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Rohit Sharma

627+ of articles published

Critical Analyst / Storytelling Expert / Narrative Designer

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upGrad

Current role in the industry:

Head of Revenue & Programs at upGrad

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M.Tech., IIT Delhi

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Data Analysis

Management Consulting

Business Analytics

Matlab

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Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

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Characteristics of Big Data: Types & 5V’s
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Characteristics of Big Data: Types & 5V’s

Introduction The world around is changing rapidly, we live a data-driven age now. Data is everywhere, from your social media comments, posts, and likes to your order and purchase data on the e-commerce websites that you visit daily. Your search data is used by the search engines to enhance your search results. For large organizations, this data is in the form of customer data, sales figures, financial data, and much more. You can imagine how much data is produced every second! Huge amounts of data are referred to as Big Data.  Check out our free courses to get an edge over the competition. Let us start with the basics concepts of Big Data and further proceed to list out and discuss the characteristics of big data. Read: Big data career path What is Big Data? Big Data refers to the huge collections of data that are structured and unstructured. This data may be sourced from servers, customer profile information, order and purchase data, financial transactions, ledgers, search history, and employee records. In large companies, this data collection is continuously growing with time. But the amount of data a company has is not important, but what it is doing with that data. Companies aim to analyze these huge collections of data properly to gain insights. The analysis helps them in understanding patterns in the data that eventually lead to better business decisions. All this helps in reducing time, efforts, and costs. But this humongous amount of data cannot be stored, processed, and studied using traditional methods of data analysis. Hence companies hire data analysts and data scientists who write programs and develop modern tools. Learn more about big data skills one needs to develop. Characteristics of Big data with examples will help you understand the various characteristics properly. Many Big Data characteristics have been discussed below precisely: Explore our Popular Software Engineering Courses Master of Science in Computer Science from LJMU & IIITB Caltech CTME Cybersecurity Certificate Program Full Stack Development Bootcamp PG Program in Blockchain Executive PG Program in Full Stack Development View All our Courses Below Software Engineering Courses Types of Big Data Big Data is present in three basic forms. They are –  1. Structured data As the name suggests, this kind of data is structured and is well-defined. It has a consistent order that can be easily understood by a computer or a human. This data can be stored, analyzed, and processed using a fixed format. Usually, this kind of data has its own data model. You will find this kind of data in databases, where it is neatly stored in columns and rows. Two sources of structured data are: Machine-generated data – This data is produced by machines such as sensors, network servers, weblogs, GPS, etc.  Human-generated data – This type of data is entered by the user in their system, such as personal details, passwords, documents, etc. A search made by the user, items browsed online, and games played are all human-generated information. For example, a database consisting of all the details of employees of a company is a type of structured data set. Learn: Mapreduce in big data 2. Unstructured data Any set of data that is not structured or well-defined is called unstructured data. This kind of data is unorganized and difficult to handle, understand and analyze. It does not follow a consistent format and may vary at different points of time. Most of the data you encounter comes under this category. For example, unstructured data are your comments, tweets, shares, posts, and likes on social media. The videos you watch on YouTube and text messages you send via WhatsApp all pile up as a huge heap of unstructured data. 3. Semi-structured data This kind of data is somewhat structured but not completely. This may seem to be unstructured at first and does not obey any formal structures of data models such as RDBMS. For example, NoSQL documents have keywords that are used to process the document. CSV files are also considered semi-structured data. After learning the basics and the characteristics of Big data with examples, now let us understand the features of Big Data. Read: Why to Become a Big Data Developer? Explore Our Software Development Free Courses Fundamentals of Cloud Computing JavaScript Basics from the scratch Data Structures and Algorithms Blockchain Technology React for Beginners Core Java Basics Java Node.js for Beginners Advanced JavaScript Characteristics of Big Data There are several characteristics of Big Data with example. The primary characteristics of Big Data are – 1. Volume Volume refers to the huge amounts of data that is collected and generated every second in large organizations. This data is generated from different sources such as IoT devices, social media, videos, financial transactions, and customer logs. Storing and processing this huge amount of data was a problem earlier. But now distributed systems such as Hadoop are used for organizing data collected from all these sources. The size of the data is crucial for understanding its value. Also, the volume is useful in determining whether a collection of data is Big Data or not. Data volume can vary. For example, a text file is a few kilobytes whereas a video file is a few megabytes. In fact, Facebook from Meta itself can produce an enormous proportion of data in a single day. Billions of messages, likes, and posts each day contribute to generating such huge data. The global mobile traffic was tallied to be around 6.2 ExaBytes( 6.2 billion GB) per month in the year 2016. Also read: Difference Between Big Data and Hadoop 2. Variety Another one of the most important Big Data characteristics is its variety. It refers to the different sources of data and their nature. The sources of data have changed over the years. Earlier, it was only available in spreadsheets and databases. Nowadays, data is present in photos, audio files, videos, text files, and PDFs. The variety of data is crucial for its storage and analysis.  A variety of data can be classified into three distinct parts: Structured data Semi-Structured data Unstructured data 3. Velocity This term refers to the speed at which the data is created or generated. This speed of data producing is also related to how fast this data is going to be processed. This is because only after analysis and processing, the data can meet the demands of the clients/users. Massive amounts of data are produced from sensors, social media sites, and application logs – and all of it is continuous. If the data flow is not continuous, there is no point in investing time or effort on it. As an example, per day, people generate more than 3.5 billion searches on Google. Check out big data certifications at upGrad 4. Value Among the characteristics of Big Data, value is perhaps the most important. No matter how fast the data is produced or its amount, it has to be reliable and useful. Otherwise, the data is not good enough for processing or analysis. Research says that poor quality data can lead to almost a 20% loss in a company’s revenue.  Data scientists first convert raw data into information. Then this data set is cleaned to retrieve the most useful data. Analysis and pattern identification is done on this data set. If the process is a success, the data can be considered to be valuable. Knowledge Read: Big data jobs & Career planning 5. Veracity This feature of Big Data is connected to the previous one. It defines the degree of trustworthiness of the data. As most of the data you encounter is unstructured, it is important to filter out the unnecessary information and use the rest for processing. Read: Big data jobs and its career opportunities Veracity is one of the characteristics of big data analytics that denotes data inconsistency as well as data uncertainty. As an example, a huge amount of data can create much confusion on the other hand, when there is a fewer amount of data, that creates inadequate information. Other than these five traits of big data in data science, there are a few more characteristics of big data analytics that have been discussed down below: 1. Volatility  One of the big data characteristics is Volatility. Volatility means rapid change. And Big data is in continuous change. Like data collected from a particular source change within a span of a few days or so. This characteristic of Big Data hampers data homogenization. This process is also known as the variability of data. 2. Visualization  Visualization is one more characteristic of big data analytics. Visualization is the method of representing that big data that has been generated in the form of graphs and charts. Big data professionals have to share their big data insights with non-technical audiences on a daily basis. In-Demand Software Development Skills JavaScript Courses Core Java Courses Data Structures Courses Node.js Courses SQL Courses Full stack development Courses NFT Courses DevOps Courses Big Data Courses React.js Courses Cyber Security Courses Cloud Computing Courses Database Design Courses Python Courses Cryptocurrency Courses Fundamental fragments of Big Data Let’s discuss the diverse traits of big data in data science a bit more in detail! Ingestion- In this step, data is gathered as well as processed. The process further extends when data is collected in batches or streams, and thereafter it is cleansed and organized to be finally prepared. Storage- After the collection of the required data, it is needed to be stored. Data is mainly stored in a data warehouse or data lake. Analysis- In this process, big data is processed to abstract valuable insights. There are four types of big data analytics: prescriptive, descriptive, predictive, and diagnostic. Consumption – This is the last stage of the big data process. The data insights are shared with non-technical audiences in the form of visualization or data storytelling. Read our Popular Articles related to Software Development Why Learn to Code? How Learn to Code? How to Install Specific Version of NPM Package? Types of Inheritance in C++ What Should You Know? Advantages and Attributes of Big Data  Big Data has emerged as a critical component of modern enterprises and sectors, providing several benefits and distinguishing itself from traditional data processing methods. The capacity to gather and interpret massive volumes of data has profound effects on businesses, allowing them to prosper in an increasingly data-driven environment.  Big Data characteristics come with several advantages. Here we have elucidated some of the advantages that explain the characteristics of Big Data with real-life examples:-  Informed Decision-Making: Big Data allows firms to make data-driven decisions. It helps businesses analyse huge amounts of data and can get important insights into consumer behaviour, market trends, and operational efficiency. This educated decision-making can result in better outcomes and a competitive advantage in the market. Improved Customer Experience: Big Data and its characteristics help in understanding customer data enabling companies to better understand consumer preferences, predict requirements, and personalise services. This results in better client experiences, increased satisfaction, and higher customer retention. Enhanced Operational Efficiency: The different features of Big Data analytics assist firms in optimizing their operations by finding inefficiencies and bottlenecks. This results in cheaper operations, lower costs, and improved overall efficiency. Product Development and Innovation: The 7 characteristics of Big Data offer insights that help stimulate both of these processes. Understanding market demands and customer preferences enables firms to produce new goods or improve existing ones in order to remain competitive. Risk Management: Various attributes of Big Data help by analysing massive databases, firms can identify possible hazards and reduce them proactively. Whether in financial markets, cybersecurity, or supply chain management, Big Data analytics aids in the effective prediction and control of risks. Personalised Marketing: By evaluating consumer behaviour and preferences, Big Data characteristics allow for personalised marketing techniques. This enables firms to design targeted marketing efforts, which increases the likelihood of turning leads into consumers with the help of Big Data and its characteristics.  Healthcare Advancements: Attributes of Big Data are being employed to examine patient information, medical history, and treatment outcomes. This contributes to customised therapy, early illness identification, and overall advances in healthcare delivery. Scientific Research and Discovery: Big Data is essential in scientific research because it allows researchers to evaluate massive datasets for patterns, correlations and discoveries. This is very useful in areas such as genetics, astronomy, and climate study. Real-time Analytics: Big Data characteristics and technologies enable businesses to evaluate and react to data in real-time. This is especially useful in areas such as banking, where real-time analytics may be used to detect fraud and anticipate stock market trends. Competitive Advantage: Businesses that properly use Big Data have a competitive advantage. Those who can quickly and efficiently assess and act on data insights have a higher chance of adapting to market changes and outperforming the competition. Application of Big Data in the Real World  The use of Big Data in the real world has become more widespread across sectors, affecting how businesses operate, make decisions, and engage with their consumers. Here, we look at some of the most famous Big Data applications in several industries. Healthcare  Predictive Analysis: Predictive analytics in healthcare uses Big Data to forecast disease outbreaks, optimise resource allocation, and enhance patient outcomes. Large datasets can be analysed to assist in uncovering trends and forecast future health hazards, allowing for proactive and preventative treatments. Personalised Medicine: Healthcare practitioners may adapt therapy to each patient by examining genetic and clinical data. Big Data facilitates the detection of genetic markers, allowing physicians to prescribe drugs and therapies tailored to a patient’s genetic composition. Electronic Health Records (EHR): The use of electronic health records has resulted in a massive volume of healthcare data. Big Data analytics is critical for processing and analyzing this information in order to improve patient care, spot patterns, and manage healthcare more efficiently. Finance Financial Fraud Detection: Big Data is essential to financial business’s attempts to identify and stop fraud. Real-time transaction data analysis identifies anomalous patterns or behaviours, enabling timely intervention to limit possible losses. Algorithmic Trading: Big Data is employed in financial markets to evaluate market patterns, news, and social media sentiment. Algorithmic trading systems use this information to make quick and educated investment decisions while optimizing trading methods. Credit Scoring and Risk Management: Big Data enables banks to more properly assess creditworthiness. Lenders can make more educated loan approval choices and manage risks by examining a wide variety of data, including transaction history, social behaviour, and internet activity. Retail  Customer Analytics: Retailers leverage Big Data to study customer behaviour, preferences, and purchasing history. This data is useful for establishing tailored marketing strategies, boosting inventory management, and improving the overall customer experience. Supply Chain Optimisation: Big Data analytics is used to improve supply chain operations by anticipating demand, enhancing logistics, and reducing delays. This ensures effective inventory management and lowers costs across the supply chain. Price Optimisation: Retailers use Big Data to dynamically modify prices depending on demand, rival pricing, and market trends. This allows firms to determine optimal pricing that maximises earnings while maintaining competition. Manufacturing  Predictive Maintenance: Big data is used in manufacturing to make predictions about the maintenance of machinery and equipment. Organisations can mitigate downtime by proactively scheduling maintenance actions based on sensor data and previous performance. Quality Control: Analysing data from the manufacturing process enables producers to maintain and enhance product quality. Big Data technologies understand patterns and abnormalities, enabling the early discovery and rectification of errors throughout the production process. Supplier Chain Visibility: Big Data gives firms complete visibility into their supplier chains. This insight aids in optimum utilisation of inventory, improved supplier collaboration, and on-time manufacturing and delivery. Telecommunications  Network Optimisation: Telecommunications businesses employ Big Data analytics to improve network performance. This involves examining data on call patterns, network traffic, and user behaviour to improve service quality and find opportunities for infrastructure enhancement. Customer Churn Prediction: By examining customer data, telecom companies can forecast which customers are likely to churn. This enables focused retention measures, such as tailored incentives or enhanced customer service, to help lessen turnover. Fraud Prevention: Big Data can help detect and prevent fraudulent activity in telecommunications, such as SIM card cloning and subscription fraud. Analysing trends and finding abnormalities aids in real-time fraud detection. Job Opportunities with Big Data  The Big Data employment market is varied, with possibilities for those with talents ranging from data analysis and machine learning to database administration and cloud computing. As companies continue to understand the potential of Big Data, the need for qualified people in these jobs is projected to remain high, making it an interesting and dynamic industry for anyone seeking a career in technology and analytics. Data Scientist: Data scientists use big data to uncover patterns and insights that are significant. They create and execute algorithms, analyse large databases, and present results to help guide decision-making. Data Engineer: The primary responsibility of a data engineer is to plan, build, and manage the infrastructure (such as warehouses and data pipelines) required for the effective processing and storing of massive amounts of data. Big Data Analysts: They interpret data to assist businesses in making educated decisions. They employ statistical approaches, data visualisation, and analytical tools to generate meaningful insights from large datasets. Machine Learning Engineer: By analysing large amounts of data using models and algorithms, machine learning engineers can build systems that are capable of learning and making judgments without the need for explicit programming. Database Administrator: Database administrators look after and administer databases, making sure they are scalable, secure, and function well. Administrators that work with Big Data often rely on distributed databases envisioned to manage large volumes of data. Business Intelligence (BI) Developer: BI developers construct tools and systems for collecting, interpreting, and presenting business information. They play an important role in converting raw data into usable insights for decision-makers. Data Architect: Data architects create the general architecture and structure of data systems, making sure that they satisfy the requirements of the company and follow industry best practices. Hadoop Developer: Hadoop developers work with tools such as HDFS, MapReduce, and Apache Spark. They create and execute solutions for processing and analyzing huge data collections. Data Privacy Analyst: With the growing significance of data privacy, analysts in this profession are responsible for ensuring that firms follow data protection legislation and apply appropriate privacy safeguards. IoT Data Analyst: Internet of Things (IoT) data analysts work with and analyse data created by IoT devices, deriving insights from massive volumes of sensor data collected in a variety of businesses. Cloud Solutions Architect: As enterprises transition to cloud platforms, cloud solutions architects develop and deploy Big Data solutions on cloud infrastructure to ensure scalability, dependability, and cost efficiency. Cybersecurity Analyst (Big Data): Experts in Big Data analyse enormous amounts of data to identify and address security issues. They employ advanced analytics to detect patterns suggestive of cyberattacks. Conclusion Big Data is the driving force behind major sectors such as business, marketing, sales, analytics, and research. It has changed the business strategies of customer-based and product-based companies worldwide. Thus, all the Big Data characteristics have to be given equal importance when it comes to analysis and decision-making. In this blog, we tried to list out and discuss the characteristics of big data, which, if grasped accurately, can fuel you to do wonders in the field of big data! If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore. Learn Software Development 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

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04 Mar 2024

Data Mining Architecture: Components, Types & Techniques
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10567

Data Mining Architecture: Components, Types & Techniques

Introduction Data mining is the process in which information that was previously unknown, which could be potentially very useful, is extracted from a very vast dataset. Data mining architecture or architecture of data mining techniques is nothing but the various components which constitute the entire process of data mining. Learn data science to gain expertise in data mining and remain competitive in the market.  Data Mining Architecture Components Let’s take a look at the components which make the entire data mining architecture.  1. Sources of Data The place where we get our data to work upon is known as the data source or the source of the data. There are many documentations presented, and one might also argue that the whole World Wide Web (WWW) is a big data warehouse. The data can be anywhere, and some might reside in text files, a standard spreadsheet document, or any other viable source like the internet. 2. Database or Data Warehouse Server The server is the place that holds all the data which is ready to be processed. The fetching of data works upon the user’s request, and, thus, the actual datasets can be very personal. 3. Data Mining Engine The field of data mining is incomplete without what is arguably the most crucial component of it, known as a data mining engine. It usually contains a lot of modules that can be used to perform a variety of tasks. The tasks which can be performed can be association, characterization, prediction, clustering, classification, etc. 4. Modules for Pattern Evaluation This module of the architecture is mainly employed to measure how interesting the pattern that has been devised is actually. For the evaluation purpose, usually, a threshold value is used. Another critical thing to note here is that this module has a direct link of interaction with the data mining engine, whose main aim is to find interesting patterns.  Our learners also read: Free Python Course with Certification upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 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 5. GUI or Graphical User Interface As the name suggests, this module of the architecture is what interacts with the user. GUI serves as the much-needed link between the user and the system of data mining. GUI’s main job is to hide the complexities involving the entire process of data mining and provide the user with an easy to use and understand module which would allow them to get an answer to their queries in an easy to understand fashion. 6. Knowledge Base The base of all the knowledge is vital for any data mining architecture. The knowledge base is usually used as the guiding beacon for the pattern of the results. It might also contain the data from what the users have experienced. The data mining engine interacts with the knowledge base often to both increase the reliability and accuracy of the final result. Even the pattern evaluation module has a link to the knowledge base. It interacts with the knowledge base on a regular interval to get various inputs and updates from it. Read: 16 Data Mining Projects Ideas & Topics For Beginners Types of data mining architecture There are four different types of architecture which have been listed below: 1. No-coupling Data Mining No-coupling architecture typically does not make the use of any functionality of the database. What no-coupling usually does is that it retrieves the required data from one or one particular source of data. That’s it; this type of architecture does not take any advantages whatsoever of the database in question. Because of this specific issue, no-coupling is usually considered a poor choice of architecture for the system of data mining. Still, it is often used for elementary processes involving data mining. 2. Loose coupling Data Mining Loose coupling data mining process employs a database to do the bidding of retrieval of the data. After it is done finding and bringing the data, it stores the data into these databases. This type of architecture is often used for memory-based data mining systems that do not require high scalability and high performance. 3. Semi-Tight coupling Data Mining Semi-Tight architecture makes uses of various features of the warehouse of data. These features of data warehouse systems are usually used to perform some tasks pertaining to data mining. Tasks like indexing, sorting, and aggregation are the ones that are generally performed. 4. Tight-coupling Data Mining The tight-coupling architecture differs from the rest in its treatment of data warehouses. Tight-coupling treats the data warehouse as a component to retrieve the information. It also makes use of all the features that you would find in the databases or the data warehouses to perform various data mining tasks. This type of architecture is usually known for its scalability, integrated information, and high performance. There are three tiers of this architecture which are listed below: 5. Data layer Data layer can be defined as the database or the system of data warehouses. The results of data mining are usually stored in this data layer. The data that this data layer houses can then be further used to present the data to the end-user in different forms like reports or some other kind of visualization. 6. Data Mining Application layer The job of Data mining application layer is to find and fetch the data from a given database. Usually, some data transformation has to be performed here to get the data into the format, which has been desired by the end-user.  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 7. Front end layer This layer has virtually the same job as a GUI. The front-end layer provides intuitive and friendly interaction with the user. The result of the data mining is usually visualized as some form or the other to the user by making use of this front-end layer. Also read: What is Text Mining: Techniques and Applications Techniques of Data Mining  There are several data mining techniques which are available for the user to make use of; some of them are listed below: 1. Decision Trees Decision trees are the most common technique for the mining of the data because of the complexity or lack thereof in this particular algorithm. The root of the tree is a condition. Each answer then builds upon this condition by leading us in a specific way, which will eventually help us to reach the final decision. 2. Sequential Patterns Sequential patterns are usually used to discover events that occur regularly or trends that can be found in any transactional data. 3. Clustering Clustering is a technique that automatically defines different classes based on the form of the object. The classes thus formed will then be used to place other similar kinds of objects in them. 4. Prediction This technique is usually employed when we are required to accurately determine an outcome that is yet to occur. These predictions are made by accurately establishing the relationship between independent and dependent entities. 5. Classification This technique is based out of a similar machine learning algorithm with the same name. This technique of classification is used to classify each item in question into predefined groups by making use of mathematical techniques such as linear programming, decision trees, neural networks, etc. 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? The Cornerstone: Delving into Data Warehouse Architecture Imagine a colossal library, meticulously organized and readily accessible, housing all your organizational data. This is the essence of a data warehouse, the foundational pillar of data mining architecture. Structured for efficient querying and analysis, it typically utilizes a star schema or snowflake schema to optimize data retrieval and performance. These schemas act as intricate maps, allowing data analysts to navigate with ease through the vast landscapes of information. Navigating the Labyrinth: OLAP Architecture in Data Mining – Unveiling Hidden Dimensions OLAP, short for Online Analytical Processing, empowers users to slice and dice data from various angles, shedding light on hidden patterns and insights. This OLAP architecture within the data warehouse leverages multidimensional cubes that enable fast retrieval and analysis of large datasets. Think of these cubes as Rubik’s cubes of information, where each side reveals a different perspective, granting invaluable insights for informed decision-making. Building the Engine: Demystifying the Architecture of a Typical Data Mining System Now, let’s delve into the core functionality of data mining itself. A typical data mining system architecture comprises five key stages, each playing a crucial role in the transformation of raw data into actionable insights: Data Acquisition: Data, the lifeblood of the system, is collected from diverse sources, including internal databases, external feeds, and internet-of-things (IoT) sensors. Imagine data flowing in like rivers, a vast lake of information ready to be explored. Data Preprocessing: Raw data can be messy and inconsistent, like unrefined ore. This stage involves cleansing, transforming, and integrating the data into a consistent format for further analysis. It’s akin to refining the ore, removing impurities and preparing it for further processing. Data Mining: Specialized algorithms, the skilled miners of the information world, are applied to uncover patterns, trends, and relationships within the preprocessed data. These algorithms work like sophisticated tools, sifting through the information to unveil hidden gems of knowledge. Pattern Evaluation: Extracted patterns, like potential diamonds unearthed from the mine, are carefully assessed for their validity, significance, and applicability. This stage involves rigorous testing and analysis to ensure the extracted insights are genuine and valuable. Deployment: Finally, the extracted insights are presented in a user-friendly format, such as reports, dashboards, or visualizations, empowering informed decision-making. Imagine these insights as polished diamonds, presented in a way that stakeholders can readily understand and utilize. Essential Components: Unveiling the Data Warehouse Components in Data Mining Several crucial components, each playing a distinct role, work in concert within the data warehouse architecture: Staging Area: This serves as a temporary haven for raw data, where it undergoes initial processing and preparation before being loaded into the main warehouse. Think of it as a sorting room, where data is organized and categorized before being placed on the shelves. ETL (Extract, Transform, Load): These processes act as the workhorses of the system, extracting data from various sources, transforming it into a consistent format, and loading it into the warehouse. Imagine ETL as a conveyor belt, efficiently moving and preparing the data for further analysis. Metadata Repository: This acts as the data dictionary, storing information about the data itself, including its structure, meaning, and lineage. It’s like a detailed index in the library, allowing users to easily find and understand the information they need. Query Tools: These empower users to interact with the data, ask questions, and extract insights. They are the tools that allow users to explore the library, search for specific information, and gain knowledge. Future-Proofing with Innovation: AI and Machine Learning Integration – Expanding the Horizons The realm of data mining is constantly evolving, driven by advancements in technology. The integration of AI and machine learning techniques promises even more sophisticated capabilities. These advanced algorithms can handle complex and unstructured data sources, like social media text and sensor data, unlocking deeper insights previously hidden within the information labyrinth. Imagine AI and machine learning as powerful new tools, opening up previously inaccessible data sources and revealing even more valuable gems of knowledge. Ethics and Transparency: Guiding Principles for Responsible Data Mining As data mining becomes more pervasive, ethical considerations take center stage. Responsible data practices, transparency in data collection and algorithm usage, and adherence to data privacy regulations are paramount to building trust and ensuring ethical data practices. Imagine navigating the information labyrinth responsibly, ensuring ethical treatment of the data while still extracting valuable insights. Democratizing Insights: Augmented Analytics – Empowering Everyone The rise of augmented analytics platforms is revolutionizing data accessibility. These platforms leverage natural language processing and automated model generation, empowering non-technical users to independently explore and analyze data, fostering a data-driven culture within organizations. Imagine everyone having access to a personal data analysis assistant, simplifying complex tasks and making insights readily available. Beyond the Horizon: Exploring the Future of Data Mining The future of data mining holds tremendous potential for innovation and growth, driven by advancements in technology and evolving business needs: Real-time Analytics: With the proliferation of IoT devices and sensors,data warehouse architecture in data mining will increasingly focus on real-time analytics, enabling organizations to respond promptly to changing market conditions, customer preferences, and emerging trends. Imagine having a real-time pulse on your business, constantly adapting and optimizing based on the latest data insights. Privacy-Preserving Techniques: To address privacy concerns, data mining algorithms will incorporate privacy-preserving techniques such as differential privacy, federated learning, and homomorphic encryption, ensuring compliance with data protection regulations while still extracting valuable insights. Imagine unlocking insights responsibly, safeguarding individual privacy while still gaining valuable knowledge. Interdisciplinary Applications: Data mining will continue to transcend traditional boundaries, finding applications in diverse fields such as healthcare, finance, transportation, and urban planning. Imagine data insights revolutionizing various industries, leading to breakthroughs and advancements in different sectors. Augmented Analytics: The rise of augmented analytics platforms will continue to empower non-technical users and democratize data exploration. Imagine a future where everyone, regardless of technical expertise, can leverage data to make informed decisions and contribute to organizational success. Conclusion  Due to the leaps and bounds made in the field of technology, the power and prowess of processing have significantly increased. This increment in technology has enabled us to go further and beyond the traditionally tedious and time-consuming ways of data processing, allowing us to get more complex datasets to gain insights that were earlier deemed impossible. This gave birth to the field of data mining. Data mining is a new upcoming field that has the potential to change the world as we know it. Data mining architecture or architecture of data mining system is how data mining is done. Thus, having knowledge of architecture is equally, if not more, important to having knowledge about the field itself. If you are curious to learn about data mining architecture, 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

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27 Feb 2024

6 Phases of Data Analytics Lifecycle Every Data Analyst Should Know About
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79275

6 Phases of Data Analytics Lifecycle Every Data Analyst Should Know About

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 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 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 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 cycle 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

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19 Feb 2024

Sorting in Data Structure: Categories & Types [With Examples]
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Sorting in Data Structure: Categories & Types [With Examples]

The arrangement of data in a preferred order is called sorting in the data structure. By sorting data, it is easier to search through it quickly and easily. The simplest example of sorting is a dictionary. Before the era of the Internet, when you wanted to look up a word in a dictionary, you would do so in alphabetical order. This made it easy. Imagine the panic if you had to go through a big book with all the English words from the world in a jumbled order! It is the same panic an engineer will go through if their data is not sorted and structured.  So, in short, sorting makes our lives easier. Check out our data science courses to learn in-depth about data science algorithms. In this post, we will take you through the different data structures & sorting algorithms. But first, let’s understand what a sorting algorithm is and sorting in data structure.  What is a Sorting Algorithm? A sorting algorithm is just a series of orders or instructions. In this, an array is an input, on which the sorting algorithm performs operations to give out a sorted array. Many children would have learned to sort in data structures in their computer science classes. It is introduced at an early stage to help interested children get an idea of deeper computer science topics – divide-and-conquer methods, binary trees, heaps, etc. Here’s an example of what sorting does. Let’s suppose you have an array of strings: [h,j,k,i,n,m,o,l] Now, sorting would yield an output array in alphabetical order. Output: [h,i,j,k,l,m,n,o] Let’s learn more about sorting in data structure. Checkout: Types of Binary Tree Importance Of Sorting In Data Structure Before delving into the different types of sorting in data structure, let’s first look at why sorting in data structure is important. Sorting in DSA actually provides users with several benefits. For example, when you are performing sorting on elements, many complications such as min/max, kth smallest/largest get automatically simplified. Furthermore, sorting also provides you with many algorithmic solutions, some of which might include divide and conquer, iterative, and recursive-based.  Last but not least, perhaps one of the biggest benefits of sorting in DSA is time complexity. As a coder, the ultimate goal is to solve any kind of complex problem within the minimum amount of time. That is where different types of sorting in data structure  come into play. It not only saves up your very precious time but also provides you with the right solution.  With that said, now lets’ take a look at the different sorting techniques in data structure.  Sorting Categories There are two different categories in sorting: Internal sorting: If the input data is such that it can be adjusted in the main memory at once, it is called internal sorting. External sorting: If the input data is such that it cannot be adjusted in the memory entirely at once, it needs to be stored in a hard disk, floppy disk, or any other storage device. This is called external sorting. Read: Interesting Data Structure Project Ideas and Topics Types of Sorting in Data Structure Here are a few of the most common types of sorting algorithms. 1. Merge Sort This algorithm works on splitting an array into two halves of comparable sizes. Each half is then sorted and merged back together by using the merge () function. Our learners also read: Free Data structures and Algorithms course! Here’s how the algorithm works: MergeSort(arr[], l,  r) If r > l Divide the array into two equal halves by locating the middle point:                middle m = (l+r)/2 Use the mergeSort function to call for the first half:                 Call mergeSort(arr, l, m) Call mergeSort for the second half:              Call mergeSort(arr, m+1, r) Use the merge () function to merge the two halves sorted in step 2 and 3:              Call merge(arr, l, m, r) Our learners also read: Free excel courses! Check out the image below to get a clear picture of how this works. Source Python program for merge sort implementation def mergeSort(a):      if len(a) >1:          mid = len(a)//2         A = a[:mid]            B = a[mid:]         mergeSort(A)          mergeSort(B)          i = j = k = 0             while i < len(A) and j < len(B):              if A[i] < B[j]:                  a[k] = A[i]                  i+=1             else:                  a[k] = B[j]                  j+=1             k+=1         while i < len(A):              a[k] = A[i]              i+=1             k+=1                while j < len(R):              a[k] = B[j]              j+=1             k+=1  def printList(a):      for i in range(len(a)):                  print(a[i],end=” “)      print()  if __name__ == ‘__main__’:      a = [12, 11, 13, 5, 6, 7]        mergeSort(a)      print(“Sorted array is: “, end=”\n”)      printList(a)  Learn more: Recursion in Data Structure: How Does it Work, Types & When Used 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 2. Selection Sort In this, at first, the smallest element is sent to the first position. Then, the next smallest element is searched in the remaining array and is placed at the second position. This goes on until the algorithm reaches the final element and places it in the right position.  Look at the picture below to understand it better.    Source Python program for selection sort implementation import sys  X = [6, 25, 10, 28, 11]  for i in range(len(X)):          min_idx = i      for j in range(i+1, len(X)):          if X[min_idx] > X[j]:              min_idx = j      X[i], X[min_idx] = X[min_idx], X[i]   print (“The sorted array is”)  for i in range(len(X)):      print(“%d” %X[i]),   Data Science Advanced Certification, 250+ Hiring Partners, 300+ Hours of Learning, 0% EMI Our learners also read: Free Python Course with Certification 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 upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 3. Bubble Sort It is the easiest and simplest of all the sorting algorithms. It works on the principle of repeatedly swapping adjacent elements in case they are not in the right order. In simpler terms, if the input is to be sorted in ascending order, the bubble sort will first compare the first two elements in the array. In case the second one is smaller than the first, it will swap the two, and move on to the next element, and so on. Example: Input: 637124 First pass 637124 -> 367124 : Bubble sort compares 6 and 3 and swaps them because 3<6. 367124 -> 367124 : Since 6<7, no swapping 367124 -> 361724 : Swapped 7and 1, as 7>1 361724 -> 361274 : Swapped 2 and 7, as 2<7 361274 -> 361247 : Swapped 4 and 7, as 4<7 Second pass 361247 -> 361247 361274 -> 316274 316274 -> 312674 312674 -> 312674 312674 -> 312647 Third pass 312647 -> 132647 132647 -> 123647 123647 -> 123647 123647 -> 123467 123467 -> 123467 As you can see, we get the ascending order result after three passes. Python program for bubble sort implementation def bubbleSort(a):      n = len(a)      for i in range(n):          for j in range(0, n-i-1):              if a[j] > a[j+1] :                  a[j], a[j+1] = a[j+1], a[j]   a = [64, 34, 25, 12, 22, 11, 90]  bubbleSort(a)  print (“The sorted array is:”)  for i in range(len(a)):      print (“%d” %a[i]),  Also read: Data Frames in Python: Python In-depth Tutorial 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? 4. Insertion Sort- Insertion sort falls under one of the most popular sorting types in data structure. It is basically an algorithm that helps to place an unsorted element at its suitable position in each iteration. It’s similar to the way you sort your cards during a card game. The first card is usually considered to be already sorted, and the next card that you pick up is then compared against the first one. Based on the first card, you wither place the unsorted second card on the right or left side of the former one. The insertion sort follows the same approach. 5. Quick Sort- Also known as partition exchange sorting, quick sort is yet another very popular sorting types in data structure that is based on partition. Using this particular algorithm, you pick on an element, which is known as the pivot element, and then rearrange the rest of the elements around the pivot element. It then further divides the array into two specific sub-arrays. Once you have fixed the pivot element, then it automatically disintegrates the rest of the elements. For example, elements that are lesser are placed on the left side of the pivot element, and elements on the right side are usually the ones that are greater. This whole process continues until only one element is left in the sub-array.  With this, we come to an end of the different types of sorting techniques in data structure. As quite visible from the list, each DSL sorting has its own advantages and disadvantages. Therefore, while choosing the most efficient one, you need to first understand the need for your data. For example, if you are looking for something stable, you should go with the merge. Simultaneously, if you are constrained in space, heap sort is the perfect choice for you.  How to Choose the Right Sorting Algorithm? Sorting algorithms play an essential role in organizing data efficiently, but selecting the right one depends on various factors. Here, we’ll explore key considerations to help you make an informed decision wholly based on your needs. Size of the Data Set When dealing with data, size matters. For smaller datasets, the choice of sorting algorithm may not be a critical concern, as most algorithms perform reasonably well with limited data. However, as your dataset grows in size, the selection becomes crucial. In such cases, prioritizing algorithms that are fast and efficient in managing memory becomes imperative. Consider options like merge sort or heap sort for large datasets, as they demonstrate better scalability and memory usage than simpler algorithms like bubble sort or insertion sort. Type of Data Understanding the nature of your data is pivotal in choosing the most effective sorting algorithm. Different algorithms exhibit varying performance depending on the data type. For numeric data, quicksort often outshines mergesort in terms of speed, while the scenario might be different for strings. For instance, if your dataset primarily consists of strings, you might want to explore sorting algorithms specifically designed for string comparisons, such as radix sort. Tailoring your choice based on the data type ensures optimal efficiency. Structure of the Data The existing structure of your data can significantly impact sorting performance. If your data is already partially sorted, leveraging algorithms that capitalize on this characteristic is prudent. Insertion sort, for example, excels in scenarios where the data is nearly sorted, outperforming more complex algorithms like quicksort. Before making a decision, assess the degree of pre-sortedness in your dataset and choose an algorithm that aligns with its inherent structure for optimal results. Your Specific Needs Consider your unique requirements when selecting a sorting algorithm. Some situations demand stability, ensuring that the order of equal elements remains unchanged. In contrast, others may allow modifications to the input array during the sorting process. Factor in these considerations based on the context of your application. If stability is crucial, algorithms like merge or bubble sort may be more suitable. At the same time, situations permitting array modifications may open doors to more aggressive and efficient options like quicksort. What are the Challenges Faced in sorting program in data structure? Challenge Description Time Complexity Sorting algorithms grapple with time complexity, contingent on the dataset’s size and organization. The larger the dataset, the more time it may take for sorting algorithms to complete their task, posing challenges, especially with substantial datasets. Memory Complexity Memory usage becomes critical as sorting algorithms may demand significant memory resources. The challenge intensifies with larger datasets, where the memory required for sorting might surpass the available resources, leading to potential bottlenecks. Computational Complexity Certain sorting algorithms exhibit intricate computational complexities, rendering them challenging to comprehend, implement, and debug. The complexity of these algorithms can hinder seamless integration into applications and necessitate careful consideration during selection. Data Representation Efficient sorting hinges on the apt representation of the data being sorted. Different types of data may necessitate diverse sorting algorithms to ensure efficiency. Adapting the sorting approach to the data’s nature is pivotal for achieving optimal performance in the sorting process. Performance The efficiency of sorting algorithms is contingent on the dataset’s size, with some algorithms outperforming others in specific scenarios. Selecting the appropriate sorting algorithm is paramount to achieving efficient sorting, ensuring optimal performance tailored to the dataset’s characteristics. What are the Applications of Sorting in Data Structure? Sorting within data structures is a cornerstone in diverse domains, pivotal in optimizing data organization and elevating algorithmic efficiency. Let’s explore some key applications where sorting proves to be indispensable. Databases: Enhancing Search Operations In databases, sorting emerges as a linchpin for optimizing search operations. The creation of indexes utilizing sorted keys is a common practice. This strategic utilization of sorting facilitates rapid data retrieval and expedites query processing. Maintaining an organized structure, databases streamline access to specific information, enhancing overall system performance. Search Algorithms: Boosting Efficiency in Retrieval Sorting takes center stage in many search algorithms, with notable examples like binary search relying on the efficiency of sorted data. Sorting significantly reduces the time required to pinpoint specific elements within vast datasets. As a result, search algorithms operating on sorted data contribute to quicker and more resource-efficient information retrieval. Information Retrieval: Accelerating Search Engines and Systems In search engines and information retrieval systems, sorting emerges as a catalyst for accelerated performance. By leveraging sorting, these systems swiftly retrieve relevant documents, websites, or information based on user queries. The organized structure facilitated by sorting ensures that pertinent results are efficiently surfaced, enhancing user experience and system responsiveness. Data Analysis: Unveiling Insights and Patterns Sorting plays a pivotal role in data analysis, acting as a catalyst for identifying patterns, trends, and outliers within datasets. In fields such as statistical analysis and financial modeling, where data-driven insights are paramount, sorting becomes crucial. The organized arrangement of data simplifies extracting meaningful information, contributing to more informed decision-making. What is the Scope of Sorting Technique in Data Structure? Types of sorting in data structure techniques are integral to algorithms, offering efficient data storage and retrieval solutions. The choice of sorting method depends on factors such as dataset size and the nature of the data being organized. Each technique like big data pipeline tools, big data practice projects, and big data project architecture carries distinct advantages and drawbacks, emphasizing the importance of choosing the perfect approach for a given task. Sorting Technique Scope Quick Sort external sorting in data structure is ideal for efficiently sorting large datasets, employing a divide-and-conquer strategy for swift results. Bubble Sort While simple to comprehend and implement, it is better suited for smaller datasets due to its inefficiency with larger ones. Merge Sort Embraces a divide-and-conquer approach, proving efficient for larger datasets where scalability is paramount. Insertion Sort Apt for small datasets, functioning by inserting each element into its correct position with a straightforward implementation. Selection Sort Well-suited for larger datasets, it selects and places the smallest or largest element at the array’s beginning. Heap Sort Leverages the heap data structure, demonstrating efficiency with large datasets through a systematic sorting process. Radix Sort Grouping elements into buckets based on numerical value excels in efficiently sorting large datasets. Bucket Sort Dividing elements into buckets and sorting each bucket is effective for efficiently handling large datasets. Conclusion That wraps up sorting in data structure and the most common sorting algorithms. You can choose any of the different types of sorting algorithms. However, remember that some of these can be a little tedious to write the program for. But then, they might come in handy for quick results. On the other hand, if you want to sort large datasets, you must choose the bubble sort. Not only does it yield accurate results, but is also easy to implement. Then again, it is slower than the other types. I hope you liked the article about sorting in data structure.  To gain more insights into how sorting works, reach out to us and we will help you get started on the course that best suits your needs! If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Program 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. Have fun coding!

by Rohit Sharma

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19 Feb 2024

Data Science Vs Data Analytics: Difference Between Data Science and Data Analytics
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Data Science Vs Data Analytics: Difference Between Data Science and Data Analytics

Summary: In this article, you will learn, Difference between Data Science and Data Analytics Job roles Skills Career perspectives Which one is right for you. Read more to know each in detail. The upsurge of Big Data has brought along two other buzzwords in the industry, Data Science and Data Analytics. Today, the whole world contributes to massive data growth in colossal volumes, hence the name, Big Data. The World Economic Forum states that by the end of 2020, the daily global data generation will reach 44 zettabytes. By 2025, this number will reach 463 exabytes of data! Big Data includes everything – texts, emails, tweets, user searches (on search engines), social media chatter, data generated from IoT and connected devices – basically, everything we do online. The data generated every day via the digital world is so vast and complex that traditional data processing and analysis systems cannot handle it. Enter Data Science and Data Analytics.  Since Big Data, Data Science, and Data Analytics are emerging technologies (they’re still evolving), we often use Data Science and Data Analytics interchangeably. The confusion primarily arises from the fact that both Data Scientists and Data Analysts work with Big Data. Even so, the difference between Data Analyst and Data Scientist is stark, fuelling the Data Science vs. Data Analytics debate.  In this article, we’ll address the Data Science vs. Data Analytics debate, focusing on the difference between the Data Analyst and Data Scientist. Our learners also read: Learn Python Online Course Free  Data Analytics vs Data Science: Two sides of the same coin Data Science and Data Analytics deal with Big Data, each taking a unique approach. Data Science is an umbrella that encompasses Data Analytics. Data Science is a combination of multiple disciplines – Mathematics, Statistics, Computer Science, Information Science, Machine Learning, and Artificial Intelligence. This makes for data science and data analytics difference. It includes concepts like data mining, data inference, predictive modeling, and ML algorithm development, to extract patterns from complex datasets and transform them into actionable business strategies. On the other hand, data analytics is mainly concerned with Statistics, Mathematics, and Statistical Analysis.  Also read: Free data structures and algorithm course! While Data Science focuses on finding meaningful correlations between large datasets, Data Analytics is designed to uncover the specifics of extracted insights. In other words, Data Analytics is a branch of Data Science that focuses on more specific answers to the questions that Data Science brings forth.  Data Science seeks to discover new and unique questions that can drive business innovation. In contrast, Data Analysis aims to find solutions to these questions and determine how they can be implemented within an organization to foster data-driven innovation.  Read: Career in data science and its Scope. upGrad’s Exclusive Data Science Webinar for you – How upGrad helps for your Data Science Career? document.createElement('video'); https://cdn.upgrad.com/blog/alumni-talk-on-ds.mp4 Data Science vs. Data Analytics: Job roles of Data Scientist and Data Analyst Data Scientists and Data Analysts utilize data in different ways. Data Scientists use a combination of Mathematical, Statistical, and Machine Learning techniques to clean, process, and interpret data to extract insights from it. They design advanced data modeling processes using prototypes, ML algorithms, predictive models, and custom analysis.  While data analysts examine data sets to identify trends and draw conclusions, Data Analysts collect large volumes of data, organize it, and analyze it to identify relevant patterns. After the analysis part is done, they strive to present their findings through data visualization methods like charts, graphs, etc. Thus, Data Analysts transform the complex insights into business-savvy language that both technical and non-technical members of an organization can understand. This is another difference between data analysis and data science. Both the roles perform varying degrees of data collection, cleaning, and analysis to gain actionable insights for data-driven decision making. Hence, the responsibilities of Data Scientists and Data Analysts often overlap. , and people often find themselves wondering Is data science and data analytics same?.  Responsibilities of Data Scientists To process, clean, and validate the integrity of data. To perform Exploratory Data Analysis on large datasets.  To perform data mining by creating ETL pipelines.  To perform statistical analysis using ML algorithms like logistic regression, KNN, Random Forest, Decision Trees, etc. To write code for automation and build resourceful ML libraries.  To glean business insights using ML tools and algorithms. To identify new trends in data for making business predictions. Must read: Learn excel online free! Responsibilities of Data Analysts To collect and interpret data. To identify relevant patterns in a dataset.  To perform data querying using SQL. To experiment with different analytical tools like predictive analytics, prescriptive analytics, descriptive analytics, and diagnostic analytics. To use data visualization tools like Tableau, IBM Cognos Analytics, etc., for presenting the extracted information.  Read: Career in Data Science Data Science vs. Data Analytics: Core Skills  Data Scientists must be proficient in Mathematics and statistics and expertise in programming (Python, R, SQL), Predictive Modelling, and Machine Learning. Data Analysts must be skilled in data mining, data modeling, data warehousing, data analysis, statistical analysis, and database management & visualization. Data Scientists and Data Analysts must be excellent problem solvers and critical thinkers. This is another data analytics and data science difference. A Data Analyst must be: Well-versed in Excel and SQL database. Proficient in using tools like SAS, Tableau, Power BI, to name a few.  Proficient in R or Python programming.  Adept in data visualization. A Data Scientist must be: Well-versed in Probability & Statistics and Multivariate Calculus & Linear Algebra.  Proficient in programming in R, Python, Java, Scala, Julia, SQL, and MATLAB. Adept in database management, data wrangling, and Machine Learning. Experienced in using Big Data platforms like Apache Spark, Hadoop, etc. Checkout: Data Science Skills 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 Data Science vs. Data Analytics: Career Perspective  The career pathway for Data Science and Data Analytics is quite similar. Data Science aspirants must have a strong educational foundation in Computer Science, or Software Engineering, or Data Science. Similarly, Data Analysts can pursue an undergraduate degree in Computer Science, or Information Technology, or Mathematics, or Statistics. 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 The Data Analysis Process The journey of data analysis encompasses several key steps: Data Requirement Gathering: This initial phase of data analysis involves a meticulous understanding of why the analysis is being conducted and what specific types of data are required to achieve the intended goals. It’s akin to creating a roadmap before embarking on a journey. This step involves delineating the purpose behind the analysis, defining the scope, and identifying the various categories or forms of data that will be most beneficial for the analysis process. Whether it’s sales figures, customer demographics, or product performance metrics, this stage sets the foundation for acquiring the right data that aligns with the objectives. Data Collection: Once the blueprint is set, the next pivotal stage is data collection. This process involves sourcing the identified data from diverse channels and platforms. It includes a range of methods such as surveys, interviews, database extraction, web scraping, or utilizing APIs to gather relevant datasets. Here, the emphasis lies not only on obtaining data but also on ensuring its quality, relevance, and accuracy. Data collected should ideally cover all necessary aspects outlined in the requirement-gathering phase and should be organized in a manner conducive to subsequent analysis. Data Cleaning: Raw data is often riddled with imperfections and inconsistencies. Data cleaning, also known as data cleansing or data scrubbing, is the process of refining the collected data to ensure its accuracy and consistency. This phase involves identifying and rectifying errors, removing duplicate entries, dealing with missing or irrelevant data points, standardizing formats, and addressing any other anomalies that might distort or hinder the analysis process. The goal is to prepare a clean and robust dataset, free from redundancies or inaccuracies, forming a solid foundation for subsequent analysis steps. Data Analysis: This phase involves leveraging a myriad of tools and software specifically designed for interpreting and comprehending the collected datasets. Utilizing tools such as Excel, Python, R, Tableau, or Power BI, data analysts dig into the dataset, applying statistical, mathematical, or computational techniques to uncover patterns, trends, correlations, or anomalies within the data. The goal is to extract meaningful insights that support decision-making processes and help achieve the predefined objectives outlined during the initial stages. Data Interpretation: Once the data is rigorously analyzed, the next critical step is interpreting the findings. Data interpretation involves making sense of the analyzed information, drawing logical conclusions, and deriving actionable insights from the patterns or trends identified during the analysis phase. Analysts use their expertise to decipher what the data signifies, translating it into meaningful recommendations or strategies that businesses or stakeholders can implement to address challenges or capitalize on opportunities. Data Visualization: Communicating complex data insights in an understandable and compelling manner is the essence of data visualization. This process entails transforming the analyzed data into visual representations like charts, graphs, maps, or infographics. These visuals serve as powerful tools for elucidating intricate patterns or trends, making it easier for stakeholders or decision-makers to grasp the insights swiftly. Through visualization, data analysts present compelling narratives and compellingly showcase the significance of the analyzed data, enhancing its impact and aiding in effective decision-making processes. Data Science vs. Data Analytics: Which One is Right For You? Typically, Data scientists are much more technical, requiring a mathematical mindset, and Data Analysts take on a statistical and analytical approach. From a career perspective, the role of a Data Analyst is more of an entry-level position. Aspirants with a strong background in statistics and programming can bag Data Analyst jobs in companies. Usually, when hiring Data Analysts, recruiters prefer candidates who have 2-5 years of industry experience. On the contrary, Data Scientists are seasoned experts having more than ten years of experience.  When talking about the salary, both Data Science and Data Analytics pay extremely well. The average salary of Data Scientists in India ranges between Rs. 8,13,500 – 9,00,000, while that of a Data Analyst is Rs. 4,24,400 – 5,04,000. And the best part about choosing to build a career in Data Science or Data Analytics is that their career trajectory is positive, continually scaling up. Read more on data scientist salary in India. Difference between data science and data analytics in tabular form – Data Science Data Analytics Skillsets include- Data Modelling Predictive Analytics Advanced Statistics Engineering Programming Skillsets include- BI Tools Statistics SQL Programming  The scope is macro. The scope is micro. The goals are to discover new questions and their answers. The goals are to use the existing information and take appropriate steps. Why is data science important? The primary data science vs data analytics difference would is that data is the new oil used in almost every industry today. The data is meaningless unless it is made meaningful. Data science functions to mine a huge amount of data containing all types of data, from unstructured to structured data. The importance of data lies in its ability to make sense of the information given. Well-put data allows the users to identify and make the best out of the available information; this is another significant difference between data analytics and data science. Data science allows the business to measure, track, and record the metrics and development in the process. This process enables individuals to make practical and rational decisions about their business. This is also a  difference between data science and data analytics.  The organisational importance of data science is ever-increasing. There are various applications of data science, and it is relevant in almost every industry today, from healthcare, finance, banking, retail, entertainment, education and so on. 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? Here are the differences between data science and data analytics. To conclude, even though Data Science and Data Analytics tread on similar lines, here’s a fair share of differences between Data Analyst and Data Scientist job roles. And the choice between these two largely depends on your interests and career goals. The key industries where data science and data analytics or data analysis vs data science find significant application: Data science vs data analytics are important across various industries, playing a pivotal role in leveraging insights from data to drive decision-making and innovation. Some key industries where data science vs data analytics fields find substantial application include Healthcare: From patient diagnostics to personalized treatment plans, data science aids in analyzing medical records, imaging data, and genomic sequences, while data analytics assists in optimizing hospital operations and resource allocation. Finance: Both data science and analytics are extensively used in the banking and financial sectors for fraud detection, risk assessment, algorithmic trading, and customer segmentation, enabling better financial decision-making. Retail: Data science helps in forecasting demands, optimizing inventory, and personalized customer experiences, while data analytics aids in market segmentation and analyzing customer purchasing patterns. Telecommunications: Both data science and analytics is utilized for network optimization, predictive maintenance, and customer churn prediction improving service quality and understanding user behavior. Marketing and Advertising: Both data science and data analytics contribute significantly by analyzing customer preferences, optimizing ad campaigns, and delivering personalized marketing strategies based on data-driven insights. Manufacturing and Supply Chain: Data science optimizes production processes, predictive maintenance, and supply chain management, while data analytics aids in inventory optimization and improving operational efficiency. Here are the difference between data science and data analytics with example. To conclude, even though data science and business analytics tread on similar lines, here’s a fair share of differences between Data Analyst and Data Scientist job roles. And the choice between these two largely depends on your interests and career goals. If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s Executive PG Programme in Data Science.

by Rohit Sharma

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19 Feb 2024

Top 21 Python Developer Skills You Must Need To Become a Successful Python Developer
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Top 21 Python Developer Skills You Must Need To Become a Successful Python Developer

Its intuitive syntax, extensive libraries and versatile integration capabilities have fueled incredible growth across web development and scientific computing sectors. Python’s “batteries included” ecosystem allows python developers skills to enhance to achieve in hours what takes days in other languages, boosting productivity tremendously with python skills. Yet more than raw efficiency, Python promotes cleaner code organisation by emphasising modular design, object-oriented principles and exception-based error handling. These best practices scale resiliently to support complex applications involving hundreds of modules and millions of lines without becoming tangled chaos behind the scenes. The resulting code proves straightforward to write and, crucially, to maintain amid changing product requirements for python backend developer skills. There are many skills one needs to learn to become a good Python Developer. Here, we are going to discuss top Python developer skills that one will require to be a Better Python developer. Its intuitive syntax, extensive libraries and versatile integration capabilities have fueled incredible growth across web development and scientific computing sectors. Python’s “batteries included” ecosystem allows developers to achieve in hours what takes days in other languages, boosting productivity tremendously. Yet more than raw efficiency, Python promotes cleaner code organisation by emphasising modular design, object-oriented principles and exception-based error handling. These best practices scale resiliently to support complex applications involving hundreds of modules and millions of lines without becoming tangled chaos behind the scenes. The resulting code proves straightforward to write and, crucially, to maintain amid changing product requirements. There are many skills one needs to learn to become a good Python Developer. Here, we are going to discuss top Python developer skills that one will require to be a Better Python developer. What are the benefits of learning Python? Python has been the most popular coding language for several years, which means that career advancement opportunities for Python developers are excellent. Many giant firms, including Pinterest, Instagram, Yahoo!, and Nokia, use Python. Python is preferred by more than 68% of Data Scientists. Python has numerous advantages, which are discussed further below.    Easy to learn and understand Despite the fact that Python is a high-level language that allows you to perform complex tasks, it is simple to learn and understand. It has a simple syntax and is appropriate for both novice and experienced programmers. Python can appear difficult at first if you are a complete beginner. This language is used by large development teams with inexperienced members to improve output.     Python is Adaptable It is used in Web Development, Data Mining, Data Science, Artificial Intelligence, Machine Learning, Gaming, Product Development, Rapid Application Development, Testing, Automation, and other areas. Python is becoming increasingly popular as the primary language for many applications.     Python is popular and offers a high salary Python is a popular programming language that is constantly growing in popularity. As Python gains popularity, so does the number of skilled Python developers. Python training can help you break into a world of hugely profitable career opportunities. Python developers are among the highest-paid in the market, especially in data science, machine learning, and web development.     Open-Source Language Python is a community-developed open-source language with a large support base. Having Python source code freely available enables global collaboration within the dev community. Anyone can contribute improvements and new features or find issues transparently. There are also no licensing fees, reducing barriers to usage. The open ecosystem allows Python capabilities to advance rapidly through decentralised enhancement, with questions like what are the skills required for python developer.     Has regular updates Python, like any other programming language, regularly receives additions and updates to improve its functionality and keep up with evolving development standards. Leveraging contributions from its widespread community, Python sees regular updates that continue fortifying it as an optimal programming language. Each new release provides access to the latest productivity features, security fixes, and compatibility improvements are some skills for python developer. The frequent refresh cycle ensures Python remains cutting-edge. Leveraging contributions from its widespread community, Python sees regular updates that continue fortifying it as an optimal programming language. Each new release provides access to the latest productivity features, security fixes, and compatibility improvements. The frequent refresh cycle ensures Python remains cutting-edge.     Minimal Program Management Python’s excellent readability and simple syntax make it easy for beginners to learn and use the programming language. It also has a large user base, which results in a diverse set of internet resources. This improves language development and allows for minimal program management. Because Python has a gentle learning curve, developers can focus more on solving programming problems versus wrestling with stubborn, complicated syntax rules especially when skills required for python developer. Moreover, Python has some complex features under the hood. But most of those are abstracted away when you’re first starting out. So newbies,with  python developer required skills can build up a good foundation in core programming concepts quickly is role of backend python developer skills.     Massive support libraries Python has a vast standard library that includes a wide range of topics. Since there are numerous libraries of already scripted commonly used tasks, it greatly reduces the time to write and the length of the code. Some basic python interview questions are about how it works. To this, answer, reinventing the wheel is an inefficient use of development time and resources. Thankfully, Python’s expansive set of curated modules covers a remarkable range of domains to incorporate directly, from web frameworks to mathematical computations with various python developer skills required. These libraries also possess thoughtful documentation, enabling relatively smooth usage and customization which is often considered python programming for network engineers, role by python full stack developer skill.     Increase in Productivity Because Python does not require compilation, the test-debug cycle is extremely fast. A segmentation fault is never caused by a bad input or a bug; rather, it raises the exception. Python’s beginner-friendly syntax, dynamic typing and lightweight code reduce complexity substantially over other languages. Developers can implement rich functionality in far fewer lines of straightforward Python versus convoluted equivalents. Removing that excess coding friction enables the delivery of projects to be faster.     Third-Party Modules The Python Package Index contains a large number of third-party modules, allowing it to converse with the majority of other platforms and programming languages. Thanks to Python’s versatility, over 150,000 third-party libraries and counting are available to incorporate. Instead of reinventing existing functionality, developers can install battle-tested modules for needs like data science, web frameworks and machine learning. Standing on the shoulders of open-source Giants saves immense development time.   Enhancing your resume Being familiar with Python and being a certified Python programmer is not the same thing. As a Python-certified programmer, you will be able to add more value to your resume and make a stronger impression on recruiters. Formal Python certification demonstrates deeper hands-on mastery versus just claiming “knowledge” of the language. Backing skills with verified credentials builds trust and confidence during the hiring process. Additionally, the journey to gaining reputable Python certification itself further solidifies the breadth of expertise.   Change your career path Python developer skills and a  Python certification, you will find it easier to transition because it demonstrates that you have the necessary knowledge to handle a variety of projects. With Python usage continuing to surge in fields ranging from backend services to data analysis, Python skills present growing career opportunities. Organisations urgently seek Python expertise, given its balance of human-friendliness and scalability. Learning Python unlocks income potential and flexibility to shift across high-demand industries. Check out all trending Python tutorial concepts in 2024 Top Python Developer Skills 1. Expertise in Core Python The developer should have an excellent knowledge of Core Python. One should know the concepts of core python and its aspects are: Iterators Generators Exception Handling File handling concepts Data types and variables OOPs Concepts Data Structures Mastering this core foundation establishes a solid technical aptitude to then expand into specialised domains. Web developers might explore GUI programming next or DevOps roles could target multi-threading. Data scientists may focus on speed optimisations. However, everyone benefits from excellence first with fundamentals like control flow, modularity, classes, and typing. There is no substitute for dedication to perfecting the basics through hands-on projects before layering on more advanced capabilities and knowing python programming skill, of python programming datacamp. 2. Sound Knowledge of Web Frameworks Flask and Django are the most popular Python web framework, and a developer should have an excellent grasp of either of these web frameworks. Flask is a Python micro web framework which is very widely used. Django encourages a practical, clean and functional design and also a high-level Python Web Framework. Knowledge of JavaScript, CSS, and HTML is also beneficial to become a Good Python Developer. Both eliminate mundane HTTP handling, templating and routing tasks so developers focus on an application’s unique logic. They support vital needs like user authentication, database integrations, and out-of-the-box session management. Unit testing and deploying applications comes smoothly as well. Strong backend skills naturally pair with sufficient font-end mastery, too – JavaScript, CSS and HTML fundamentals for responsive, dynamic UIs. Modern JavaScript frameworks like React may incorporate advanced features like real-time updated. 3. Object Relational Mappers An object-oriented Programming language is used for the conversion of data between two systems that are incompatible, and that technique is called Object Relational Mapper (ORM). From any programming language, a virtual object database is created by Object Relational Mappers. Programmers also use many customizable ORM tools. In Python, SQLAlchemy is the most comprehensive ORM option. It supports PostgreSQL, MySQL, Oracle and MSSQL through a unified interface while retaining vast SQL control if needed. But Django and other frameworks have lightweight ORM modules as well. Good Python developers are adept both utilising ORMs for cleaner data access and understanding how they simplify SQL use cases like joins, aggregations and connection management behind the scenes. ORMs promote code reuse since common record operations get implemented only once. They also enable switching database systems more easily by abstracting storage details with issues of programming and problem solving with python. 4. Skills of Data Scientists A Python developer can grow very much in the field of Data Science by having a good grasp in the popular aspect of Data Science such as: Good Knowledge of SQL Knowledge to use Python packages such as Scikit learn, MatPlotib, NumPy, etc. for Data Science Cleaning up of Data Data Wrangling Analysis of Data Visualization of Data Mathematical Knowledge Knowledge of Probability Knowledge of Statistics Finding a best data science course will help you learn all the important topics and make you ready for your dream job. 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 upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Beyond foundational skills, one must stay atop bleeding edge data science advancements to excel. Continued learning in domains like NLP augments abilities, as do adjacencies like cloud infrastructure management. Soft skills even emerge for collaborating cross-functionally conveying metrics or models to non-technical stakeholders. Moreover, mastering full spectrum data capabilities – from scratch preprocessing to bleeding-edge ML algorithm customisation – establishes expertise beyond the transient popularity of singular techniques. There will always be new nuances to incorporate. It comes with the intrinsically fast-moving territory of data science. 5. Artificial Intelligence and Machine Learning Skill A developer in Data Science should have good knowledge about Artificial Intelligence and Machine Learning as it comes under Data Science. One should have good proficiency in Machine Learning algorithms. He should have a good understanding of Neural Networks, Produce insight from data, visualization of data, Data analysis, and collection of data from various sources. Must read: Excel online course free! From initially exploring popular ML packages like TensorFlow, PyTorch and Keras, professionals gradually cement fluency in applying ML-powered decision-making to products. APis democratise access to advanced ML models. Adding skills in data visualization, pipeline automation, and metrics-driven fine-tuning establishes full-stack ML abilities. Moreover, having baseline ML-powered feature knowledge despite focusing on particular domains allows more open collaboration as the practice permeates industries. One day, AI transformation may sneak into initially unrelated roles. 6. Deep Learning Deep Learning is also a field that comes under Data Science. One is expected to have a good understanding of the concepts in deep learning and Neural network architecture. This is after covering all the aspects of Artificial Intelligence, Machine Learning, and the Basics of Data Science. There are many aspects of Deep Learning, such as: Audio Processing Video Processing NLP ( Natural Language Processing) Our learners also read – python online course free! 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? 7. Good Understanding of Multi-Process Architecture A developer should know the codes that are used in the working of release and deployment. The design engineer usually does this, but a developer too must have a basic understanding. If one wants to become a good Python Developer, then they should learn about MVT (Model View Template) and MVC (Model View Controller) Architecture. A developer will be able to solve the problem in the core framework after understanding the multi-process architecture. 8. Analytical Skills One must have excellent analytical skills in the field of Data Science to be a good python developer in the future. The required analytical skills may need a good understanding of building useful websites for web development, visualizing datasets for Data Science in a better way, optimization of algorithms while coding, writing clean code which is not redundant, etc. 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 9. Design Skills A good developer should also have a good understanding of designs which will be helpful while designing scalable products. Servers should be implemented to make them highly available. A python developer can use any python framework like Flask or Django to create a website. In this way, Python will be able to work at both server-side and client-side programming. Must read: Data structures and algorithm free! 10. Communication Skills Communication skill is one of the essential skills which one should have in any profession. Successful communication can bring success to the professional and the organization. One must be able to avoid any communication gap or confusion in understanding or any kind of misunderstanding. Half of the job is already done when one communicates do peer code review and make a contribution within the team in an efficient manner. Communication is also needed to solve any problem and to communicate with teammates. 11. Version Control Changes in the source code are multiple times, and one should be able to keep track of every change. It is one of the vital job requirements expected by any company. If a developer has correctly set up their GitHub, and they are already familiar with the words like “commit, pull, fork, push”, then they will be efficiently handling different versions of codings and track the changes in the codes. 12. Front-End Technologies Knowledge A Python developer will need to work and coordinate with the front-end team to match the client-side with the server-side. There should be a good understanding of front-end technologies to a developer so that they can know about the appearance of the application, working of front-end, possibilities, and limitations of the front-end. For a Python Developer, the experience and knowledge of front-end will be helpful in some of the main projects. 13. The Ability of Integration To enhance your Python Developer skills, you must be able to integrate multiple databases and data sources into a single system. This integration will help in the smooth running of the system, and there will be fewer discrepancies. For example, Python might populate analytical models by aggregating social media metrics, real-time server logs and historical records in a lake. Clean pipelines simplify gleaning insights across silos. Or a CRUD backend centralises multiple specialty databases under a common OData interface for mobile sync. Even on frontend systems, Python plays integration glue – whether rendering React server views or embedding MATLAB visualisations into dashboards. At scale, teams require hybrid technology blending. 14. Knowledge of Server-Side Templating Language A python developer also needs a good understanding of modelling language at the server-side such as Make, Jinja 2, etc. Both render templates filled with backend values into final documents sent to browsers. Additional engines add niche functionality like internationalization or async rendering as needed. Master templaters design with separation of concerns in mind – HTML/CSS static assets focus purely on structure/presentation while Python views handle model preparation. Template inheritance reduces duplication through reuse while template logic improves responsiveness by skipping unnecessary processing. Configuration not code prevails. 15. Knowledge of User Authorization and Authentication A developer in Python should also know about the user authorization and authentication that happens between multiple environments, servers, and systems. Developers leverage industry-standard protocols like OAuth and cryptographic best practices to fulfil these needs reliably. For example, JSON Web Tokens allow stateless user validation by embedding identity claims directly into tamper-proof tokens carried in requests. Hashing and salting techniques now prevent exposing passwords, even in breaches. Role-based access systems grant precise access cryptographic configurations that balance security with overhead through digest algorithms like BCrypt tailored for passwords. Together, these techniques assemble comprehensive identity frameworks managing users from initial access through ongoing session validation checks. 16. Python Event-Driven programming A developer in Python should be familiar with programming in Python that is event-driven. Rather than traditional synchronous code executing sequentially, event-driven systems react dynamically to notifications from external sources. These triggers invoke event handler callbacks to process notifications as needed. For example, a web server may spawn a thread per incoming request while queueing a backend event for each. Separate database logic processes data asynchronously while serving pages and API responses fully. By decoupling event handling from primary logic, overall throughput improves dramatically. Python frameworks like asyncio (standard) and Twisted (third party) encourage this non-blocking I/O approach for network programming especially. 17. Good Debugging and Unit Test Skills A developer should have strong skills in debugging and unit testing. Debugging involves systematically isolating, diagnosing and addressing unexpected faults or unintended behaviour in applications. Strategies here run the gamut from inserting print statements to monitoring metrics dashboards. Unit testing establishes repeatable validation suites protecting against regressions as code evolves. Tests confirm components operate correctly in isolation and help clarify assumptions early. Frameworks like PyTest encourage test-driven designs validated incrementally as complexity increases. Together, debugging prowess and unit testing expertise afford resilience no tutorial or documentation can replace. 18. Code Versioning Tool Understanding A developer can also have a good understanding of the code versioning tools like SVN, Mercurial, and Git. Git simplifies creating experimental branches, merging contributions from peers, resolving conflicts intelligently and rolling back problematic updates. Workflow automation around testing, validation checks and deployments further increases productivity by setting policy safeguards. Built-in facilitation guides non-technical stakeholders, also contributing through intuitive interfaces, expanding engagement. Understanding Git dynamics builds awareness into often opaque software lifecycles. 19. Database Schemas Creation Ability If a developer can create schemas in the database, then he can support business processes and represent it. Developers should grasp relationships, normalization rules, indexing strategies and other model optimization considerations. Schemas provide the backbone connecting object code to persistent storage. Careful column type selection preserves data integrity, while foreign keys and joins handle complex interconnections. Schema migration allows graceful evolutionary enhancements as well. Excellent Python developers possess mental maps from common access patterns to ideal table layouts, index choices and even database engine strengths or weaknesses based on unique workloads. 20. Multiple Delivery Platforms Understanding There are many platforms to deliver an output such as desktop or mobile, and a python developer should have good knowledge of the difference between different platforms. He should also be able to optimize the output according to the target platform to match the requirements. 21. Logical Thinking Ability In any case, the Python developer is required to think logically and the ability of good logical thinking should be the second nature of a developer to become a good developer in the python programming language. Conclusion If you are reading this article, most likely you have ambitions towards becoming a Python developer. Here we have discussed the top skills required by a Python developer and we hope this article has been informative to you. If you’re interested to learn python & want to get your hands dirty on various tools and libraries, check out Executive PG Programme in Data Science.

by Rohit Sharma

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19 Feb 2024

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