency. Implementations can employ various structures like sorted arrays or linked lists to maintain this descending priority order efficiently. Also read: Free data structures and algorithm course! Ascending Order Priority Queue An ascending order priority queue gives the highest priority to the lower number in that queue. For example, you have six numbers in the priority queue that are 4, 8, 12, 45, 35, 20. Firstly, you will arrange these numbers in ascending order. The new list is as follows: 4, 8, 12, 20. 35, 45. In this list, 4 is the smallest number. Hence, the ascending order priority queue treats number 4 as the highest priority. 4 8 12 20 35 45 In the above table, 4 has the highest priority, and 45 has the lowest priority. Must read: Learn excel online free! Descending Order Priority Queue A descending order priority queue gives the highest priority to the highest number in that queue. For example, you have six numbers in the priority queue that are 4, 8, 12, 45, 35, 20. Firstly, you will arrange these numbers in ascending order. The new list is as follows: 45, 35, 20, 12, 8, 4. In this list, 45 is the highest number. Hence, the descending order priority queue treats number 45 as the highest priority. 45 35 20 12 8 4 In the above table, 4 has the lowest priority, and 45 has the highest priority. Our learners also read: Free Python Course with Certification Implementation of the Priority Queue in Data Structure There are several types of priority queue in data structure, and each come with a separate use case for separate scenarios.  You can implement the priority queues in one of the following ways: Linked list Binary heap Arrays Binary search tree The binary heap is the most efficient method for implementing the priority queue in the data structure. The below tables summarize the complexity of different operations in a priority queue. Operation Unordered Array Ordered Array Binary Heap Binary Search Tree Insert 0(1) 0(N) 0(log(N)) 0(log(N)) Peek 0(N) 0(1) 0(1) 0(1) Delete 0(N) 0(1) 0(log (N)) 0(log(N)) Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Linked List A linked list used as a priority queue operates by arranging elements according to their priorities. When an element is added, it finds its place in the list based on its priority level, aligning either from the lowest to the highest or vice versa.  Accessing elements involves scanning through the list to find the one with the highest or lowest priority. Deleting elements follows their priority order, removing them in line with their importance. Linked lists allow for flexible operations such as adding, removing, and locating high or low-priority elements.  However, due to its structure, pinpointing specific elements might take longer than other data structures. Deciding to use a linked list as a priority queue depends on balancing its flexibility with the potential trade-offs in terms of access speed for certain tasks or systems. Binary Heap A tree-based data structure with a specific arrangement that satisfies the heap property is known as a binary heap. When employed as a priority queue, it provides efficient access to the highest (in a max heap) or lowest (in a min heap) priority element. A binary heap priority queue offers an efficient way to manage priorities by organizing elements in a hierarchical tree structure. It provides quick access to the highest or lowest priority element, making it valuable in scenarios where prioritization and efficient retrieval of extreme values are essential, such as in scheduling, graph algorithms, and sorting. A binary heap tree organises all the parent and child nodes of the tree in a particular order. In a binary heap tree, a parent node can have a maximum of 2 child nodes. The value of the parent node could either be: equal to or less than the value of a child node.This ensures that the largest element (the maximum value) is at the root node. It guarantees that each parent node holds a value lesser than or equal to its child nodes. It preserves the hierarchical structure where every level maintains this property. As a result, the maximum value is always present at the root of the heap. equal to or more than the value of a child node.The value of the parent node is equal to or more than the values of its child nodes. This ensures that the smallest element (the minimum value) is at the root node. It guarantees that each parent node holds a value greater than or equal to its child nodes, maintaining the hierarchical arrangement where every level follows this rule. Consequently, the minimum value is always at the root of the heap. The above process divides the binary heap into two types: max heap and min-heap. Array Arrays provide a solid foundation for building a priority queue in data structures. They organize elements based on their priorities, often in order from lowest to highest or vice versa.  Each element’s position in the array corresponds to its priority level, allowing quick access to high or low-priority items. Adding elements involves placing them in the array according to their priority and possibly readjusting to maintain the order.  Deleting elements often targets the highest or lowest priority element, usually found at the start or end of the array. Arrays offer fast access to elements using their index. However, their fixed size might need adjustment as the queue changes, affecting performance and memory use. While arrays efficiently handle priority-based access, their size limitations and potential resizing issues need consideration when adapting to varying system needs. Max Heap The max heap is a binary heap in which a parent node has a value either equal to or greater than the child node value. The root node of the tree has the highest value. This design ensures that the biggest value, the top priority, sits at the root. Inserting elements means placing them in the right spot to maintain this order.  Deleting involves replacing the root with the last element and adjusting to maintain the structure. Max heaps are useful in priority queues for quick access to the highest priority and in sorting algorithms like heap sort. Their primary strength lies in quickly accessing the maximum value, making them valuable for tasks prioritizing the largest elements. Watch our Webinar on Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4   Inserting an Element in a Max Heap Binary Tree You can perform the following steps to insert an element/number in the priority queue in the data structure. The algorithm scans the tree from top to bottom and left to right to find an empty slot. It then inserts the element at the last node in the tree. After inserting the element, the order of the binary tree is disturbed. You must swap the data with each other to sort the order of the max heap binary tree. You must keep shuffling the data until the tree satisfies the max-heap property. Algorithm to Insert an Element in a Max Heap Binary Tree If the tree is empty and contains no node,     create a new parent node newElement. else (a parent node is already available)     insert the newElement at the end of the tree (i.e., last node of the tree from left to right.) max-heapify the tree Deleting an Element in a Max Heap Binary Tree You can perform the following steps to delete an element in the Priority Queue in Data Structure. Choose the element that you want to delete from the binary tree.Start the deletion process by singling out the element you intend to remove from the binary tree. Typically, this focuses on the element with the highest priority, especially in a max heap scenario. Shift the data at the end of the tree by swapping it with the last node data.To efficiently maintain the structure of the binary tree, replace your element of choice with the data from the last node in the tree. This process involves swapping the data of the element to be deleted with the data from the last node. This action ensures that the tree remains complete. Remove the last element of the binary tree.Once the data has been swapped, the last node of the binary tree, which now contains the element to be deleted, is removed. This action effectively eliminates the duplicate or now-relocated element from the tree. After deleting the element, the order of the binary tree is disturbed. You must sort the order to satisfy the property of max-heap. You must keep shuffling the data until the tree meets the max-heap property.This process involves recursively shifting the element downwards in the tree until it finds the appropriate position according to the max-heap property. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Algorithm to Delete an Element in a Max Heap Binary Tree If the elementUpForDeletion is the lastNode, delete the elementUpForDeletion else replace elementUpForDeletion with the lastNode delete the elementUpForDeletion max-heapify the tree Find the Maximum or Minimum Element in a Max Heap Binary Tree In a max heap binary tree, the find operation returns the parent node (the highest element) of the tree. Algorithm to Find the Max or Min in a Max Heap Binary Tree return ParentNode Program Implementation of the Priority Queue using the Max Heap Binary Tree #include <stdio.h>  int binary_tree = 10; int max_heap = 0; const int test = 100000;   void swap( int *x, int *y ) {   int a;   a = *x;   *x= *y;   *y = a; }   //Code to find the parent in the max heap tree int findParentNode(int node[], int root) {   if ((root > 1) && (root < binary_tree)) { return root/2;   }   return -1; }   void max_heapify(int node[], int root) {   int leftNodeRoot = findLeftChild(node, root);   int rightNodeRoot = findRightChild(node, root);     // finding highest among root, left child and right child   int highest = root;     if ((leftNodeRoot <= max_heap) && (leftNodeRoot >0)) { if (node[leftNodeRoot] > node[highest]) {    highest = leftNodeRoot; }   }     if ((rightNodeRoot <= max_heap) && (rightNodeRoot >0)) { if (node[rightNodeRoot] > node[highest]) {    highest = rightNodeRoot; }   }       if (highest != root) { swap(&node[root], &node[highest]);     max_heapify(node, highest);   } }   void create_max_heap(int node[]) {   int d;   for(d=max_heap/2; d>=1; d–) {     max_heapify(node, d);   } }   int maximum(int node[]) {   return node[1]; }   int find_max(int node[]) {   int maxNode = node[1];   node[1] = node[max_heap];   max_heap–;   max_heapify(node, 1);   return maxNode; } void descend_key(int node[], int node, int key) {   A[root] = key;   max_heapify(node, root); } void increase_key(int node[], int root, int key) {   node[root] = key;   while((root>1) && (node[findParentNode(node, root)] < node[root])) { swap(&node[root], &node[findParentNode(node, root)]); root = findParentNode(node, root);   } }   void insert(int node[], int key) {   max_heap++;   node[max_heap] = -1*test;   increase_key(node, max_heap, key); }   void display_heap(int node[]) {   int d;   for(d=1; d<=max_heap; d++) {     printf(“%d\n”,node[d]);   }   printf(“\n”); }   int main() {   int node[binary_tree];   insert(node, 10);   insert(node, 4);   insert(node, 20);   insert(node, 50);   insert(node, 1);   insert(node, 15);     display_heap(node);     printf(“%d\n\n”, maximum(node));   display_heap(node);     printf(“%d\n”, extract_max(node));   printf(“%d\n”, extract_max(node));   return 0; } Min Heap The min-heap is a binary heap in which a parent node has a value equal to or lesser than the child node value. The root node of the tree has the lowest value. It’s often represented using arrays and maintains a complete binary tree structure for efficient storage. New elements are added by appending and adjusting their position to maintain the min-heap property.  To delete the minimum element (root), it’s replaced with the last element while preserving the heap’s structure. Min heaps are handy in priority queues for fast access to the lowest priority. They’re used in Prim’s algorithm and heap sort due to their efficiency in handling smaller values. You can implement the min-heap in the same manner as the max-heap except reverse the order. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Conclusion A priority queue in DS serves as a crucial tool, managing elements based on their priorities. Whether applied in algorithms, simulations, or organizing events, the priority queue ensures the timely processing of high-priority elements.  Using efficient structures such as binary heaps or arrays, it optimizes computational processes across different scenarios, enhancing system efficiency and responsiveness. This foundational concept significantly contributes to smoother task management and streamlined operations in numerous applications within the digital landscape. The examples given in the article are only for explanatory purposes. You can modify the statements given above as per your requirements. In this blog, we learned about the concept of the priority queue in the data structure. You can try out the example to strengthen your data structure knowledge.   If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.

by Rohit Sharma

15 Jul 2024

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101684

Why data mining techniques are important like never before? Businesses these days are collecting data at a very striking rate. The sources of this enormous data stream are varied. It could come from credit card transactions, publicly available customer data, data from banks and financial institutions, as well as the data that users have to provide just to use and download an application on their laptops, mobile phones, tablets, and desktops. It is not easy to store such massive amounts of data. So, many relational database servers are being continuously built for this purpose. Online transactional protocol or OLTP systems are also being developed to store all that into different database servers. OLTP systems play a vital role in helping businesses function smoothly. It is these systems that are responsible for storing data that comes out of the smallest of transactions into the database. So, data related to sale, purchase, human capital management, and other transactions are stored in database servers by OLTP systems.  Now, top executives need access to facts based on data to base their decisions on. This is where online analytical processing or OLAP systems enter the picture. Data warehouses and other OLAP systems are built more and more because of this very need of or top executives. We don’t only need data but also the analytics associated with it to make better and more profitable decisions. OLTP and OLAP systems work in tandem. Our learners also read: Free excel courses! OLTP systems store all massive amounts of data that we generate on a daily basis. This data is then sent to OLAP systems for building data-based analytics. If you don’t already know, then let us tell you that data plays a very important role in the growth of a company. It can help in making knowledge-backed decisions that can take a company to the next level of growth. Data examination should never happen superficially. It doesn’t serve the purpose. We need to analyze data to enrich ourselves with the knowledge that will help us in making the right calls for the success of our business. All the data that we have been flooded with these days isn’t of any use if we aren’t learning anything from it. Data available to us is so huge that it is humanly impossible for us to process it and make sense of it. Data mining or knowledge discovery is what we need to solve this problem. Learn about other applications of data mining in real world. Data Mining Techniques 1. Association It is one of the most used data mining techniques out of all the others. In this technique, a transaction and the relationship between its items are used to identify a pattern. This is the reason this technique is also referred to as a relation technique. It is used to conduct market basket analysis, which is done to find out all those products that customers buy together on a regular basis. This technique is very helpful for retailers who can use it to study the buying habits of different customers. Retailers can study sales data of the past and then lookout for products that customers buy together. Then they can put those products in close proximity of each other in their retail stores to help customers save their time and to increase their sales.  The association rule provides two key details: How often is the support rule applied? How often is the Confidence rule correct? This data mining technique adopts a two-step process. Finds out all the repeatedly occurring data sets. Develop strong association rules from the recurrent data sets. Three types of association rules are: Multilevel Association Rule Quantitative Association Rule Multidimensional Association Rule 2. Clustering Another data mining methodology is clustering. This creates meaningful object clusters that share the same characteristics. People often confuse it with classification, but if they properly understand how both these data mining methodologies or techniques work, they won’t have any issue. Unlike classification that puts objects into predefined classes, clustering puts objects in classes that are defined by it. Let us take an example. A library is full of books on different topics. Now the challenge is to organize those books in a way that readers don’t have any problem in finding out books on a particular topic. We can use clustering to keep books with similarities in one shelf and then give those shelves a meaningful name. Readers looking for books on a particular topic can go straight to that shelf. They won’t be required to roam the entire library to find their book.  Clustering analysis identifies data that are identical to each other. It clarifies the similarities and differences between the data. It is known as segmentation and provides an understanding of the events taking place in the database. Different types of clustering methods are: Density-Based Methods Model-Based Methods Partitioning Methods Hierarchical Agglomerative methods Grid-Based Methods The most famous clustering algorithm is the Nearest Neighbor which is quite identical to clustering. Essentially, it is a prediction technique to predict an estimated value that records look for records with identical estimated values within a historical database. Consequently, it uses the prediction value from the form adjacent to the unclassified document. So, this data mining technique explains that the objects which are nearer to one another will share identical prediction values. 3. Classification This technique finds its origins in machine learning. It classifies items or variables in a data set into predefined groups or classes. It uses linear programming, statistics, decision trees, and artificial neural network in data mining, amongst other techniques. Classification is used to develop software that can be modelled in a way that it becomes capable of classifying items in a data set into different classes. For instance, we can use it to classify all the candidates who attended an interview into two groups – the first group is the list of those candidates who were selected and the second is the list that features candidates that were rejected. Data mining software can be used to perform this classification job.  4. Prediction Prediction is one of the other data mining methodologies. This technique predicts the relationship that exists between independent and dependent variables as well as independent variables alone. It can be used to predict future profit depending on the sale. Let us assume that profit and sale are dependent and independent variables, respectively. Now, based on what the past sales data says, we can make a profit prediction of the future using a regression curve.  5. Sequential patterns This technique aims to use transaction data, and then identify similar trends, patterns, and events in it over a period of time. The historical sales data can be used to discover items that buyers bought together at different times of the year. Business can make sense of this information by recommending customers to buy those products at times when the historical data doesn’t suggest they would. Businesses can use lucrative deals and discounts to push through this recommendation. 6. Statistical Techniques Statistics is one of the branches of mathematics that links to the data’s collection and description. Many analysts don’t consider it a data mining technique. However, it helps to identify the patterns and develop predictive models. Therefore, data analysts must have some knowledge about various statistical techniques. Currently, people have to handle several pieces of data and derive significant patterns from them. The statistical data mining techniques help them get answers to the following questions: What are the ways available in their database? What is the likelihood of an event occurring? Which patterns are more beneficial to the business? What is the high-level summary capable of providing you with an in-depth view of components existing in the database? Statistical techniques not only answer these questions but also help to summarize the data and calculate it. You can make smart decisions from the precise data mining definition conveyed through statistical reports. From diverse forms of statistics, the most useful technique is gathering and calculating data. Various ways to collect data are: Mean Median Mode Max Min Variance Histogram Linear Regression  7. Induction Decision Tree Technique Implied from the name, it appears like a tree and is a predictive model. In this data mining technique, every tree branch is observed as a classification question. The trees’ leaves are the partitions of the dataset associated with that specific classification. Moreover, this technique is used for data pre-processing, exploration analysis, and prediction analysis. So, it is one of the versatile data mining methods. The decision tree used in this technique is the original dataset’s segmentation. Every data falling under a segment shares certain similarities with the information already predicted. The decision trees offer easily understandable results. Two examples of the Induction Decision Tree Technique are CART (Classification and Regression Trees) and CHAID (Chi-Square Automatic Interaction Detector). 8. Visualization Visualization is used to determine data patterns. This data mining technique is used in the initial phase of the data mining process. It is one of those effective data mining methods that help to discover hidden patterns. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Data Mining Process After understanding the data mining definition, let’s understand the data mining process. Before the actual data mining could occur, there are several processes involved in data mining implementation. Here’s how: Step 1: Business Research – Before you begin, you need to have a complete understanding of your enterprise’s objectives, available resources, and current scenarios in alignment with its requirements. This would help create a detailed data mining plan that effectively reaches organizations’ goals. Step 2: Data Quality Checks – As the data gets collected from various sources, it needs to be checked and matched to ensure no bottlenecks in the data integration process. The quality assurance helps spot any underlying anomalies in the data, such as missing data interpolation, keeping the data in top-shape before it undergoes mining. Step 3: Data Cleaning – It is believed that 90% of the time gets taken in the selecting, cleaning, formatting, and anonymizing data before mining. Step 4: Data Transformation – Comprising five sub-stages, here, the processes involved make data ready into final data sets. It involves: Data Smoothing: Here, noise is removed from the data. Noisy data is information that has been corrupted in transit, storage, or manipulation to the point that it is unusable in data analysis. Aside from potentially skewing the outcomes of any data mining research, storing noisy data also raises the amount of space that must be allocated for the dataset. Data Summary: The aggregation of data sets is applied in this process. Data Generalization: Here, the data gets generalized by replacing any low-level data with higher-level conceptualizations. Data Normalization: Here, data is defined in set ranges. For data mining to work, normalization of the data is a must. It basically means changing the data from its original format into one more suitable for processing. The goal of data normalization is to reduce or eliminate redundant information. Data Attribute Construction: The data sets are required to be in the set of attributes before data mining. Step 5: Data Modelling: For better identification of data patterns, several mathematical models are implemented in the dataset, based on several conditions. Learn data science to understand and utilize the power of data mining. Our learners also read: Free Python Course with Certification Types of data that can be mined What kind of data can be mined? Let’s discuss about the types of data in data mining. 1. Data stored in the database A database is also called a database management system or DBMS. Every DBMS stores data that are related to each other in a way or the other. It also has a set of software programs that are used to manage data and provide easy access to it. These software programs serve a lot of purposes, including defining structure for database, making sure that the stored information remains secured and consistent, and managing different types of data access, such as shared, distributed, and concurrent. A relational database has tables that have different names, attributes, and can store rows or records of large data sets. Every record stored in a table has a unique key. Entity-relationship model is created to provide a representation of a relational database that features entities and the relationships that exist between them. 2. Data warehouse A data warehouse is a single data storage location that collects data from multiple sources and then stores it in the form of a unified plan. When data is stored in a data warehouse, it undergoes cleaning, integration, loading, and refreshing. Data stored in a data warehouse is organized in several parts. If you want information on data that was stored 6 or 12 months back, you will get it in the form of a summary. 3. Transactional data Transactional database stores record that are captured as transactions. These transactions include flight booking, customer purchase, click on a website, and others. Every transaction record has a unique ID. It also lists all those items that made it a transaction. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis 4. Other types of data We have a lot of other types of data as well that are known for their structure, semantic meanings, and versatility. They are used in a lot of applications. Here are a few of those data types in data mining: data streams, engineering design data, sequence data, graph data, spatial data, multimedia data, and more. Data Mining Applications Data mining methods are applied in a variety of sectors from healthcare to finance and banking. We have taken the epitome of the lot to bring into light the characteristics of data mining and its five applications.  Below are some most useful data mining applications lets know more about them. 1. Healthcare Data mining methods has the potential to transform the healthcare system completely. It can be used to identify best practices based on data and analytics, which can help healthcare facilities to reduce costs and improve patient outcomes. Data mining, along with machine learning, statistics, data visualization, and other techniques can be used to make a difference. It can come in handy when forecasting patients of different categories. This will help patients to receive intensive care when and where they want it. Data mining can also help healthcare insurers to identify fraudulent activities. 2. Education Use of data mining methods in education is still in its nascent phase. It aims to develop techniques that can use data coming out of education environments for knowledge exploration. The purposes that these techniques are expected to serve include studying how educational support impacts students, supporting the future-leaning needs of students, and promoting the science of learning amongst others. Educational institutions can use these techniques to not only predict how students are going to do in examinations but also make accurate decisions. With this knowledge, these institutions can focus more on their teaching pedagogy.  3. Market basket analysis This is a modelling technique that uses hypothesis as a basis. The hypothesis says that if you purchase certain products, then it is highly likely that you will also purchase products that don’t belong to that group that you usually purchase from. Retailers can use this technique to understand the buying habits of their customers. Retailers can use this information to make changes in the layout of their store and to make shopping a lot easier and less time consuming for customers.  Apart from the ones where characteristics of data mining and its five applications in major fields are mentioned above. Other fields and methodologies also benefit from data mining methods, we have listed them below as well: 4. Customer relationship management (CRM) CRM involves acquiring and keeping customers, improving loyalty, and employing customer-centric strategies. Every business needs customer data to analyze it and use the findings in a way that they can build a long-lasting relationship with their customers. Data mining can help them do that.  Applications of data mining in CRM include: Sales Forecasting: Businesses may better plan restocking needs by analyzing trends over time with the use of data mining techniques. It also aids in financial management, and supply chain management, and offers you full command over your own internal processes. Market Segmentation: Keep their preferences in mind when creating ads and other marketing materials. With the use of data mining techniques, it is possible to recognize which segment of the market provides the best return on investment. With that information, one won’t waste time or resources pursuing leads who aren’t interested in purchasing a particular product.  Identifying the loyalty of customers: In order to improve brand service, customer satisfaction, and customer loyalty, data mining employs a concept known as “customer cluster,” which draws upon information shared by social media audiences. 5. Manufacturing engineering A manufacturing company relies a lot on the data or information available to it. Data mining can help these companies in identifying patterns in processes that are too complex for a human mind to understand. They can identify the relationships that exist between different system-level designing elements, including customer data needs, architecture, and portfolio of products. Data mining can also prove useful in forecasting the overall time required for product development, the cost involved in the process, and the expectations companies can have from the final product.  The data can be evaluated by guaranteeing that the manufacturing firm owns enough knowledge of certain parameters. These parameters are recognizing the product architecture, the correct set of product portfolios, and the customer requirements. The efficient data mining capabilities in manufacturing and engineering guarantee that the product development completes in the stipulated time frame and does not surpass the budget allocated initially. 6. Finance and banking The banking system has been witnessing the generation of massive amounts of data from the time it underwent digitalization. Bankers can use data mining techniques to solve the baking and financial problems that businesses face by finding out correlations and trends in market costs and business information. This job is too difficult without data mining as the volume of data that they are dealing with is too large. Managers in the banking and financial sectors can use this information to acquire, retain, and maintain a customer.  The analysis turns easy and quick by sampling and recognizing a large set of customer data. Tracking mistrustful activities become straightforward by analyzing the parameters like transaction period, mode of payments, geographical locations, customer activity history, and more. The customer’s relative measure is calculated based on these parameters. Consequently, it can be used in any form depending on the calculated indices. So, finance and banking are one of valuable data mining techniques. Learn more: Association Rule Mining 7. Fraud detection Fraudulent activities cost businesses billions of dollars every year. Methods that are usually used for detecting frauds are too complex and time-consuming. Data mining provides a simple alternative. Every ideal fraud detection system needs to protect user data in all circumstances. A method is supervised to collect data, and then this data is categorized into fraudulent or non-fraudulent data. This data is used in training a model that identifies every document as fraudulent or non-fraudulent. 8. Monitoring Patterns Known as one of the fundamental data mining techniques, it generally comprises tracking data patterns to derive business conclusions. For an organization, it could mean anything from identifying sales upsurge or tapping newer demographics. 9. Classification To derive relevant metadata, the classification technique in data mining helps in differentiating data into separate classes: Based on the type of data sources, mined Depending on the type of data handled like text-based data, multimedia data, spatial data, time-series data, etc. Based on the data framework involved Any data set that is based on the object-oriented database, relational database, etc. Based on data mining functionalities Here the data sets are differentiated based on the approach taken like Machine Learning, Algorithms, Statistics, Database or data warehouse, etc. Based on user interaction in data mining The datasets are used to differentiate based on query-driven systems, autonomous systems.  10. Association Otherwise known as relation technique, the data is identified based on the relationship between the values in the same transaction. It is especially handy for organizations trying to spot trends into purchases or product preferences. Since it is related to customers’ shopping behavior, an organization can break down data patterns based on the buyers’ purchase histories. 11. Anomaly Detection If a data item is identified that does not match up to a precedent behavior, it is an outlier or an exception. This method digs deep into the process of the creation of such exceptions and backs it with critical information.  Generally, anomalies can be aloof in its origin, but it also comes with the possibility of finding out a focus area. Therefore, businesses often use this method to trace system intrusion, error detection, and keeping a check on the system’s overall health. Experts prefer the emission of anomalies from the data sets to increase the chances of correctness. 12. Clustering Just as it sounds, this technique involves collating identical data objects into the same clusters. Based on the dissimilarities, the groups often consist of using metrics to facilitate maximum data association. Such processes can be helpful to profile customers based on their income, shopping frequency, etc.  Check out: Difference between Data Science and Data Mining 13. Regression A data mining process that helps in predicting customer behavior and yield, it is used by enterprises to understand the correlation and independence of variables in an environment. For product development, such analysis can help understand the influence of factors like market demands, competition, etc.  14. Prediction As implied in its name, this compelling data mining technique helps enterprises to match patterns based on current and historical data records for predictive analysis of the future. While some of the approaches involve Artificial Intelligence and Machine Learning aspects, some can be conducted via simple algorithms.   Organizations can often predict profits, derive regression values, and more with such data mining techniques. 15. Sequential Patterns It is used to identify striking patterns, trends in the transaction data available in the given time. For discovering items that customers prefer to buy at different times of the year, businesses offer deals on such products.  Read: Data Mining Project Ideas 16. Decision Trees One of the most commonly used data mining techniques; here, a simple condition  is the crux of the method. Since such terms have multiple answers, each of the solutions further branches out into more states until the conclusion is reached. Learn more about decision trees. 17. Visualization No data is useful without visualizing the right way since it’s always changing. The different colors and objects can reveal valuable trends, patterns, and insights into the vast datasets. Therefore, businesses often turn to data visualization dashboards that automate the process of generating numerical models. 18. Neural Networks It represents the connection of a particular machine learning model to an AI-based learning technique. Since it is inspired by the neural multi-layer system found in human anatomy, it represents the working of machine learning models in precision. It can be increasingly complex and therefore needs to be dealt with extreme care. 19. Data Warehousing While it means data storage, it symbolizes the storing of data in the form of cloud warehouses. Companies often use such a precise data mining method to have more in-depth real-time data analysis. Read more about data warehousing. 20. Transportation The batch or historic form data helps recognize the mode of transport a specific customer usually chooses to a specific place. It accordingly offers them attractive offers and discounts on newly launched products and services. Therefore, it will be included in the organic and targeted advertisements wherein the customer’s potential leader produces the right to transform the lead. Moreover, it helps in deciding the distribution of the schedules across different outlets and warehouses for analyzing load-focused patterns. The transportation sector uses advanced mining methods in data mining. Importance of Data Mining Data mining is the process that helps in extracting information from a given data set to identify trends, patterns, and useful data. The objective of using data mining is to make data-supported decisions from enormous data sets. Data mining works in conjunction with predictive analysis, a branch of statistical science that uses complex algorithms designed to work with a special group of problems. The predictive analysis first identifies patterns in huge amounts of data, which data mining generalizes for predictions and forecasts. Data mining serves a unique purpose, which is to recognize patterns in datasets for a set of problems that belong to a specific domain. It does this by using a sophisticated algorithm to train a model for a specific problem. When you know the domain of the problem you are dealing with, you can even use machine learning to model a system that is capable of identifying patterns in a data set. When you put machine learning to work, you will be automating the problem-solving system as a whole, and you wouldn’t need to come up with special programming to solve every problem that you come across. Must read: Data structures and algorithms free course! We can also define data mining as a technique of investigation patterns of data that belong to particular perspectives. This helps us in categorizing that data into useful information. This useful information is then accumulated and assembled to either be stored in database servers, like data warehouses, or used in data mining algorithms and analysis to help in decision making. Moreover, it can be used for revenue generation and cost-cutting amongst other purposes. Data mining is the process of searching large sets of data to look out for patterns and trends that can’t be found using simple analysis techniques. It makes use of complex mathematical algorithms to study data and then evaluate the possibility of events happening in the future based on the findings. It is also referred to as knowledge discovery of data or KDD. Data mining is used by businesses to draw out specific information from large volumes of data to find solutions to their business problems. It has the capability of transforming raw data into information that can help businesses grow by taking better decisions. Data mining has several types, including pictorial data mining, text mining, social media mining, web mining, and audio and video mining amongst others. Read: Data Mining vs Machine Learning upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Data Mining Tools All that AI and Machine learning inference must have got you into wondering that for data mining implementation, you’d require nothing less. That might not entirely be true, as, with the help of most straightforward databases, you can get the job done with equal accuracy.  Let us talk about a few data mining methodology and tools that are currently being used in the industry: RapidMiner: RapidMiner is an open-source platform for data science that is available for no cost and includes several algorithms for tasks such as data preprocessing ML/DL, text mining, and predictive analytics. For use cases like fraud detection and customer attrition, RapidMiner’s easy GUI(graphical user interface)and pre-built models make it easy for non-programmers to construct predictive processes. Meanwhile, RapidMiner’s R and Python add-ons allow developers to fine-tune data mining to their specific needs. Oracle Data Mining: Predictive models may be developed and implemented with the help of Oracle Data Mining, which is a part of Oracle Advanced Analytics. Models built using Oracle Data Mining may be used to do things like anticipating customer behaviour, dividing up customer profiles into subsets, spot fraud, and zeroing in on the best leads. These models are available as a Java API for integration into business intelligence tools, where they might aid in the identification of previously unnoticed patterns and trends. Apache Mahout: It is a free and open-source machine-learning framework. Its purpose is to facilitate the use of custom algorithms by data scientists and researchers. This framework is built on top of Apache Hadoop and is written in JavaScript. Its primary functions are in the fields of clustering and classification. Large-scale, sophisticated data mining projects that deal with plenty of information work well with the Apache Mahout. KNIME: KNIME (Konstanz Information Miner) is an (open-source) data analysis platform that allows you to quickly develop, deploy, and scale. This tool makes predictive intelligence accessible to beginners. It simplifies the process through its GUI tool, which includes a step-by-step guide. The product is endorsed as an ‘End to End Data Science’ product. ORANGE: You must know what is data mining before you use tools like ORANGE. It is a data mining techniques in machine learning tool. It uses visual programming and Python scripting that features engaging data analysis and component-focused assembly of data mining mechanisms. Moreover, ORANGE is one of the versatile mining methods in data mining because it provides a wider range of features than many other Python-focused machine learning and data mining tools. Moreover, it presents a visual programming platform with a GUI tool for engaging data visualization. Also, read about the most useful data mining applications. Conclusion Data mining techniques brings together different methods from a variety of disciplines, including data visualization, machine learning, database management, statistics, and others. These techniques can be made to work together to tackle complex problems. Generally, data mining software or systems make use of one or more of these methods to deal with different data requirements, types of data, application areas, and mining tasks.  If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

by Rohit Sharma

12 Jul 2024

Blogs

58114

Pandas is a BSD-licensed and open-source Python library offering high-performance, easy-to-use data structures, and data analysis tools. The full form of “pandas” is Python Data Analysis Library. Pandas is used for data manipulation and analysis, providing powerful data structures like DataFrame and Series for handling structured data efficiently. In this article, we have listed some essential pandas interview questions and NumPy interview questions and answers that a python learner must know. If you want to learn more about python, check out our data science programs. What are the Different Job Titles That Encounter Pandas and Numpy Interview Questions? Here are some common job titles that often encounter pandas in python interview questions. 1. Data Analyst Data analysts often use Pandas to clean, preprocess, and analyze data for insights. They may be asked about their proficiency in using Pandas for data wrangling, summarization, and visualization. 2. Data Scientist Data scientists use Pandas extensively for preprocessing and exploratory data analysis (EDA). During interviews, they may face questions related to Pandas for data manipulation and feature engineering. 3. Machine Learning Engineer When building machine learning models, machine learning engineers leverage Pandas for data preparation and feature extraction. They may be asked Pandas-related questions in the context of model development. 4. Quantitative Analyst (Quant) Quants use Pandas for financial data analysis, modeling, and strategy development. They may be questioned on their Pandas skills as part of the interview process. 5. Business Analyst Business analysts use Pandas to extract meaningful insights from data to support decision-making. They may encounter Pandas interview questions related to data cleaning and visualization. 6. Data Engineer Data engineers often work on data pipelines and ETL processes where Pandas can be used for data transformation tasks. They may be quizzed on their knowledge of Pandas in data engineering scenarios. 7. Research Analyst Research analysts across various domains, such as market research or social sciences, might use Pandas for data analysis. They may be assessed on their ability to manipulate data using Pandas. 8. Financial Analyst Financial analysts use Pandas for financial data analysis and modeling. Interview questions might focus on using Pandas to calculate financial metrics and perform time series analysis. 9. Operations Analyst Operations analysts may use Pandas to analyze operational data and optimize processes. Questions might revolve around using Pandas for efficiency improvements. 10. Data Consultant Data consultants work with diverse clients and datasets. They may be asked Pandas questions to gauge their adaptability and problem-solving skills in various data contexts. What is the Importance of Pandas in Data Science? Pandas is a crucial library in data science, offering a powerful and flexible toolkit for data manipulation and analysis. So, let’s explore Panda in detail: – 1. Data Handling Pandas provides essential data structures, primarily the Data Frame and Series, which are highly efficient for handling and managing structured data. These structures make it easy to import, clean, and transform data, often the initial step in any data science project. 2. Data Cleaning Data in the real world is messy and inconsistent. Pandas simplifies the process of cleaning and preprocessing data by offering functions for handling missing values, outliers, duplicates, and other data quality issues. This ensures that the data used for analysis is accurate and reliable. 3. Data Exploration Pandas facilitate exploratory data analysis (EDA) by offering a wide range of tools for summarizing and visualizing data. Data scientists can quickly generate descriptive statistics, histograms, scatter plots, and more to gain insights into the dataset’s characteristics. 4. Data Transformation Data often needs to be transformed to make it suitable for modeling or analysis. Pandas support various operations, such as merging, reshaping, and pivoting data, essential for feature engineering and preparing data for machine learning algorithms. 5. Time Series Analysis Pandas are particularly useful for working with time series data, a common data type in various domains, including finance, economics, and IoT. It offers specialized functions for resampling, shifting time series, and handling date/time information. 6. Data Integration It’s common to work with data from multiple sources in data science projects. Pandas enable data integration by allowing easy merging and joining of datasets, even with different structures or formats. Pandas Interview Questions & Answers Question 1 – Define Python Pandas. Pandas refer to a software library explicitly written for Python, which is used to analyze and manipulate data. Pandas is an open-source, cross-platform library created by Wes McKinney. It was released in 2008 and provided data structures and operations to manipulate numerical and time-series data. Pandas can be installed using pip or Anaconda distribution. Pandas make it very easy to perform machine learning operations on tabular data. Question 2 – What Are The Different Types Of Data Structures In Pandas? Panda library supports two major types of data structures, DataFrames and Series. Both these data structures are built on the top of NumPy. Series is a one dimensional and simplest data structure, while DataFrame is two dimensional. Another axis label known as the “Panel” is a 3-dimensional data structure and includes items such as major_axis and minor_axis. Source Question 3 – Explain Series In Pandas. Series is a one-dimensional array that can hold data values of any type (string, float, integer, python objects, etc.). It is the simplest type of data structure in Pandas; here, the data’s axis labels are called the index. Question 4 – Define Dataframe In Pandas. A DataFrame is a 2-dimensional array in which data is aligned in a tabular form with rows and columns. With this structure, you can perform an arithmetic operation on rows and columns. Our learners also read: Free online python course for beginners! Question 5 – How Can You Create An Empty Dataframe In Pandas? To create an empty DataFrame in Pandas, type import pandas as pd ab = pd.DataFrame() Also read: Free data structures and algorithm course! Question 6 – What Are The Most Important Features Of The Pandas Library? Important features of the panda’s library are: Data Alignment Merge and join Memory Efficient Time series Reshaping Read: Dataframe in Apache PySpark: Comprehensive Tutorial Question 7 – How Will You Explain Reindexing In Pandas? To reindex means to modify the data to match a particular set of labels along a particular axis. Various operations can be achieved using indexing, such as- Insert missing value (NA) markers in label locations where no data for the label existed. Reorder the existing set of data to match a new set of labels. upGrad’s Exclusive Data Science Webinar for you – How to Build Digital & Data Mindset document.createElement('video'); https://cdn.upgrad.com/blog/webinar-on-building-digital-and-data-mindset.mp4 Question 8 – What are the different ways of creating DataFrame in pandas? Explain with examples. DataFrame can be created using Lists or Dict of nd arrays. Example 1 – Creating a DataFrame using List import pandas as pd     # a list of strings     Strlist = [‘Pandas’, ‘NumPy’]     # Calling DataFrame constructor on the list     list = pd.DataFrame(Strlist)     print(list)    Must read: Learn excel online free! Example 2 – Creating a DataFrame using dict of arrays import pandas as pd     list = {‘ID’: [1001, 1002, 1003],’Department’:[‘Science’, ‘Commerce’, ‘Arts’,]}     list = pd.DataFrame(list)     print (list)    Check out: Data Science Interview Questions Question 9 – Explain Categorical Data In Pandas? Categorical data refers to real-time data that can be repetitive; for instance, data values under categories such as country, gender, codes will always be repetitive. Categorical values in pandas can also take only a limited and fixed number of possible values.  Numerical operations cannot be performed on such data. All values of categorical data in pandas are either in categories or np.nan. This data type can be useful in the following cases: If a string variable contains only a few different values, converting it into a categorical variable can save some memory. It is useful as a signal to other Python libraries because this column must be treated as a categorical variable. A lexical order can be converted to a categorical order to be sorted correctly, like a logical order. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Question 10 – Create A Series Using Dict In Pandas. import pandas as pd     import numpy as np     ser = {‘a’ : 1, ‘b’ : 2, ‘c’ : 3}     ans = pd.Series(ser)     print (ans)    Question 11 – How To Create A Copy Of The Series In Pandas? To create a copy of the series in pandas, the following syntax is used: pandas.Series.copy Series.copy(deep=True) * if the value of deep is set to false, it will neither copy data nor the indices. Question 12 – How Will You Add An Index, Row, Or Column To A Dataframe In Pandas? To add rows to a DataFrame, we can use .loc (), .iloc () and .ix(). The .loc () is label based, .iloc() is integer based and .ix() is booth label and integer based. To add columns to the DataFrame, we can again use .loc () or .iloc (). Question 13 – What Method Will You Use To Rename The Index Or Columns Of Pandas Dataframe? .rename method can be used to rename columns or index values of DataFrame Question 14 – How Can You Iterate Over Dataframe In Pandas? To iterate over DataFrame in pandas for loop can be used in combination with an iterrows () call. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Question 15 – What Is Pandas Numpy Array? Numerical Python (NumPy) is defined as an inbuilt package in python to perform numerical computations and processing of multidimensional and single-dimensional array elements.  NumPy array calculates faster as compared to other Python arrays. Question 16 – How Can A Dataframe Be Converted To An Excel File? To convert a single object to an excel file, we can simply specify the target file’s name. However, to convert multiple sheets, we need to create an ExcelWriter object along with the target filename and specify the sheet we wish to export. Question 17 – What Is Groupby Function In Pandas? In Pandas, groupby () function allows the programmers to rearrange data by using them on real-world sets. The primary task of the function is to split the data into various groups. Also Read: Top 15 Python AI & Machine Learning Open Source Projects DataFrame Vs. Series: Their distinguishing features In Pandas, DataFrame and Series are two fundamental data structures that play an important role in data analysis and manipulation. Here’s a concise overview of the key differences between DataFrame and series: Feature DataFrame Series Structure Two-dimensional tabular structure One-dimensional labeled array Data Type Heterogeneous – Columns can have different data types Homogeneous – All elements must be of the same data type Size Mutability Size Mutable – Can add or drop columns and rows after creation Size Immutable – Once created, size cannot be changed Creation Created using dictionaries of Pandas Series, dictionaries of lists or ndarrays, lists of dictionaries, or another DataFrame Created using dictionaries, ndarrays, or scalar values, it serves as the basic building block for a DataFrame. Dimensionality Two-dimensional One-dimensional Data Type Flexibility Allows columns with different data types Requires homogeneity Size Flexibility Can be changed after creation Cannot be changed after creation Use Case Suitable for tabular data with multiple variables, resembling a database table Suitable for representing a single variable or a row/column in a DataFrame Creation Flexibility Versatile creation from various data structures, including series Building block for a DataFrame, created using dictionaries, ndarrays, or scalar values Understanding the distinction between DataFrame and Series is essential for efficiently working with Pandas, especially in scenarios involving data cleaning, analysis, and transformation.  However, while DataFrame provides a comprehensive structure for handling diverse datasets, series offers a more focused, one-dimensional approach for individual variables or observations.  Thus, we can say that both play integral roles in the toolkit of data scientists and analysts using Pandas for Python-based data manipulation. Handling missing data in Panda It is a crucial aspect of data analysis, as datasets often contain incomplete or undefined values. In Pandas, a famous Python library for data manipulation and analysis, various methods and tools are available to manage missing data effectively. Here is a detailed guide on how you can handle missing data in pandas: 1. Identifying Missing Data Before addressing missing data, it’s crucial to identify its presence in the dataset. Missing values are conventionally represented as NaN (Not a Number) in pandas. By using functions like isnull() and sum(), you can systematically locate and quantify these missing values within your dataset. 2. Dropping Missing Values A simplistic yet effective strategy involves the removal of rows or columns containing missing values. The dropna() method enables this, but caution is necessary as it might impact the dataset’s size and integrity. 3. Filling Missing Values Instead of discarding data, another approach is to fill in missing values. The fillna() method facilitates this process, allowing you to replace missing values with a constant or values derived from the existing dataset, such as the mean. 4. Interpolation Interpolation proves useful for datasets with a time series or sequential structure. The interpolate() method estimates missing values based on existing data points, providing a coherent approach to filling gaps in the dataset. 5. Replacing Generic Values The replace() method offers flexibility in replacing specific values, including missing ones, with designated alternatives. This allows for a controlled substitution of missing data tailored to the requirements of the analysis. 6. Limiting Interpolation: Fine-tuning the interpolation process is possible by setting constraints on consecutive NaN values. The limit and limit_direction parameters in the interpolate() method empower you to control the extent of filling, limiting the number of consecutive NaN values introduced since the last valid observation. These are some of the topics, which one might get pandas interview questions for experienced. 7. Using Nullable Integer Data Type: For integer columns, pandas provide a special type called “Int64″ (dtype=”Int64”), allowing the representation of missing values in these columns. This nullable integer data type is particularly useful when dealing with datasets containing integer values with potential missing entries. 8. Experimental NA Scalar: Pandas introduces an experimental scalar, pd.NA is designed to signify missing values consistently across various data types. While still in the experimental stage, pd.NA offers a unified representation for scalar missing values, aiding in standardized handling. 9. Propagation in Arithmetic and Comparison Operations: In arithmetic operations involving pd.NA, the missing values propagate similarly to NumPy’s NaN. Logical operations adhere to three-valued logic (Kleene logic), where the outcome depends on the logical context and the values involved. Understanding the nuanced behavior of pd.NA in different operations is crucial for accurate analysis. 10. Conversion: After identifying and handling missing data, converting data to newer dtypes is facilitated by the convert_dtypes() method. This is particularly valuable when transitioning from traditional types with NaN representations to more advanced integers, strings, and boolean types. This step ensures data consistency and enhances compatibility with the latest features offered by pandas. Handling missing data is a detailed task that depends on the nature of your data and the goals of your analysis. Moreover, the choice of method should be driven by a clear understanding of the data and the potential impact of handling missing values on your results. Frequently Asked Python Pandas Interview Questions For Experienced Candidates Till now, we have looked at some of the basic pandas questions that you can expect in an interview. If you are looking for some more advanced pandas interview questions for the experienced, then refer to the list below. Seek reference from these questions and curate your own pandas interview questions and answers pdf. 1. What do we mean by data aggregation? One of the most popular numpy and pandas interview questions that are frequently asked in interviews is this one. The main goal of data aggregation is to add some aggregation in one or more columns. It does so by using the following Sum- It is specifically used when you want to return the sum of values for the requested axis. Min-This is used to return the minimum values for the requested axis. Max- Contrary to min, Max is used to return a maximum value for the requested axis.  2. What do we mean by Pandas index?  Yet another frequently asked pandas interview bit python question is what do we mean by pandas index. Well, you can answer the same in the following manner. Pandas index basically refers to the technique of selecting particular rows and columns of data from a data frame. Also known as subset selection, you can either select all the rows and some of the columns, or some rows and all of the columns. It also allows you to select only some of the rows and columns. There are mainly four types of multi-axes indexing, supported by Pandas. They are  Dataframe.[ ] Dataframe.loc[ ] Dataframe.iloc[ ] Dataframe.ix[ ] 3. What do we mean by Multiple Indexing? Multiple indexing is often referred to as essential indexing since it allows you to deal with data analysis and analysis, especially when you are working with high-dimensional data. Furthermore, with the help of this, you can also store and manipulate data with an arbitrary number of dimensions.  These are some of the most common python pandas interview questions that you can expect in an interview. Therefore, it is important that you clear all your doubts regarding the same for a successful interview experience. Incorporate these questions in your pandas interview questions and answers pdf to get started on your interview preparation! Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis 4. What is “mean data” in the Panda series?  The mean, in the context of a Pandas series, serves as a crucial statistical metric that provides insights into the central tendency of the data. It is a measure of average that aims to represent a typical or central value within the series. The computation of the mean involves a two-step process that ensures a representative value for the entire dataset. Firstly, all the numerical values in the Pandas series are summed up. This summation aggregates the individual data points, preparing for the next step. Subsequently, the total sum is divided by the count of values in the series. This division accounts for the varying dataset sizes and ensures that the mean is normalized with respect to the total number of observations To perform this computation in Pandas, the mean() method is employed. This method abstracts away the intricate arithmetic operations, providing a convenient and efficient means of get the average. By executing mean() on a Pandas series, you gain valuable information about the central tendency of the data, aiding in the interpretation and analysis of the dataset. 5. How can data be obtained in a Pandas DataFrame using the Pandas DataFrame get() method? Acquiring data in a Pandas DataFrame is a fundamental step in working with tabular data in Python. The Pandas library provides various methods for this purpose, and one such method is the `get()` method. Moreover, the `get()` method in Pandas DataFrame is designed to retrieve specified column(s) from the DataFrame. Its functionality accommodates single and multiple-column retrievals, offering flexibility in data extraction. When you utilize the `get()` method to fetch a single column, the return type is a Pandas Series object. A Series is a one-dimensional labeled array, effectively representing a single column of data. This is particularly useful when you need to analyze or manipulate data within a specific column especially when you solve pandas mcq questions. Should you require multiple columns, you can specify them inside an array. This approach results in the creation of a new DataFrame object containing the selected columns. A DataFrame is a two-dimensional, tabular data structure with labeled axes (rows and columns), making it suitable for various analytical and data manipulation tasks. The `get()` method in Pandas DataFrame is a versatile tool for extracting specific columns, allowing for seamless navigation and manipulation of tabular data based on your analytical requirements. 6. What are lists in Python? In Python, a list is a versatile and fundamental data structure used for storing and organizing multiple items within a single variable. Lists are part of the four built-in data types in Python, which also include Tuple, Set, and Dictionary. Unlike other data types, lists allow for the sequential arrangement of elements and are mutable, meaning their contents can be modified after creation. Lists in Python or python pandas interview questions are defined by enclosing a comma-separated sequence of elements within square brackets. These elements are of any data type like numbers, strings, or other lists. The ability to store heterogeneous data types within a single list makes it a flexible and powerful tool for managing collections of related information. Furthermore, lists provide various methods and operations for manipulating and accessing their elements. Elements within a list are indexed, starting from zero for the first element, allowing for easy retrieval and modification. Additionally, lists support functions like appending, extending, and removing elements, making them dynamic and adaptable to changing data requirements. Thus, we can say that a list in Python is a mutable data structure that allows storing multiple items in a single variable. Its flexibility, coupled with a range of built-in methods, makes lists a fundamental tool for handling collections of data in Python programming, to solve pandas practice questions. Conclusion We hope the above-mentioned Pandas interview questions and NumPy interview questions will help you prepare for your upcoming interview sessions. If you are looking for courses that can help you get a hold of Python language, upGrad can be the best platform. Additionally, Pandas Interview Questions for Freshers and experienced professionals are available to aid in your preparation. If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.

by Rohit Sharma

11 Jul 2024

Blogs

99373

Data types are an essential concept in the python programming language. In Python, every value has its own python data type. The classification of data items or to put the data value into some sort of data category is called Data Types. It helps to understand what kind of operations can be performed on a value. If you are a beginner and interested to learn more about data science, check out our data science certification from top universities. In the Python Programming Language, everything is an object. Data types in Python represents the classes. The objects or instances of these classes are called variables. How many data types in Python? Let us now discuss the different kinds of data types in Python. Built-in Data Types in Python Binary Types: memoryview, bytearray, bytes Boolean Type: bool Set Types: frozenset, set Mapping Type: dict Sequence Types: range, tuple, list Numeric Types: complex, float, int Text Type: str If you are using Python, check data type using the syntax type (variable). Get a detailed insight into what are the common built-in data types in Python and associated terms with this blog.   Our learners also read – free online python course for beginners! 1. Python Numbers We can find complex numbers, floating point numbers and integers in the category of Python Numbers. Complex numbers are defined as a complex class, floating point numbers are defined as float and integers are defined as an int in Python. There is one more type of datatype in this category, and that is long. It is used to hold longer integers. One will find this datatype only in Python 2.x which was later removed in Python 3.x.  “Type()” function is used to know the class of a value or variable. To check the value for a particular class, “isinstance()” function is used.  Must read: Data structures and algorithms free course! Integers: There is no maximum limit on the value of an integer. The integer can be of any length without any limitation which can go up to the maximum available memory of the system.  Integers can look like this: >>> print(123123123123123123123123123123123123123123123123123 + 1) 123123123123123123123123123123123123123123123123124 Floating Point Number: The difference between floating points and integers is decimal points. Floating point number can be represented as “1.0”, and integer can be represented as “1”. It is accurate up to 15 decimal places. Complex Number: “x + yj” is the written form of the complex number. Here y is the imaginary part and x is the real part. 2. Python List An ordered sequence of items is called List. It is a very flexible data type in Python. There is no need for the value in the list to be of the same data type. The List is the data type that is highly used data type in Python. List datatype is the most exclusive datatype in Python for containing versatile data. It can easily hold different types of data in Python.   Lists are among the most common built-in data types in Python. Like arrays, they are also collections of data arranged in order. The flexibility associated with this type of data is remarkable.  It is effortless to declare a list. The list is enclosed with brackets and commas are used to separate the items.  A list can look like this: >>> a = [5,9.9,’list’] One can also alter the value of an element in the list. Complexities in declaring lists: Space complexity: O(n) Time complexity: O(1) How to Access Elements in a Python List Programmers refer to the index number and use the index operator [ ] to access the list items. In Python, negative sequence indexes represent the positions placed at the end of the array.  Therefore, negative indexing means starting from the items at the end, where -1 means the last item, -2 means the second last item, and so on.  How to Add Elements to a Python List There are three methods of adding elements to a Python list: Method 1: Adding an element using the append() method  Using the append() method, you can add elements in this Python data type. This is ideally suited when adding only one element at a time. Loops are used to add multiple elements using this method. Both the time and space complexity for adding elements in a list using the append() method is O(1).  Method 2: Adding an element using the insert() method  Unlike the append() method, the insert() method takes two arguments: the position and the value. In this case, the time complexity is O(n), and space complexity is O(1).  Method 3: Adding an element using extend() method Alongside the append() and insert() methods, there is one more method used to add elements to a Python list known as the extend() method. The extend() method helps add multiple elements at the end of the list simultaneously. Here, the time complexity is O(n), and the space complexity is O(1).  Eager to put your Python skills to the test or build something amazing? Dive into our collection of Python project ideas to inspire your next coding adventure. How to Remove Elements from a Python List Removing elements from a Python list can be done using two methods: Method 1: Removing elements using the remove() method This built-in function can be used to remove elements from a Python list. Only one element can be removed at a time using this function.  If the element whose removal has been requested does not exist in the list, an error message pops up. Removing elements using the remove() method takes a time complexity of O(n) and a space complexity of O(1).  Method 2: Removing elements using pop() method  The pop() function can also help eliminate and return an element from this Python data type. However, by default, the function only removes the last element of the list.  If you want to remove any element from any specific position, provide the index of the element to be removed in the argument of the pop() function.  In this functionality, the time complexity for removing the last element is O(1)/O(n) O(1), and that for removing the first and middle elements is O(n). The space complexity in this case is O(1).  Also, Check out all Trending Python Tutorial Concepts in 2024. 3. Python Tuple A Tuple is a sequence of items that are in order, and it is not possible to modify the Tuples. The main difference list and tuples are that tuple is immutable, which means it cannot be altered. Tuples are generally faster than the list data type in Python because it cannot be changed or modified like list datatype. The primary use of Tuples is to write-protect data. Tuples can be represented by using parentheses (), and commas are used to separate the items.  Tuples can look like this: >>> t = (6,’tuple’,4+2r) In the case of a tuple, one can use the slicing operator to extract the item, but it will not allow changing the value.  Data Frames in Python Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses You can create tuples in Python by placing the values sequentially separated by commas. The use of parentheses is entirely optional. If a tuple is created without using parentheses, it is known as Tuple Packing.  Tuple in Python can contain various data types like integers, lists, strings, etc. The time complexity for creating tuples is O(1), and the auxiliary space is O(n).  How to Access the Elements in Tuples  Tuples are one of the built-in types in Python that contain a variety of heterogeneous elements that can be accessed by unpacking or indexing. In the case of named tuples, elements can be accessed by attribute.  In this case, the time complexity is O(1), and space complexity is O(1).  Concatenation of Tuples  This is the process of joining multiple tuples together. This function is performed using the “+” operator. Concatenation takes a time complexity of O(1) and auxiliary space of O(1). How to Delete Tuples Since tuples are immutable, you cannot delete a part of a tuple in Python. Using the del() method, you can delete the entire tuple. upGrad’s Exclusive Data Science Webinar for you – document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 4. Python Strings Strings are among the other common built-in data types in Python. A String is a sequence of Unicode characters. In Python, String is called str. Strings are represented by using Double quotes or single quotes. If the strings are multiple, then it can be denoted by the use of triple quotes “”” or ”’. All the characters between the quotes are items of the string. One can put as many as the character they want with the only limitation being the memory resources of the machine system. Deletion or Updation of a string is not allowed in python programming language because it will cause an error. Thus, the modification of strings is not supported in the python programming language. A string can look like this: >>> s = “Python String” >>> s = ”’a multi-string Strings are also immutable like tuples and items can be extracted using slicing operators []. If one wants to represent something in the string using quotes, then they will need to use other types of quotes to define the string in the beginning and the ending. Such as:  >>> print(“This string contains a single quote (‘) character.”) This string contains a single quote (‘) character. >>> print(‘This string contains a double quote (“) character.’) This string contains a double quote (“) character. Our learners also read: Excel online course free! How to Create Strings You can create strings in Python using single, double, or even triple quotes.  How to Access Characters in a String in Python  If you want to access an individual character from a string in Python, you can use the indexing method. In indexing, use negative indexes to refer to the characters at the end of the string. For instance, -1 refers to the last character of the string, -2 refers to the second last character, and so on.  How to Slice a String In Python, slicing a string means accessing a range of elements present in the string. This is done with the help of the slicing operator, which is a colon(:).  5. Python Set The Collection of Unique items that are not in order is called Set. Braces {} are used to defined set and a comma is used to separate values. One will find that the items are unordered in a set data type. Duplicates are eliminated in a set and set only keeps unique values. Operations like intersection and union can be performed on two sets.  Python set will look like this: >>> a = {4,5,5,6,6,6} >>> a  {4, 5, 6} The slicing operator does not work on set because the set is not a collection of ordered items, and that is why there is no meaning to the indexing of set. Python Developer Tools Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? How to Create a Set  To create this Python data type, use the built-in set() function along with an iterable object or a sequence, in which the sequence is to be placed inside curly brackets {} and distinguished with the help of commas.  The time complexity for creating a set is O(n), n being the length of the dictionary, tuple, string, or list. The auxiliary space is O(n).  How to Add Elements to a Set  Adding elements to a set can be done using the following ways: Method 1: Using the add() method This built-in function can be used to add elements to a set. However, this method can add only one element at a time.  Method 2: Using the update() method This method is used to add two or more elements. This method accepts tuples, strings, lists, and all other sets as arguments.  How to Remove Elements From a Set  One can remove elements from a set using the built-in remove() function or the discard() method. The remove() function may sometimes display the KeyError if the element is not present in the set. However, you can use the discard() function to avoid this. This way, the set does not change if the element is not present in the set. Elements can also be removed using the pop() function. This function is used to remove and return an element from a set. However, it removes the last element from the set.  The clear() function deletes all the elements from a set.  Sets being unordered, the elements do not have a specific index. Therefore, one cannot access the items by referring to an index.  6. Python Dictionary Dictionary is a type of python data type in which collections are unordered, and values are in pairs called key-value pairs. This type of data type is useful when there is a high volume of data. One of the best functions of Dictionaries data type is retrieving the data for which it is optimized. The value can only be retrieved if one knows the key to retrieve it.  Braces {} (curly brackets) are used to define dictionaries data type in Python. A Pair in the dictionary data type is an item which is represented as key:value. The value and the key can be of any data type. Python Dictionary can look like this: >> d = {3:’key’,4:’value’} How to Create a Dictionary To create a dictionary in Python, a sequence of elements is placed inside curly brackets, and the elements are separated using a comma. The values in a dictionary can be repeated, and it can be any datatype. However, keys should not only be immutable but also cannot be repeated. You can also create a dictionary using the built-in dict() function. To create an empty dictionary, just place it in curly brackets {}.  Accessing the Key-value in a Dictionary To access the items in a dictionary, refer to their key names or use the get() method.  7. Boolean Type There can be only two types of value in the Boolean data type of Python, and that is True or False.  It can look like this: >>> type(True) <class ‘bool’> >>> type(False) <class ‘bool’> The true value in the Boolean context is called “truthy”, and for false value in the Boolean context, it is called “falsy”. Truthy is defined by the objects in boolean, which is equal to True, and in the same way, Falsy is defined by the objects equal to falsy. One can also evaluate Non-Boolean objects in a Boolean context. Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Conclusion Python is the third most popular programming language, after JavaScript and HTML/CSS, used by software developers all across the globe. It is widely used for data analytics. If you are reading this article, you are probably learning Python or trying to become a Python developer. We hope this article helped you learn about the data types in Python, including numeric data types in Python and primitive data types in Python. Data types in Python with examples will help you understand what values can be assigned to variables and what operations can be performed on the data. If you’re interested to learn python & want to get your hands dirty on various tools and libraries, check out Executive PG Program in Data Science. This comprehensive course will help you extensively answer questions like ‘what are the different data types in Python?’ apart from building a base in machine learning, big data, NLP, and more. Once you acquire knowledge about the different data types in Python, working with the humongous amounts of data that industries generate will be easier.  Hurry! Enroll now and boost your chances of getting hired today.

by Rohit Sharma

11 Jul 2024

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16859

Introduction to Data Mining In its raw form, data requires efficient processing to transform into valuable information. Predicting outcomes hinges on uncovering patterns, anomalies, or correlations within the data, a process known as “knowledge discovery in databases.”  The term “data mining” emerged in the 1990s, integrating principles from statistics, artificial intelligence, and machine learning. As someone deeply entrenched in this field, I’ve witnessed how automated data mining, particularly through decision tree in data mining, revolutionized analysis, accelerating the process significantly.. With data mining, users can uncover insights and extract valuable knowledge from vast datasets more swiftly and effectively than ever before. It’s truly remarkable how technology has transformed the landscape of data analysis, making it more accessible and efficient for professionals across various industries. Data mining might also be referred to as the process of identifying hidden patterns of information which require categorization. Only then the data can be converted into useful data. The useful data can be fed into a data warehouse, data mining algorithms, data analysis for decision making. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Decision tree in Data mining A type of data mining technique, Decision tree in data mining builds a model for classification of data. The models are built in the form of the tree structure and hence belong to the supervised form of learning. Other than the classification models, decision trees are used for building regression models for predicting class labels or values aiding the decision-making process. Both the numerical and categorical data like gender, age, etc. can be used by a decision tree. Explore our Popular Data Science Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Certifications Structure of a decision tree The structure of a decision tree consists of a root node, branches, and leaf nodes. The branched nodes are the outcomes of a tree and the internal nodes represent the test on an attribute. The leaf nodes represent a class label.  Working of a decision tree 1. A decision tree works under the supervised learning approach for both discreet and continuous variables. The dataset is split into subsets on the basis of the dataset’s most significant attribute. Identification of the attribute and splitting is done through the algorithms. 2. The structure of the decision tree consists of the root node, which is the significant predictor node. The process of splitting occurs from the decision nodes which are the sub-nodes of the tree. The nodes which do not split further are termed as the leaf or terminal nodes.  3. The dataset is divided into homogenous and non-overlapping regions following a top-down approach. The top layer provides the observations at a single place which then splits into branches. The process is termed as “Greedy Approach” due to its focus only on the current node rather than the future nodes. 4. Until and unless a stop criterion is reached, the decision tree will keep on running. 5. With the building of a decision tree, lots of noise and outliers are generated. To remove these outliers and noisy data, a method of “Tree pruning” is applied. Hence, the accuracy of the model increases. 6. Accuracy of a model is checked on a test set consisting of test tuples and class labels. An accurate model is defined based on the percentages of classification test set tuples and classes by the model.  Figure 1: An example of an unpruned and a pruned tree Source Types of Decision Tree Decision trees lead to the development of models for classification and regression based on a tree-like structure. The data is broken down into smaller subsets. The result of a decision tree is a tree with decision nodes and leaf nodes. Two types of decision trees are explained below: 1. Classification The classification includes the building up of models describing important class labels. They are applied in the areas of machine learning and pattern recognition. Decision trees in machine learning through classification models lead to Fraud detection, medical diagnosis, etc. Two step process of a classification model includes: Learning: A classification model based on the training data is built. Classification: Model accuracy is checked and then used for classification of the new data. Class labels are in the form of discrete values like “yes”, or “no”, etc. Figure 2: Example of a classification model. Source 2. Regression Regression models are used for the regression analysis of data, i.e. the prediction of numerical attributes.  These are also called continuous values. Therefore, instead of predicting the class labels, the regression model predicts the continuous values.  List of Algorithms Used A decision tree algorithm known as “ID3” was developed in 1980 by a machine researcher named, J. Ross Quinlan. This algorithm was succeeded by other algorithms like C4.5 developed by him. Both the algorithms applied the greedy approach. The algorithm C4.5 doesn’t use backtracking and the trees are constructed in a top-down recursive divide and conquer manner. The algorithm used a training dataset with class labels which get divided into smaller subsets as the tree gets constructed. Three parameters are selected initially- attribute list, attribute selection method, and data partition. Attributes of the training set are described in the attribute list. Attribution selection method includes the method for selection of the best attribute for discrimination among the tuples. A tree structure depends on the attribute selection method. The construction of a tree starts with a single node. Splitting of the tuples occurs when different class labels are represented in a tuple. This will lead to the branch formation of the tree. The method of splitting determines which attribute should be selected for the data partition. Based on this method, the branches are grown from a node based on the outcome of the test. The method of splitting and partitioning is recursively carried out, ultimately resulting in a decision tree for the training dataset tuples. The process of tree formation keeps on going until and unless the tuples left cannot be partitioned further. The complexity of the algorithm is denoted by  n * |D| * log |D|  Where, n is the number of attributes in training dataset D and |D| is the number of tuples. Source Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Figure 3: A discrete value splitting  The lists of algorithms used in a decision tree are: ID3 The whole set of data S is considered as the root node while forming the decision tree. Iteration is then carried out on every attribute and splitting of the data into fragments. The algorithm checks and takes those attributes which were not taken before the iterated ones. Splitting data in the ID3 algorithm is time consuming and is not an ideal algorithm as it overfits the data. C4.5 It is an advanced form of an algorithm as the data are classified as samples. Both continuous and discrete values can be handled efficiently unlike ID3. Method of pruning is present which removes the unwanted branches. CART Both classification and regression tasks can be performed by the algorithm. Unlike ID3 and C4.5, decision points are created by considering the Gini index. A greedy algorithm is applied for the splitting method aiming to reduce the cost function. In classification tasks, the Gini index is used as the cost function to indicate the purity of leaf nodes. In regression tasks, sum squared error is used as the cost function to find the best prediction. CHAID As the name suggests, it stands for Chi-square Automatic Interaction Detector, a process dealing with any type of variables. They might be nominal, ordinal, or continuous variables. Regression trees use the F-test, while the Chi-square test is used in the classification model. upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4   MARS It stands for Multivariate adaptive regression splines. The algorithm is specially implemented in regression tasks, where the data is mostly non-linear. Greedy Recursive Binary Splitting A binary splitting method occurs resulting in two branches. Splitting of the tuples is carried out with the calculation of the split cost function. The lowest cost split is selected and the process is recursively carried out to calculate the cost function of the other tuples. Functions of Decision Tree in Data Mining   Classification: Decision trees serve as powerful tools for classification tasks in data mining. They classify data points into distinct categories based on predetermined criteria.  Prediction: Decision trees can predict outcomes by analyzing input variables and identifying the most likely outcome based on historical data patterns.  Visualization: Decision trees offer a visual representation of the decision-making process, making it easier for users to interpret and understand the underlying logic.  Feature Selection: Decision trees assist in identifying the most relevant features or variables that contribute to the classification or prediction process.  Interpretability: Decision trees provide transparent and interpretable models, allowing users to understand the rationale behind each decision made by the decision tree algorithm in data mining.  Overall, decision trees play a crucial role in data mining by facilitating classification, prediction, visualization, feature selection, and interpretability in the analysis of large datasets. Decision Tree Examples in Real World Predict loan eligibility process from given data. Step1: Loading of the data  The null values can be either dropped off or filled with some values. The original dataset’s shape was (614,13), and the new data-set after dropping the null values is (480,13). Step2: a look at the dataset. Step3: Splitting the data into training and test sets. Step 4: Build the model and fit the train set Before visualization some calculations are to be made. Calculation 1: calculate the entropy of the total dataset. Calculation 2: Find the entropy and gain for every column. Gender column Condition 1: data-set with all male’s in it and then, p = 278, n=116 , p+n=489 Entropy(G=Male) = 0.87 Condition 2: data-set with all female’s in it and then, p = 54 , n = 32 , p+n = 86 Entropy(G=Female) = 0.95 Average information in gender column Married column Condition 1: Married = Yes(1) In this split the whole data-set with Married status yes p = 227 , n = 84 , p+n = 311 E(Married = Yes) = 0.84 Condition 2: Married = No(0) In this split the whole data-set with Married status no p = 105 , n = 64 , p+n = 169 E(Married = No) = 0.957 Average Information in Married column is Educational column Condition 1: Education = Graduate(1) p = 271 , n = 112 , p+n = 383 E(Education = Graduate) = 0.87 Condition 2: Education = Not Graduate(0) p = 61 , n = 36 , p+n = 97 E(Education = Not Graduate) = 0.95 Average Information of Education column= 0.886 Gain = 0.01 4) Self-Employed Column Condition 1: Self-Employed = Yes(1) p = 43 , n = 23 , p+n = 66 E(Self-Employed=Yes) = 0.93 Condition 2: Self-Employed = No(0) p = 289 , n = 125 , p+n = 414 E(Self-Employed=No) = 0.88 Average Information in Self-Employed in Education Column = 0.886 Gain = 0.01 Credit Score column: the column has 0 and 1 value. Condition 1: Credit Score = 1 p = 325 , n = 85 , p+n = 410 E(Credit Score = 1) = 0.73 Condition 2: Credit Score = 0 p = 63 , n = 7 , p+n = 70 E(Credit Score = 0) = 0.46 Average Information in Credit Score column = 0.69 Gain = 0.2 Compare all the gain values Credit score has the highest gain. Hence, it will be used as the root node. Step 5: Visualize the Decision Tree Figure 5: Decision tree with criterion Gini Source Figure 6: Decision tree with criterion entropy Source  Step 6: Check the score of the model Almost 80% percent accuracy scored. Applications of Decision Tree in Data Mining Decision trees are mostly used by information experts to carry on an analytical investigation. They might be used extensively for business purposes to analyze or predict difficulties. The flexibility of the decision tree allows them to be used in a different area: 1. Healthcare Decision trees allow the prediction of whether a patient is suffering from a particular disease with conditions of age, weight, sex, etc. Other predictions include deciding the effect of medicine considering factors like composition, period of manufacture, etc. 2. Banking sectors Decision trees help in predicting whether a person is eligible for a loan considering his financial status, salary, family members, etc. It can also identify credit card frauds, loan defaults, etc. 3. Educational Sectors Shortlisting of a student based on his merit score, attendance, etc. can be decided with the help of decision trees.  Advantages of Decision Tree in Data Mining The interpretable results of a decision model can be represented to senior management and stakeholders. While building a decision tree model, preprocessing of the data, i.e. normalization, scaling, etc. is not required. Both types of data- numerical and categorical can be handled by a decision tree which displays its higher efficiency of use over other algorithms. Missing value in data doesn’t affect the process of a decision tree thereby making it a flexible algorithm. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? What Next?  If you are interested in gaining hands-on experience in data mining and getting trained by experts in the, you can check out upGrad’s Executive PG Program in Data Science. The course is directed for any age group within 21-45 years of age with minimum eligibility criteria of 50% or equivalent passing marks in graduation. Any working professionals can join this executive PG program certified from IIIT Bangalore. Conclusion: Understanding a decision tree in data mining is pivotal for mid-career professionals seeking to enhance their analytical skills. Decision trees serve as powerful tools for classification and prediction tasks, offering a clear and interpretable framework for data analysis. By exploring the various types of decision tree in data mining with examples, professionals can gain valuable insights into their applications across diverse industries. Armed with this knowledge, individuals can leverage decision trees to make informed decisions and drive business outcomes. Moving forward, continued learning and practical application of decision tree techniques will further empower professionals to excel in the dynamic field of data mining.  

by Rohit Sharma

04 Jul 2024

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82805

What is a Data Analytics Lifecycle? Data is crucial in today’s digital world. As it gets created, consumed, tested, processed, and reused, data goes through several phases/ stages during its entire life. A data analytics architecture maps out such steps for data science professionals. It is a cyclic structure that encompasses all the data life cycle phases, where each stage has its significance and characteristics. The lifecycle’s circular form guides data professionals to proceed with data analytics in one direction, either forward or backward. Based on the newly received information, professionals can scrap the entire research and move back to the initial step to redo the complete analysis as per the lifecycle diagram for the data analytics life cycle. However, while there are talks of the data analytics lifecycle among the experts, there is still no defined structure of the mentioned stages. You’re unlikely to find a concrete data analytics architecture that is uniformly followed by every data analysis expert. Such ambiguity gives rise to the probability of adding extra phases (when necessary) and removing the basic steps. There is also the possibility of working for different stages at once or skipping a phase entirely. One of the other main reasons why the Data Analytics lifecycle or business analytics cycle was created was to address the problems of Big Data and Data Science. The 6 phases of Data Analysis is a process that focuses on the specific demands that solving Big Data problems require. The six data analysis phases or steps: ask, prepare, process, analyze, share, and act. The meticulous step-by-step 6 phases of Data Analysis method help in mapping out all the different processes associated with the process of data analysis.  Learn Data Science Courses online at upGrad So if we are to have a discussion about Big Data analytics life cycle, then these 6 stages will likely come up to present as a basic structure. The data analytics life cycle in big data constitutes the fundamental steps in ensuring that the data is being acquired, processed, analyzed and recycles properly. upGrad follows these basic steps to determine a data professional’s overall work and the data analysis results. Types of Data Anaytics Descriptive Analytics Descriptive analytics serves as a time machine for organizations, allowing them to delve into their past. This type of analytics is all about gathering and visualizing historical data, answering fundamental questions like “what happened?” and “how many?” It essentially provides a snapshot of the aftermath of decisions made at the organizational level, aiding in measuring their impact. For instance, in a corporate setting, descriptive analytics, often dubbed as “business intelligence,” might play a pivotal role in crafting internal reports. These reports could encapsulate sales and profitability figures, breaking down the numbers based on divisions, product lines, and geographic regions. Diagnostic Analytics While descriptive analytics lays the groundwork by portraying what transpired, diagnostic analytics takes a step further by unraveling the mysteries behind the events. It dives into historical data points, meticulously identifying patterns and dependencies among variables that can explain a particular outcome. In essence, it answers the question of “why did it happen?” In a practical scenario, imagine a corporate finance department using diagnostic analytics to dissect the impacts of currency exchange, local economics, and taxes on results across various geographic regions. Predictive Analytics Armed with the knowledge gleaned from descriptive and diagnostic analytics, predictive analytics peers into the future. It utilizes historical trends to forecast what might unfold in the days to come. A classic data analytics lifecycle with example involves predictive analysts using their expertise to project the business outcomes of decisions, such as increasing the price of a product by a certain percentage. In a corporate finance context, predictive analytics could be seamlessly integrated to incorporate forecasted economic and market-demand data. This, in turn, aids in predicting sales for the upcoming month or quarter, allowing organizations to prepare strategically. Prescriptive Analytics Taking the analytics journey to its zenith, prescriptive analytics utilizes machine learning to offer actionable recommendations. It goes beyond predicting future outcomes; it actively guides organizations on how to achieve desired results. This could involve optimizing company operations, boosting sales, and driving increased revenue. In the corporate finance department, prescriptive analytics could play a pivotal role in generating recommendations for relative investments. This might encompass making informed decisions about production and advertising budgets, broken down by product line and region, for the upcoming month or quarter. Phases of Data Analytics Lifecycle A scientific method that helps give the data analytics life cycle a structured framework is divided into six phases of data analytics architecture. The framework is simple and cyclical. This means that all these steps in the data analytics life cycle in big data will have to be followed one after the other. It is also interesting to note that these steps can be followed both forward and backward as they are cyclical in nature. So here are the 6 phases of data analyst that are the most basic processes that need to be followed in data science projects.  Phase 1: Data Discovery and Formation Everything begins with a defined goal. In this phase, you’ll define your data’s purpose and how to achieve it by the time you reach the end of the data analytics lifecycle. Everything begins with a defined goal. In this phase, you’ll define your data’s purpose and how to achieve it by the time you reach the end of the data analytics lifecycle. The goal of this first phase is to make evaluations and assessments to come up with a basic hypothesis for resolving any problem and challenges in the business.  The initial stage consists of mapping out the potential use and requirement of data, such as where the information is coming from, what story you want your data to convey, and how your organization benefits from the incoming data. As a data analyst, you will have to study the business industry domain, research case studies that involve similar data analytics and, most importantly, scrutinize the current business trends. Then you also have to assess all the in-house infrastructure and resources, time and technology requirements to match with the previously gathered data. After the evaluations are done, the team then concludes this stage with hypotheses that will be tested with data later. This is the preliminary stage in the big data analytics lifecycle and a very important one.  Basically, as a data analysis expert, you’ll need to focus on enterprise requirements related to data, rather than data itself. Additionally, your work also includes assessing the tools and systems that are necessary to read, organize, and process all the incoming data. Must read: Learn excel online free! Essential activities in this phase include structuring the business problem in the form of an analytics challenge and formulating the initial hypotheses (IHs) to test and start learning the data. The subsequent phases are then based on achieving the goal that is drawn in this stage. So you will need to develop an understanding and concept that will later come in handy while testing it with data.  Our learners also read: Python free courses! upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Preparing for a data analyst role? Sharpen your interview skills with our comprehensive list of data analyst interview questions and answers to confidently tackle any challenge thrown your way. Phase 2: Data Preparation and Processing This stage consists of everything that has anything to do with data. In phase 2, the attention of experts moves from business requirements to information requirements. The data preparation and processing step involve collecting, processing, and cleansing the accumulated data. One of the essential parts of this phase is to make sure that the data you need is actually available to you for processing. The earliest step of the data preparation phase is to collect valuable information and proceed with the data analytics lifecycle in a business ecosystem. Data is collected using the below methods: Data Acquisition: Accumulating information from external sources. Data Entry: Formulating recent data points using digital systems or manual data entry techniques within the enterprise. Signal Reception: Capturing information from digital devices, such as control systems and the Internet of Things. The Data preparation stage in the big data analytics life cycle requires something known as an analytical sandbox. This is a scalable platform that data analysts and data scientists use to process data. The analytical sandbox is filled with data that was executed, loaded and transformed into the sandbox. This stage in the business analytical lifecycle does not have to happen in a predetermined sequence and can be repeated later if the need arises.  Read: Data Analytics Vs Data Science Top Data Science Skills to Learn Top Data Science Skills to Learn 1 Data Analysis Course Inferential Statistics Courses 2 Hypothesis Testing Programs Logistic Regression Courses 3 Linear Regression Courses Linear Algebra for Analysis Phase 3: Design a Model After mapping out your business goals and collecting a glut of data (structured, unstructured, or semi-structured), it is time to build a model that utilizes the data to achieve the goal. This phase of the data analytics process is known as model planning.  There are several techniques available to load data into the system and start studying it: ETL (Extract, Transform, and Load) transforms the data first using a set of business rules, before loading it into a sandbox. ELT (Extract, Load, and Transform) first loads raw data into the sandbox and then transform it. ETLT (Extract, Transform, Load, Transform) is a mixture; it has two transformation levels. Also read: Free data structures and algorithm course! This step also includes the teamwork to determine the methods, techniques, and workflow to build the model in the subsequent phase. The model’s building initiates with identifying the relation between data points to select the key variables and eventually find a suitable model. Data sets are developed by the team to test, train and produce the data. In the later phases, the team builds and executes the models that were created in the model planning stage.  Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Phase 4: Model Building This step of data analytics architecture comprises developing data sets for testing, training, and production purposes. The data analytics experts meticulously build and operate the model that they had designed in the previous step. They rely on tools and several techniques like decision trees, regression techniques (logistic regression), and neural networks for building and executing the model. The experts also perform a trial run of the model to observe if the model corresponds to the datasets. It helps them determine whether the tools they have currently are going to sufficiently execute the model or if they need a more robust system for it to work properly.  Checkout: Data Analyst Salary in India Phase 5: Result Communication and Publication Remember the goal you had set for your business in phase 1? Now is the time to check if those criteria are met by the tests you have run in the previous phase. The communication step starts with a collaboration with major stakeholders to determine if the project results are a success or failure. The project team is required to identify the key findings of the analysis, measure the business value associated with the result, and produce a narrative to summarise and convey the results to the stakeholders. Phase 6: Measuring of Effectiveness As your data analytics lifecycle draws to a conclusion, the final step is to provide a detailed report with key findings, coding, briefings, technical papers/ documents to the stakeholders. Additionally, to measure the analysis’s effectiveness, the data is moved to a live environment from the sandbox and monitored to observe if the results match the expected business goal. If the findings are as per the objective, the reports and the results are finalized. However, suppose the outcome deviates from the intent set out in phase 1then. You can move backward in the data analytics lifecycle to any of the previous phases to change your input and get a different output. If there are any performative constraints in the model, then the team goes back to make adjustments to the model before deploying it.  Also Read: Data Analytics Project Ideas Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Importance of Data Analytics Lifecycle The Data Analytics Lifecycle outlines how data is created, gathered, processed, used, and analyzed to meet corporate objectives. It provides a structured method of handling data so that it may be transformed into knowledge that can be applied to achieve organizational and project objectives. The process offers the guidance and techniques needed to extract information from the data and move forward to achieve corporate objectives. Data analysts use the circular nature of the lifecycle to go ahead or backward with data analytics. They can choose whether to continue with their current research or abandon it and conduct a fresh analysis in light of the recently acquired insights. Their progress is guided by the Data Analytics lifecycle. Big Data Analytics Lifecycle example Take a chain of retail stores as an example, which seeks to maximize the prices of its products in order to increase sales. It is an extremely difficult situation because the retail chain has thousands of products spread over hundreds of sites. After determining the goal of the chain of stores, you locate the data you require, prepare it, and follow the big data analytics lifecycle. You see many types of clients, including regular clients and clients who make large purchases, such as contractors. You believe that finding a solution lies in how you handle different types of consumers. However, you must consult the customer team about this if you lack adequate knowledge To determine whether different client categories impact the model findings and obtain the desired output, you must first obtain a definition, locate data, and conduct hypothesis testing. As soon as you are satisfied with the model’s output, you may put it into use, integrate it into your operations, and then set the prices you believe to be the best ones for all of the store’s outlets. This is a small-scale example of how deploying the business analytics cycle can positively affect the profits of a business. But this model is used across huge business chains in the world.  Who uses Big data and analytics? Huge Data and analytics are being used by medium to large-scale businesses throughout the world to achieve great success. Big data analytics technically means the process of analyzing and processing a huge amount of data to find trends and patterns. This makes them able to quickly find solutions to problems by making fast and adequate decisions based on the data.  The king of online retail, Amazon, accesses consumer names, addresses, payments, and search history through its vast data bank and uses them in advertising algorithms and to enhance customer relations. The American Express Company uses big data to study consumer behavior. Capital One, a market leader, uses big data analysis to guarantee the success of its consumer offers. Netflix leverages big data to understand the viewing preferences of users from around the world. Spotify is a platform that is using the data analytics lifecycle in big data to its fullest. They use this method to make sure that each user gets their favourite type of music handed to them.  Big data is routinely used by companies like Marriott Hotels, Uber Eats, McDonald’s, and Starbucks as part of their fundamental operations. Benefits of Big data and analytics Learning the life cycle of data analytics gives you a competitive advantage. Businesses, be it large or small, can benefit a lot from big data effectively. Here are some of the benefits of Big data and analytics lifecycle. 1. Customer Loyalty and Retention Customers’ digital footprints contain a wealth of information regarding their requirements, preferences, buying habits, etc. Businesses utilize big data to track consumer trends and customize their goods and services to meet unique client requirements. This significantly increases consumer satisfaction, brand loyalty, and eventually, sales. Amazon has used this big data and analytics lifecycle to its advantage by providing the most customized buying experience, in which recommendations are made based on past purchases and items that other customers have purchased, browsing habits, and other characteristics. 2. Targeted and Specific Promotions With the use of big data, firms may provide specialized goods to their target market without spending a fortune on ineffective advertising campaigns. Businesses can use big data to study consumer trends by keeping an eye on point-of-sale and online purchase activity. Using these insights, targeted and specific marketing strategies are created to assist businesses in meeting customer expectations and promoting brand loyalty. 3. Identification of Potential Risks Businesses operate in high-risk settings and thus need efficient risk management solutions to deal with problems. Creating efficient risk management procedures and strategies depends heavily on big data. Big data analytics life cycle and tools quickly minimize risks by optimizing complicated decisions for unforeseen occurrences and prospective threats. 4. Boost Performance The use of big data solutions can increase operational effectiveness. Your interactions with consumers and the important feedback they provide enable you to gather a wealth of relevant customer data. Analytics can then uncover significant trends in the data to produce products that are unique to the customer. In order to provide employees more time to work on activities demanding cognitive skills, the tools can automate repetitive processes and tasks. 5. Optimize Cost One of the greatest benefits of the big data analytics life cycle is the fact that it can help you cut down on business costs. It is a proven fact that the return cost of an item is much more than the shipping cost. By using big data, companies can calculate the chances of the products being returned and then take the necessary steps to make sure that they suffer minimum losses from product returns.  Ways to Use Data Analytics Let’s delve into how this transformative data analysis stages can be harnessed effectively. Enhancing Decision-Making Data analytics life cycle sweeps away the fog of uncertainty, ushering in an era where decisions are grounded in insights rather than guesswork. Whether it’s selecting the most compelling content, orchestrating targeted marketing campaigns, or shaping innovative products, organizations leverage data analysis life cycle to drive informed decision-making. The result? Better outcomes and heightened customer satisfaction. Elevating Customer Service Customizing customer service to individual needs is no longer a lofty aspiration but a tangible reality with data analytics. The power of personalization, fueled by analyzed data, fosters stronger customer relationships. Insights into customers’ interests and concerns enable businesses to offer more than just products – they provide tailored recommendations, creating a personalized journey that resonates with customers. Efficiency Unleashed In the realm of operational efficiency, the life cycle of data analytics or data analytics phases emerges as a key ally. Streamlining processes, cutting costs, and optimizing production become achievable feats with a profound understanding of audience preferences. As the veil lifts on what captivates your audience, valuable time and resources are saved, ensuring that efforts align seamlessly with audience interests. Mastering Marketing Data analytics life cycle or data analytics phases empowers businesses to unravel the performance tapestry of their marketing campaigns. Insights gleaned allow for meticulous adjustments and fine-tuning strategies for optimal results. Beyond this, identifying potential customers primed for interaction and conversion becomes a strategic advantage. The precision of data analytics life cycle ensures that every marketing endeavor resonates with the right audience, maximizing impact. Data Analytics Tools Python: A Versatile and Open-Source Programming Language Python stands out as a powerful and open-source programming language that excels in object-oriented programming. This language offers a diverse array of libraries tailored for data manipulation, visualization, and modeling. With its flexibility and ease of use, Python has become a go-to choice for programmers and data scientists alike. R: Unleashing Statistical Power through Open Source Programming R, another open-source programming language, specializes in numerical and statistical analysis. It boasts an extensive collection of libraries designed for data analysis and visualization. Widely embraced by statisticians and researchers, R provides a robust platform for delving into the intricacies of data with precision and depth. Tableau: Crafting Interactive Data Narratives Enter Tableau, a simplified yet powerful tool for data visualization and analytics. Its user-friendly interface empowers users to create diverse visualizations, allowing for interactive data exploration. With the ability to build reports and dashboards, Tableau transforms data into compelling narratives, presenting insights and trends in a visually engaging manner. Power BI: Empowering Business Intelligence with Ease Power BI emerges as a business intelligence powerhouse with its drag-and-drop functionality. This tool seamlessly integrates with multiple data sources and entices users with visually appealing features. Beyond its aesthetics, Power BI facilitates dynamic interactions with data, enabling users to pose questions and obtain immediate insights, making it an indispensable asset for businesses. QlikView: Unveiling Interactive Analytics and Guided Insights QlikView distinguishes itself by offering interactive analytics fueled by in-memory storage technology. This enables the analysis of vast data volumes and empowers users with data discoveries that guide decision-making. The platform excels in manipulating massive datasets swiftly and accurately, making it a preferred choice for those seeking robust analytics capabilities. Apache Spark: Real-Time Data Analytics Powerhouse Apache Spark, an open-source life cycle of data analytics engine, steps into the arena to process data in real-time. It executes sophisticated analytics through SQL queries and machine learning algorithms. With its prowess, Apache Spark addresses the need for quick and efficient data processing, making it an invaluable tool in the world of big data. SAS: Statistical Analysis and Beyond SAS, a statistical phases of data analysis software, proves to be a versatile companion for data enthusiasts. It facilitates analytics, data visualization, SQL queries, statistical analysis, and the development of machine learning models for predictive insights. SAS stands as a comprehensive solution catering to a spectrum of data-related tasks, making it an indispensable tool for professionals in the field. What are the Applications of Data Analytics? In the dynamic landscape of the digital era, business analytics life cycle applications play a pivotal role in extracting valuable insights from vast datasets. These applications empower organizations across various sectors to make informed decisions, enhance efficiency, and gain a competitive edge. Let’s delve into the diverse applications of business analytics life cycle and their impact on different domains. Business Intelligence Data analytics lifecycle case study applications serve as the backbone of Business Intelligence (BI), enabling businesses to transform raw data into actionable intelligence. Through sophisticated analysis, companies can identify trends, customer preferences, and market dynamics. This information aids in strategic planning, helping businesses stay ahead of the curve and optimize their operations for sustained success. Healthcare In the healthcare sector, data analytics applications contribute significantly to improving patient outcomes and operational efficiency. By analyzing patient records, treatment outcomes, and demographic data, healthcare providers can make data-driven decisions, personalize patient care, and identify potential health risks. This not only enhances the quality of healthcare services but also helps in preventing and managing diseases more effectively. Finance and Banking Financial institutions harness the power of data analytics applications or data analytics life cycles for example to manage risk, detect fraudulent activities, and make informed investment decisions. Analyzing market trends and customer behavior allows banks to offer personalized financial products, streamline operations, and ensure compliance with regulatory requirements. This, in turn, enhances customer satisfaction and builds trust within the financial sector. E-Commerce In the realm of e-commerce, data analytics applications revolutionize the way businesses understand and cater to customer needs. By analyzing purchasing patterns, preferences, and browsing behavior, online retailers can create targeted marketing strategies, optimize product recommendations, and enhance the overall customer shopping experience. This leads to increased customer satisfaction and loyalty. Education Data analytics applications are transforming the education sector by providing insights into student performance, learning trends, and institutional effectiveness. Educators can tailor their teaching methods based on data-driven assessments, identify areas for improvement, and enhance the overall learning experience. This personalized approach fosters student success and contributes to the continuous improvement of educational institutions. Manufacturing and Supply Chain In the manufacturing industry, data analytics applications optimize production processes, reduce downtime, and improve overall efficiency. By analyzing supply chain data, manufacturers can forecast demand, minimize inventory costs, and enhance product quality. This results in streamlined operations, reduced wastage, and increased competitiveness in the market. Conclusion The data analytics lifecycle is a circular process that consists of six basic stages that define how information is created, gathered, processed, used, and analyzed for business goals. However, the ambiguity in having a standard set of phases for data analytics architecture does plague data experts in working with the information. But the first step of mapping out a business objective and working toward achieving them helps in drawing out the rest of the stages. upGrad’s Executive PG Programme in Data Science in association with IIIT-B and a certification in Business Analytics covers all these stages of data analytics architecture. The program offers detailed insight into the professional and industry practices and 1-on-1 mentorship with several case studies and examples. Hurry up and register now!

by Rohit Sharma

04 Jul 2024