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- Types of Graphs in Data Structure & Applications
Types of Graphs in Data Structure & Applications
Updated on 04 March, 2024
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Table of Contents
Introduction
A graph is a non-linear structure comprising nodes and edges. It can include a finite or infinite set of nodes held by an edge connecting a pair of nodes. Data structures are an essential part of any coding concept; thus, having a firm grasp of different types of graphs in data structures can assist you through complex real-world problems.
In today’s world, data is power. Thus, organizing data efficiently for easy access is essential for any programmer. Knowledge of data structures and its variety of graphs empowers your coding skills to target real-world problems and effectively deliver its solutions.
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Let’s take a look at different types of graphs commonly used in data structures and how they are applied in real life.
Types of Graphs in Data Structures
A data structure is a practical data storage standard for all languages, such as python graph data structure or java graph data structure. Mastering all types of graphs should be a priority for anyone aspiring to study data structures. Since graph theory has many real-life applications, they become vital in data structures.
The various types of graphs in Data structures can be listed below:
1. Null Graph
As the name suggests, the null graph is empty; in other words, it is a graph with no edges. It only consists of isolated vertices in the graph with a vacant edge set.
2. Finite Graph
If the number of edges and nodes consists of a finite number in a graph, then the graph is known as a finite graph.
3. Infinite Graph
If one cannot put a finite number to the number of nodes and the number of edges in a graph, the graph is known as an infinite graph. Infinite graphs are uncountable, which means you cannot count the number of nodes or edges in this type of graph.
4. Simple Graph
A graph is said to be simple when there is only one edge between a pair of vertices. Thus, two nodes are connected by one edge in a graph, which can identify a definite relationship between them.
5. Multi Graph
If a pair of nodes is connected with multiple edges in a graph, then the graph is known as a multi-graph. A multi-graph does not consist of self-loops. There are two kinds of edges that can exist in a multi-graph. They are:
Parallel Edges
Edges that run parallel, like two parallel roads going from one source to the same destination, are known as parallel edges.
Loop
This is an edge whose source and destination vertices are the same.
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6. Directed Graph
A graph is said to be directed if all the edges present between two nodes or vertices have a defined direction. A directed graph is also known as a digraph. We can determine the starting and ending node by looking at a directed graph. Remember, all the edges in a directed graph must be directed to be called a directed graph.
7. Non-Directed Graph
A graph is said to be a non-directed graph if it is hard to identify the starting and ending node by looking at its edges. Just like a directed graph, the edges have to be non-directed for it to be called a non-directed graph.
8. Connected Graph
A connected graph is a graph where at least one path exists between all nodes. In simpler terms, if you start from a node in a connected graph, you should be able to visit every node present in the graph. Hence, there should be at least one path for every node.
9. Disconnected Graph
In this type of graph, no edge exists between a pair of nodes or vertices. Thus, unlike connected graphs, reaching all the nodes from just any vertice is not possible. If any pair of vertices do not have a path between them, it is called a disconnected graph.
10. Complete Graph
A graph is only considered complete when an edge exists between every node, meaning an edge will connect all the vertices in the graph. A complete graph on n vertices is denoted as Kn, and the number of edges in the graph is nC2.
11. Cyclic Graph
A graph should have at least one cyclic component to be considered a cyclic graph. On the contrary, if the graph does not contain any cycle, it is considered an acyclic graph.
12. Regular Graph
In a regular graph, all the vertices should have the same degree. The degree of a node can be defined as the number of nodes connected with it. Thus, in a regular graph, all the nodes should be connected to the same number of nodes.
13. Bipartite Graph
For a graph to be bipartite, it should satisfy the following criteria.
- The graph should be divided into sets of vertices.
- Edges should only form between one group of nodes to the other side. This rule prevents the connection between two vertices of the same set of nodes.
- The two groups should not have any common vertices between them.
A graph following all the above rules should be considered a bipartite graph.
14. Labeled Graph
The edges in graphs can be weighted. A weight associated with an edge can be understood as the cost of traveling through that edge. These values can be based on a fixed parameter and can change between graphs. Now, if all the edges hold some weight associated with them, then that graph can be termed a labeled graph.
15. Directed Acyclic Graph
A directed acyclic graph is a combination of directed and acyclic graphs where the directed edges of the graph do not make any form of cycle. On the contrary, a directed cyclic graph is a graph with directed edges that forms a cycle.
Application of Graph in Data Structure
The most prominent application of a graph in computer science is the representation of the flow of computation. Some other famous used cases of graphs are:
1. Google Maps
In Google Maps, graph data structures define and compute the transport system. When a road is met with another road and forms a crossing, it is considered a node, and the road between two such nodes is treated as an edge. Hence, Google Maps finds you the shortest and quickest way to your destination using the graph data structure.
2. Facebook
Facebook uses undirected graphs to identify a user and the user’s friends. Every user is treated as the vertices, and the connections joining them as friends are the network’s edges. With algorithms based on graph data structure, Facebook suggests “people you may know” and shows “mutual friends.”
3. World wide web
The World Wide Web is an example of a directed graph. It is also the basic idea behind the Google ranking system. In the World Wide Web system, every website and web app is treated as a node or vertices, and the links from one website to another are considered the edge.
4. Operating System
The operating system is a popularly used case of Resource Allocation Graphs that uses every process and resource as nodes or vertices. Edges occur between resources to the allocated process or from requesting process to requested resources. Sometimes this cycle might form an infinite loop, initializing the deadlock.
5. Mapping System
Your GPS is a popularly used case of graphs to locate nearby restaurants, shops, and places you choose to search with the help of this technology.
6. Microsoft Excel
Directed Acyclic Graphs or DAG is used in Microsoft Excel.
7. The Dijkstra algorithm
The Dijkstra Algorithm uses graph data structure to identify the shortest path between two, or in some cases, more than two nodes.
8. Flight Networks:
Computing optimized flight networks is another real-life application of graph data structure. If you consider the airports as nodes and the routes as the edges, the data perfectly fit the criteria of graphs. That is why with the help of various enhanced algorithms, the best routes between two airports or nodes are determined.
These are the various applications of graphs in the data structure, used across the globe in various applications and systems to organize and maintain their smooth functioning,
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Binomial Theorem: Standard Deviation, Related Terms & Properties
The binomial theorem is one of the most frequently used equations in the field of mathematics and also has a large number of applications in various other fields. Some of the real-world applications of the binomial theorem include:
The distribution of IP Addresses to the computers.
Prediction of various factors related to the economy of the nation.
Weather forecasting.
Architecture.
Binomial theorem, also sometimes known as the binomial expansion, is used in statistics, algebra, probability, and various other mathematics and physics fields. The binomial theorem is denoted by the formula below:
where, n N and x,y R
Source
What is a Binomial Experiment?
The binomial theorem formula is generally used for calculating the probability of the outcome of a binomial experiment. A binomial experiment is an event that can have only two outcomes. For example, predicting rain on a particular day; the result can only be one of the two cases – either it will rain on that day, or it will not rain that day.
Since there are only two fixed outcomes to a situation, it’s referred to as a binomial experiment. You can find lots of examples of binomial experiments in your daily life. Tossing a coin, winning a race, etc. are binomial experiments.
Read: Binomial Distribution in Python with Real-World Examples
What is a Binomial Distribution?
The binomial distribution can be termed to measure probability for something to happen or not happen in a binomial experiment. It is generally represented as:
p: The probability that a particular outcome will happen
n: The number of times we perform the experiment
Here are some examples to help you understand,
If we roll the dice 10 times, then n = 10 and p for 1,2,3,4,5 and 6 will be ⅙.
If we toss a coin for 15 times, then n = 15 and p for heads and tails will be ½.
There are a lot of terms related to the binomial distribution, which can help you find valuable insights about any problem. Let us look at the two main terms, standard deviation and mean of the binomial distribution.
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Standard deviation of a binomial distribution
The standard deviation of a binomial distribution is determined by the formula below:
= npq
Where,
n = Number of trials
p = The probability of successful trial
q = 1-p = The probability of a failed trial
Mean of a binomial distribution
The mean of a binomial distribution is determined by,
= n*p
Where,
n = Number of trials
p = The probability of successful trial
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Introduction to the binomial theorem
The binomial theorem can be seen as a method to expand a finite power expression. There are a few things you need to keep in mind about a binomial expansion:
For an equation (x+y)n the number of terms in this expansion is n+1.
In the binomial expansion, the sum of exponents of both terms is n.
C0n, C1n, C2n, …. is called the binomial coefficients.
The binomial coefficients which are at an equal distance from beginning and end are always equal.
Source
Coefficients of all the terms can be found by looking at Pascal’s Triangle.
Source
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Terms related to binomial theorem
Let us now look at the most frequently used terms with the binomial theorem.
General Term
The general term in the binomial theorem can be referred to as a generic equation for any given term, which will correspond to that specific term if we insert the necessary values in that equation. It is usually represented as Tr+1.
Tr+1=Crn . xn-r . yr
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Middle Term
The middle term of the binomial theorem can be referred to as the middle term’s value in the expansion of the binomial theorem.
If the number of terms in the expansion is even, the (n/2 + 1)th term is the middle term, and if the number of terms in the binomial expansion is odd, then [(n+1)/2]th and [(n+3)/2)th are the middle terms.
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Independent Term
The term which is independent of the variables in the expansion of an expression is called the independent term. The independent term in the expansion of axp + (b/xq)]n is
Tr+1 = nCr an-r br, where r = (np/p+q) , which is an integer.
Properties of Binomial Theorem
C0 + C1 + C2 + … + Cn = 2n
C0 + C2 + C4 + … = C1 + C3 + C5 + … = 2n-1
C0 – C1 + C2 – C3 + … +(−1)n . nCn = 0
nC1 + 2.nC2 + 3.nC3 + … + n.nCn = n.2n-1
C1 − 2C2 + 3C3 − 4C4 + … +(−1)n-1 Cn = 0 for n > 1
C02 + C12 + C22 + …Cn2 = [(2n)!/ (n!)2]
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Conclusion
The binomial theorem is one of the most used formulas used in mathematics. It has one of the most important uses in statistics, which is used to solve problems in data science.
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Data Science Industry Prediction For 2024
We have arrived at a new year—and it’s time to predict the trend in trend! According to data scientists, there will be a massive leap in data science implementation in 2024. Various data science algorithms implemented on massive datasets will make tasks much more permissive.
According to some data science industry predictions, from 2024, data performance with analytics will become even more mission-critical. According to Gartner’s data science industry prediction 2024, CEOs, CIOs, and analytic innovators seem to enhance their strategic plans for more productivity through applied Data Science.
‘Organisations are making tense budget cuts in many areas to overcome the effects of COVID-19 and keep their business viable,’ says Nick Elprin, Co-founder and CEO of Domino Data Labs. He also added, ‘By 2023, we predict that many will provide or enhance their investment in data science to drive the significant business decisions that may make the difference between survival and liquidation.’
Analysing the digital business and its future confronts us with different possibilities of data analytics on different verticals. Data science predictions of 2024 endure diverse transformations and solve challenges that CIOs and data analytics leaders should adopt and introduce in their planning for successful strategies. More the implementation, more job opportunities.
That will also thrive innovations and data science applications on various markets, including retail, healthcare, and manufacturing industries. Let us look at the different verticals that will witness a change as per data science industry prediction 2024.
Data Science Industry Prediction 2024
Businesses have already started democratising data across the organisation and industries while aiming for more employees to extract real-time insights. If there is one good thing that the COVID-19 situation has shown us more vividly, it’s to rely on data more. To get the most out of the generated data, organisations need to spend more on job opportunities, innovations, problem-solving approaches, and employees’ upskilling. Here are some of the verticals that the data science industry prediction is looking forward to witnessing enrichment.
How Many Job Opportunities Will Be There for Data Science Experts?
More than 2,50,000 e-commerce firms exist globally. Therefore, it is evident that these firms will require a large workforce of data analysts and data scientists to analyse enormous amounts of data generated every day. According to the latest survey conducted by Analytics Insight, in 2023, more than 3,037,810 new job openings will spring up. Startups and MNCs are posting job roles for data science experts globally and in the US. It vividly indicates that data is a big hot job openings aggregator.
New Problems that Data Science Will Solve Efficiently
The previous year, it seems like 2023 is a stream of opportunity for tech trends to flourish. According to some predictions, hybrid cloud, intelligent machines, Natural Language Processing (NLP), healthcare systems, manufacturing industries, and other broad niches are grooming their problem-solving approaches through data analytics tools and machine learning models. Here are some of the list of the top trending issues that data science will solve.
o Automation systems and intelligent machines backed up via data science will drive critical roles to automate organizational tasks. It will enhance the Robotic Automation Process (RPA) to bring low-valued efforts and focus on high-value activities. Collecting data and modelling the algorithms to extract intelligence from those data is the target of the firms.
Cloud deployment and usage will fully implement the use of data analytics. As the computation power grows exponentially and data is getting more affordable and easier to access, cloud and serverless technology focus more on computation and the data residing inside for easier deployment and analysis. In 2024, we will also see data scientists focusing on the complex problems of serverless technology and hybrid cloud solving conspicuous difficulties more effectively using data analytics.
NLP models will now be more magnanimous than ever. NLP will be able to synthesize complex problems and large datasets to power human-machine conversations more effectively. In conjunction with data analytics, AI tools and ML models will efficiently leverage various data analytics stages.
Learn data analytics courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
NLP, along with data science algorithms, are attempting to extract clear speech recognition and are also getting implemented in various other native languages. Refined ML algorithms will more efficiently assist language processing steps like sentence synthesizing, word tokenization, predicting part of speech, dependency parsing, named entity recognition, etc.
Innovations in Data Science
Data science is backing Deep learning models for a long time now. According to data science industry prediction 2024, the popularity of large-scale deep learning models will increase. The next-generation smart devices will produce as well as consume sensor data from the Internet of Things.
Organisations are also planning to make intelligent computing to the edge of industry function, allowing devices to operate in almost every industry. Adding intelligence to these sensor systems will also help to interact these machines with humans and among each other without a centralized command and control (C&C). It will surely open new routes of innovation in industries and firms.
Organisations and firms are using data analytics algorithms intensely in the field of media also. Applications like understanding your audience, media crowd, and analysing their tastes help media content creators discover the content their audience will cherish. According to data science predictions, firms will analyse large datasets generated by the audience and their choices to bring new media content on the platform that will surely flourish. It will be possible with the help of data analytics and efficient machine learning models.
Another research is going on with Deep Reinforcement Learning and Transfer Learning to discover new ways of writing efficient algorithms and ML models that are more appropriate, and therefore, more accurate & less biased. Organisations gradually started appreciating the economic value of data science and analytics. According to many firms, digital assets that never wear out become more valuable with time as they are more in use.
Among data science practitioners, in 2024, a large focus will also be on the potentialities of feature engineering, predicts Dr Ryohei Fujimaki, Founder and CEO of dot data. Feature engineering talks about utilising domain knowledge for extracting additional features from unprocessed data through data mining and data analytics. Feature engineering, aka AutoML 2.0, will provide automated hypothesis generations that will explore thousands and millions of hypothesis patterns to automate discovery and engineering with more clarity, transparency, and insights.
Applications of Data Science in Healthcare and Manufacturing Industries
Data science and data analytics are popular in the field of healthcare and manufacturing industries. In the branch of healthcare, organisations use applied data science to predict patient’s health conditions, medical image comprehending, virtual assistance for patients, tracking & understanding the mutation of diseases, and many more.
As per data science industry prediction, by 2024, the healthcare industry will heavily utilise Data Science for understanding the secrets of genetics and extend genomics research. New drug discovery will be there as organisations will use drug composition datasets to simulate their composition through data analytics and ML algorithms. It gives birth to a new branch of medicine called Predictive Medicine that will use predictive analysis to bring more solutions to problems.
Data analytics approaches are also prominent in the manufacturing and retail fields to detect fault prediction and preventive maintenance. Organisations demand forecasting and autonomous inventory management system to understand and forecast complex industrial processes.
Organisations are planning to utilise data science blending machine learning models to optimise product pricing and logistics efficiently. These models and analysis algorithms are entering the next level by 2024 to predict supply chain risk and manage them more accurately automatically.
Why Can’t You Escape Upskilling Yourself?
Regardless of the skills, degree, or experience, there is always a path to pursue Data Science as a career option. As per the data science industry prediction 2024, the US and India are the top two countries to generate demand for more than 50,000 data scientists and over 300,000 data analysts job opportunities.
Skills required to prepare yourself as data analysts are Statistics, programming (using Python or R), Machine Learning, Multivariable Calculus, Data Wrangling, Data visualisation, Data Intuition, and Data Communication. upGrad has an unparalleled collection of data science courses with varying prices and duration.
Executive PG Program in Data Science, IIIT-B
Masters of Science in Data Science
Advanced Certificate in Data Science, IIIT-B
Conclusion
Advanced data analytics, in combination with AI, are turning out to be the fast and efficient mainstream solution for most organisations. To remain competitive in the aggressive market, industry experts predict that enterprises will attempt to adopt advanced analytics and acclimate their business standards by establishing specialised data science teams to rethink & redesign the existing strategies.
Read Moreby Rohit Sharma
12 Mar'215.35K+
Best Data Science Courses Online in 2024
Data science has been among the most sought-after professions in the US for the past few years, and there are many reasons why it would be best to pursue a career in this field.
However, to enter this field, you’ll need to have highly specialised and advanced qualifications. This article will shed light on some of the best data science courses available that you can join and kickstart your data science career.
Why Learn Data Science?
Here are some of the primary reasons why you should enrol in data science courses online:
It Is Among The Top 3 Best Jobs in America
Data scientist stayed at the top ofGlassdoor’s annual list of the top 50 jobs in the United States for four years until 2020, where it dropped to third place, going below the fronted engineer and Java developer.
However, you should note that even after dropping to 3rd place, the data scientist’s role offers higher pay and job satisfaction than the other two. Considering it stayed at the top for four consecutive years and is still among the top three of the US’s best jobs, a data scientist’s role is fantastic for tech aspirants. Read about data scientist salary in The US.
In 2022, the data scientist’s profile is in second place next to that of Java Developer. This indicates that data scientists will stay in demand for the coming years for sure.
A High Market Demand Backs It
The demand for data scientists is also on the rise, even though it’s a niche industry. According to Peter Bailis, CEO of Sisu, data scientists’ job prospects are strong, and the demand has also increased.
Since we have better machine learning and analytics tools available, the entry barrier for data science roles has lowered considerably. These solutions have made the jobs of data scientists much more efficient and quicker.
It Offers Handsome Annual Packages
The average pay of a data scientist in the US is $96,420 per annum, including bonuses, shared profits, and commissions.
A beginner with less than a year of experience earns around $85,000 per year on average in this field. Similarly, a data scientist with one to four years of experience makes $95,000 per year on average, while one with five to nine years of experience earns $109,000 per annum.
Experience and expertise matter a lot in this industry as data scientists with more than 20 years of industry experience get $136,000 per annum on average.
Best Data Science Courses Online
The reasons we discussed in the previous section highlighted how data science is among the best industries to enter right now. However, to enter this industry as a skilled professional, you’ll need to join one of the best data science courses online.
Joining a data science course will ensure that you learn all the required skills through a well-structured curriculum. At upGrad, we offer some of the best data science courses online available in the US:
1. Advanced Certificate Program in Data Science
Our Advanced Certification Program in Data Science is a 7-month course designed in collaboration with IIIT-B (International Institution of Information Technology Bangalore). This course’s learner base is in more than 50 countries globally and covers more than 300 hours of learning material.
We offer a complimentary Python Programming Bootcamp with this course so that you can easily transition from a non-tech job to a technical role like a data scientist. This course offers more than 20 hours of live sessions where you can resolve your doubts and get answers to your questions.
There will also be group coaching sessions giving you a comprehensive learning experience. You’d have the option to upgrade to the Post Graduate Diploma in Data Science program while taking this course (we have covered the course later in this article).
What You’ll Learn
The syllabus of our PG Diploma in Data Science course is:
Pre-Program Preparatory Content
In the first section of this course, you’ll study the fundamentals of MS Excel, MySQL, and Python. All three of them are industry staples for data science roles. You’ll also learn about analytics problem solving and data analysis in Excel.
Data Toolkit
This section of the course lasts for 12 weeks and consists of two assignments to test your knowledge. We’ll introduce you to Python, Python programming, and how you use Python in data science. This section will also teach you about data visualisation, hypothesis testing, inferential statistics, and exploratory data analysis.
Machine Learning
Many machine learning concepts find application in data science, and this section will introduce you to the same. You’ll learn about linear regression, clustering, and logistic regression, among others.
Final Section
Our course’s final section introduces you to advanced data science concepts and covers topics such as business intelligence, natural language processing, data engineering, etc.
Minimum Eligibility
To join this program, you must have a bachelor’s degree with a 50% Final Graduation Score. No prior coding experience is required to enrol in this course, as we’ll teach you the necessary programming tools and skills for becoming a data science professional.
Learn data analytics courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
2. Executive PG Program in Data Science
Executive PG Program in Data Science is a 12-month program we offer with IIIT-B. Like the previous course, the learner base for this program is spread across 50 countries worldwide.
This program offers six unique specialisations, and you can choose any one of them according to your background and career aspirations. You will be working on more than 60 industry projects and NASSCOM validated PG Diploma.
The six specialisations we offer with this program are:
Data Science Generalist
Deep Learning
Natural Learning Processing
Business Intelligence / Data Analytics
Business Analytics
Data Engineering
It is among the best data science courses for working professionals as it’s completely online and doesn’t require you to quit your job for continuing your studies. You will receive 25 expert coaching sessions for doubt resolution and progress feedback.
This course offers more than 400 hours of content and 20+ live learning sessions to provide an efficient and effective learning experience.
What You’ll Learn
Our PG Diploma in Data Science course has the following syllabus:
Preparatory Content
The course will cover MS Excel basics in data science, such as data analysis in Excel and analytics problem-solving. It will give you the necessary foundation to learn more advanced concepts.
Data Toolkit + Machine Learning
This section will teach you the basics and applications of Python in data science. You will also learn about machine learning and its applications in data science.
Specialisation Course
The majority of the course would depend on the specialisation you choose. This section will last for 22 to 27 weeks, depending on the specialisation.
Minimum Eligibility
You only need to have a bachelor’s degree to be eligible for this program. Like the previous course, this program doesn’t require you to have any coding experience as well.
2. Master’s of Science in Data Science- LJMU & IIITB
Master of Science in Data Science is among the best data science courses online for those who want to pursue senior roles in the data science industry. This program lasts for 18 months and has empowered over 34,500 students.
Our Master of Science in Data Science is the only online MSc program in data science. We offer this program with IIIT-B and Liverpool John Moores University. You will be working on more than 60 case studies and projects during this program and get 500+ hours of learning.
You will get 20+ live sessions and 25 coaching sessions with industry experts. Like the previous course, our Master of Science in Data Science also offers six specialisations you can pick from:
Data Science Generalist
Deep Learning
Natural Learning Processing
Business Intelligence / Data Analytics
Business Analytics
Data Engineering
We also offer a complimentary Python Programming Bootcamp and a career essential soft skills program with this course.
What You’ll Learn
The detailed curriculum of this program makes it one of the best data science courses online. An overview of this course’s syllabus is below:
Preparatory Content
Here, we’ll familiarise you with the fundamentals of data science, MS Excel and other relevant concepts.
Data Toolkit + Machine Learning
This section will focus on teaching you the necessary programming skills and data science concepts. It will allow you to understand the upcoming specialised courses.
Specialisation Courses + Master’s Dissertation
This section would depend on your chosen specialisation. Once you learn the advanced concepts, you’ll apply what you’ve learnt in the Master’s Dissertation module.
Minimum Eligibility
You only need to have a bachelor’s degree to be eligible for this program. You don’t need to have any coding experience to join this course.
4. Advanced Certificate Programme in Machine Learning
upGrad’s 7-months course is designed for freshers and mid-level managers. Senior executives can also apply for the course and uplevel in their careers.
What You’ll Learn
The course comprises 20 live sessions, 92 hours of learning, and 3 industry-relevant case studies and assignments designed to enhance practical skills in machine learning and develop knowledge of:
Underlying mathematics in machine learning
Optimization techniques
Evaluation metrics
Unsupervised Learning
Supervised Learning
Large Scale Machine Learning
Querying and Indexing
Data Streams
Introduction to Deep Learning.
Minimum Eligibility
Candidates require a minimum of a bachelor’s degree with 50% passing marks in Engineering, Science or Commerce to apply at one of the premier educational institutes in India.
Book your seat in our machine learning course today!
Final Thoughts
All the courses we discussed above are available online and allow you to study without interrupting your professional life. If you are interested in joining these programs, you can contact us or check our website’s courses.
Read Moreby Rohit Sharma
03 May'215.4K+
Python While Loop Statements: Explained With Examples
Python is a robust programming language that offers many functionalities. One of those functionalities is loops. Loops allow you to perform iterative processes with very little code.
In the following article, we’ll look at the while loop Python statement and learn how you can use it. We will also cover the various ways you can use this statement and what other functions you can combine with this statement. If you are a beginner in python and data science, upGrad’s data science certification can definitely help you dive deeper into the world of data and analytics.
Let’s get started.
What is a While loop Python Statement?
A while loop in Python runs a target repeatedly until the condition is true. In programming, iteration refers to running the same code multiple times. When a programming system implements iteration, we call it a loop.
The syntax of a while loop is:
while <expression>:
<statement(s)>
Here, <expression> refers to the controlling expression. It usually has one or more variables that get evaluated before beginning the loop and get modified in the loop body. The <statement(s)> refers to the blocks that get executed repeatedly. We call them the body of the loop. You denote them by using indentation, similar to if statements.
When you run a while loop, it first evaluates <expression> in Boolean. If the controlling expression is true, the loop body will execute. After that, the system checks <expression> again, and if it turns out to be true again, it will run the body again.
This process repeats until <expression> becomes false. When the controlling expression becomes false, the loop execution ends, and the code moves on to the next statement after the loop body, if there is any.
The following examples will help you understand the while loop better:
Example 1:
Input:
n = 7
while n > 0:
n -= 1
print(n)
Output:
6
5
4
3
2
1
0
Let’s explain what happened in the above example.
Initially, n is 7, as you can see in the first line of our code. The while statement header’s expression in the second line is n is greater than 0. That’s true, so the loop gets executed. Inline three, we see that n is decreased by 1 to 6, and then the code prints it.
When the loop’s body has been completed, the program execution goes back to the loop’s top (i.e., the second line). It evaluates the expression accordingly and finds that it’s still true. So, the body is executed again, and it prints 5.
This process will continue until n becomes 0. When that happens, the expression test will be false, and the loop will terminate. If there was another statement after the loop body, the execution would continue from there. However, in this case, there isn’t any statement so that the code will end.
Example 2:
Input:
n = 1
while n > 1:
n -= 1
print(n)
There is no output in this example.
In this example, n is 1. Notice that the controlling expression in this code is false (n > 1), so the code never gets executed. A while loop Python statement never executes if its initial condition is false.
Example 3:
Consider the following example:
Input:
a = [‘cat’, ‘bat’, ‘rat’]
while a:
print(a.pop(-1))
Output:
rat
bat
cat
When you evaluate a list in Boolean, it remains true as long as it has elements in it. It becomes false when it is or if it becomes empty. In our example, the list ‘a’ is true until it has the elements ‘cat’, ‘bat’, and ‘rat’. After removing those elements using the .pop() technique, the list will become empty, making ‘a’ false and terminating the loop. Read about python while loop statements.
Using the Break Statement
Suppose you want to stop your loop in the middle of its execution even though the while condition is true. To do so, you’ll have to use the break statement. The break statement would terminate the loop immediately, and the program execution would proceed to the first statement after the loop body.
Here’s the break statement in action:
Example 4:
Input:
n = 7
while n > 0:
n -= 1
if n ==3:
break
print(n)
print(‘Loop reached the end.’)
Output:
6
5
4
Loop reached the end.
When n became 3, the break statement ended the loop. Because the loop stopped completely, the program moved on to the next statement in the code, which is the print() statement in our example.
Using the Continue Statement
The continue statement allows you to stop the current loop and resume with the next one. In other words, it stops the current iteration and moves onto the next one.
The continued statement makes the program execution re-evaluate the controlling expression while skipping the current iteration.
Example 5:
Input:
n = 7
while n > 0:
n -= 1
if n ==3:
continue
print(n)
print(‘Loop reached the end.’)
Output:
6
5
4
2
1
Loop reached the end.
When we used the continue statement, it terminated the iteration when n became 3. That’s why the program execution didn’t print 3. On the other hand, it resumed its iteration and re-evaluated its condition. As the condition was still true, the program execution printed further digits until n became false, after which it moved onto the print() statement after the loop.
Using the else statement
One of Python’s exclusive features is the use of the else statement. Other programming languages lack this feature. The else statement allows you to execute code when your while loop’s controlling expression becomes false.
Keep in mind that the else statement will only get executed if the while loop becomes false through iterations. If you use the break statement to terminate the loop, the else statement wouldn’t be executed.
Example 6:
Input:
n = 10
while n < 15:
print (n, “is less than 15”)
n += 1
else:
print (n, “is not less than 15”)
Output:
10 is less than 15
11 is less than 15
12 is less than 15
13 is less than 15
14 is less than 15
15 is not less than 15
Become an expert in Python and Data Science
The while loop is one of the many tools you have available in Python. Python is a vast programming language and is the preferred solution among data scientists. Learning Python and its various concepts, along with data science all by yourself, can be tricky.
That’s why we recommend taking a data science course. It will help you study the programming language in the context of data science with the relevant technologies and concepts.
At upGrad, we offer the Executive PG Programme in Data Science. This is a 12-month course that teaches you 14+ programming tools and languages. It is a NASSCOM validated first Executive PGP in India, and we offer this program in partnership with the International Institute of Information Technology, Bangalore.
The program offers you six unique specializations to choose from:
Data science generalist
Deep learning
Natural language processing
Data engineering
Business analytics
Business intelligence/data analytics
Some of the crucial concepts you’ll learn in this program include machine learning, data visualization, predictive analysis with Python, natural language processing, and big data. You only need to have a bachelor’s degree with at least 50% or equivalent passing marks. This program doesn’t require you to have any prior coding experience.
upGrad has a learner base of over 40,000 learners in over 85 countries. Along with learning necessary skills, the program will allow you to avail of peer-to-peer networking, career counselling, interview preparation, and resume feedback.
These additional features will allow you to kickstart your Python and data science career much easier.
Conclusion
The while loop Python statement has many utilities. When combined with the break and the continue statements, the while loop can efficiently perform repetitive tasks.
Be sure to practice the loop in scenarios to understand its application properly. If you’re eager to learn more, check out the article we have shared above. It will help you significantly in your career pursuit.
Read Moreby Rohit Sharma
23 Jun'217.09K+
Python Classes and Objects [With Examples]
OOP – short for Object-Oriented Programming – is a paradigm that relies on objects and classes to create functional programs. OOPs work on the modularity of code, and classes and objects help in writing reusable, simple pieces of code that can be used to create larger software features and modules. C++, Java, and Python are the three most commonly used Object-Oriented Programming languages. However, when it comes to today’s use cases – the likes of Data Science and Statistical Analysis – Python trumps the other two.
This is no surprise as Data Scientists across the globe swear by the capabilities of the Python programming language. If you’re planning to start a career in Data Science and are looking to master Python – knowing about classes and objects should be your first priority.
Through this article, we’ll help you understand all the nuances behind objects and classes in Python, along with how you can get started with creating your own classes and working with them.
Classes in Python
A class in Python is a user-defined prototype using which objects are created. Put simply, a class is a method for bundling data and functionality together. The two keywords are important to note. Data means any variables instantiated or defined, whereas functionality means any operation that can be performed on that data. Together with data and functionality bundled under one package, we get classes.
To understand the need for creating a class, consider the following simple example. Suppose, you wish to keep track of cats in your neighbourhood having different characteristics like age, breed, colour, weight, etc. You can use a list and track elements in a 1:1 manner, i.e., you could track the breed to the age, or age to the weight using a list. What if there are supposed to be 100 different cats? What if there are more properties to be added? In such a scenario, using lists tends to be unorganized and messy.
That is precisely where classes come in!
Classes help you create a user-defined data structure that has its own data members (variables) and member functions. You can access these variables and methods simply by creating an object for the class (we’ll talk more about it later). So, in a sense, classes are just like a blueprint for an object.
Further, creating classes automatically creates a new type of objects – which allows you to further create more objects of that same type. Each class instance can have attributes attached to it in order to maintain its state. Class instances can themselves have methods (as defined by their class) for modifying the state.
Some points on Python class:
Classes are created by using the keyword class.
Attributes are the variables that are specific to the class you created.
These attributes are always public in nature and can be accessed by using the dot operator after the class name. For example, ClassName.AttributeName will fetch you the particular attribute detail of that particular class.
Syntax for defining a class:
class ClassName:
# Statement-1
.
.
.
# Statement-N
For example:
class cat:
pass
In the above example, the class keyword indicates that you are creating a class followed by the name of the class (Cat in this case). The role of this class has not been defined yet.
Check out All Python tutorial concepts Explained with Examples.
Advantages of using Classes in Python
Classes help you keep all the different types of data properly organized in one place. In this way, you’re keeping the code clean and modular, improving your code’s readability.
Using classes allows you to take the benefit of another OOP paradigm – called Inheritance. This is when a class inherits the properties of another class.
Classes allow you to override any standard operators.
Classes make your code reusable which makes your program a lot more efficient.
Objects in Python
An object is simply an instance of any class that you’ve defined. The moment you create a class, an automatic instance is already created. Thus, like in the example, the Cat class automatically instantiates an object like an actual cat – of Persian breed and 3 years of age. You can have many different instances of cats having different properties, but for it to make sense – you’ll need a class as your guide. Otherwise, you’ll end up feeling lost, not knowing what information is needed.
An object is broadly characterized by three things:
State: This refers to the various attributes of any object and the various properties it can show.
Behaviour: This basically denotes the methods of that object. It also reflects how this particular object interacts with or responds to other objects.
Identity: Identity is the unique name of the object using which it can be invoked as and when required.
1. Declaring Objects
Declaring Objects is also known as instantiating a class because as soon as you define a class, a default object is created in itself (as we saw earlier) – which is the instance of that class. Likewise, each time you create an object, you’re essentially creating a new instance of your class.
In terms of the three things (state, behaviour, identity) we mentioned earlier, all the instances (objects) share behaviour and state, but their identities are different. One single class can have any number of objects, as required by the programmer.
Check out the example below. Here’s a program that explains how to instantiate classes.
class cat:
# A simple class
# attribute
attr1 = “feline”
attr2 = “cat”
# A sample method
def fun(self):
print(“I’m a”, self.attr1)
print(“I’m a”, self.attr2)
# Driver code
# Object instantiation
Tom = cat()
# Accessing class attributes
# and method through objects
print(Tom.attr1)
Tom.fun();
The output of this simple program will be as follows:
Feline
I’m a feline
I’m a cat
As you can see, we first created a class called cat and then instantiated an object with the name ‘Tom.’ The three outputs we got were as follows:
Feline – this was the result of the statement print(Tom.attr1). Since Tom is an object of the Cat class and attr1 has been set as Feline, this function returns the output Feline.
I’m a feline – Tom.fun(); uses the object called Tom to invoke a function in the cat class, known as ‘fun’. The Tom object brings with it the attributes to the function, and therefore the function outputs the following two sentences – “I’m a feline”.
I’m a cat – same reason as stated above.
Now that you have an understanding of how classes and objects work in Python, let’s look at some essential methods.
2. The Self Method
All the methods defined in any class are required to have an extra first parameter in the function definition. This parameter is not assigned any value by the programmer. However, when the method is called, Python provides it a value.
As a result, if you define a function with no arguments, it still technically has one argument. This is called ‘self’ in Python. To understand this better, you can revise your concepts of Pointers in C++ or reference them in Java. The self method works in essentially the same manner.
To understand this better – when we call any method of an object, for example:
myObject.myMethod(arg1, arg2), Python automatically converts it into myClass.myMethod(myObject, arg1, arg2).
So you see, the object itself becomes the first argument of the method. This is what the self in Python is about.
3. The __init__ method
This method is similar to constructors in Java or C++. Like constructors, the init method is used to initialize an object’s state. This contains a collection of instructions (statements) that are executed at the time of object creation. When an object is instantiated for a class, the init method will automatically run the methods initialized by you.
Here’s a code piece of code to explain that better:
# A Sample class with init method
class Person:
# init method or constructor
def __init__(self, name):
self.name = name
# Sample Method
def say_hi(self):
print(‘Hello, my name is’, self.name)
p = Person(“Sam”)
p.say_hi()
Output:
Hello, my name is Sam
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Class and Instance Variables
Instance variables are unique to each instance, whereas class variables are for methods and attributes shared by all the instances of a class. Consequently, instance variables are basically variables whose value is assigned inside a constructor or a method with self. On the other hand, class variables are those whose values are assigned within a class.
Go through the following code to understand how instance variables are defined using a constructor (init method):
class cat:
# Class Variable
animal = ‘cat’
# The init method or constructor
def __init__(self, breed, color):
# Instance Variable
self.breed = breed
self.color = color
# Objects of Dog class
Tom = cat(“Persian”, “black”)
Snowy = cat(“Indie”, “white”)
print(“Tom details:’)
print(‘Tom is a’, Tom.animal)
print(‘Breed: ‘, Tom.breed)
print(‘Color: ‘, Tom.color)
print(‘\nSnowy details:’)
print(“Snowy is a’, Snowy.animal)
print(‘Breed: ‘, Snowy.breed)
print(‘Color: ‘, Snowy.color)
If you follow the above code line-by-line, here’s the output you’ll receive:
Output:
Tom details:
Tom is a cat
Breed: Persian
Color: black
Snowy details:
Snowy is a cat
Breed: Indie
Color: white
In Conclusion
Python is a comparatively easier programming language, particularly for beginners. Once you’ve mastered the basics of it, you’ll be ready to work with various Python libraries and solve data-specific problems. However, remember that while the journey begins from understanding classes and objects, you must also learn how to work with different objects, classes, and their nuances.
We hope this article helped clarify your doubts about classes and objects in Python. If you have any questions, please drop us a comment below – we’ll get back to you real soon!
If you’re looking for a career change and are seeking professional help – upGrad is here for you. Check out our Executive PG Program in Data Science offered in collaboration with IIIT-B. Get acquainted with 14+ programming languages and tools (including Python) while also gaining access to more than 30 industry-relevant projects. Students from any stream can enroll in this program, provided they scored a minimum of 50% in their bachelor’s.
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Read Moreby Rohit Sharma
25 Jun'215.43K+
Top 10 Programming Languages to Learn for Data Science
Data science is one of the hottest fields in the tech domain today. Although an emerging field, data science has given birth to numerous unique job profiles with exciting job descriptions. What’s even more exciting is that aspirants from multiple disciplines – statistics, programming, behavioural science, computer science, etc. – can upskill to enter the data science domain. However, for beginners, the initial journey might get a little daunting if one doesn’t know where to start.
At upGrad, we’ve guided students from different educational and professional backgrounds across the world and helped them enter the world of data science. So, trust us when we say it’s always best to start your data science journey by learning about the tools of the trade. When looking to master data science, we recommend you begin with programming languages.
Now the important question arises – which programming language to choose?
Let’s find out!
Best programming languages for Data Science
The role of programming in Data Science generally comes when you need to do some number crunching or create statistical or mathematical models. However, not all programming languages are treated alike – some languages are often preferred over others when it comes to solving Data Science challenges.
Keeping that in mind, here’s a list of 10 programming languages. Read it till the end, and you’ll have some clarity in terms of what programming language would best suit your data science goals.
1. Python
Python is one of the more popular programming languages in the Data Science circles. This is because Python can cater to a wide array of data science use cases. It is the go-to programming language for tasks related to data analysis, machine learning, artificial intelligence, and many other fields under the data science umbrella.
Python comes with powerful, specialized libraries for specific tasks, making it easier to work with. Using these libraries, you can perform important tasks like data mining, collecting, analyzing, visualizing, modelling, etc.
Another great thing about Python is the strong developers’ community that will guide you through any possible challenging situations and tasks. You’ll never be left without an answer when it comes to Python programming – someone from the community will always be there to help solve your problems.
Mostly used for: While Python has specialized libraries for different tasks, its primary use case is automation. You can use Python to automate various tasks and save a lot of time.
The good and bad: The active developers’ community is one of the biggest reasons why aspiring programmers and experienced professionals love Python and steer towards it. Also, you get many open-source tools related to visualization, machine learning, and more to help you with different data science tasks. There are not many cons to this language, except that it is relatively slower than many other languages present on this list – especially in terms of computational times.
2. R
In terms of popularity, R is second only to Python for working with data science challenges. This is an easy-to-learn language that fosters the perfect computational environment for statistics and graphical programming.
Things like mathematical modelling, statistical analysis, and visualization are a breeze with the R programming language. All of this has made the language a priority for data scientists across the world. Further, R can seamlessly handle large and complex datasets, making it a suitable language for dealing with the problems arising from the ever-increasing heaps of data. An active community of developers backs R, and you’ll find yourself learning a lot from your peers once you embark on the R journey!
Mostly used for: R is hands-down the most famous language for statistical and mathematical modelling.
The good and bad: R is an open-sourced programming language that comes with a solid support system, diverse packages, quality data visualization, as well as machine learning operations. However, in terms of cons, the security factor is a concern with the R programming language.
3. Java
Java is a programming language that needs no introduction. It has been used by top businesses for software development, and today, it finds use in the world of data science. Java helps with analysis, mining, visualization, and machine learning.
Java brings with it the power to build complex web and desktop applications from ground zero. It’s a common myth that Java is a language for beginners. Truth be told, Java is suitable for every stage of your career. In the field of Data Science, it can be used for deep learning, machine learning, natural language processing, data analysis, and data mining.
Mostly used for: Java has been mostly used for creating end-to-end enterprise applications for both mobiles and desktops.
The good and bad: Java is much faster than its competitors because of its garbage collector abilities. Thus, it is an ideal choice for building high-quality, scalable software. The language is extremely portable, and offers the write once, run anywhere (WORA) approach. On the downside, Java is a very structured and disciplined language. It isn’t as flexible as Python or Scala. So, getting the hang of the syntax and basics is pretty challenging.
4. C/C++
C++ and C are both very important languages in terms of understanding the fundamentals of programming and computer science. In the context of data science, too, these languages are extremely useful. This is because most new languages, frameworks, and tools use either C or C++ as their codebase.
C and C++ are preferred for data science owing to their quick data compilation abilities. In this sense, they offer much more command to developers. Being low-level languages, they allow developers to fine-tune different aspects of their programming per their needs.
Mostly used for: C and C++ are used for high-functioning projects with scalability requirements.
The good and bad: These two languages are really fast and are the only languages that can compile GBs of data in less than a second. On the downside, they come with a steep learning curve. However, if you’re able to get control of C or C++, you’ll find all other languages relatively easy, and it’ll take you less time to master them!
5. SQL
Short for Structured Query Language, SQL is a vital role if you’re dealing with structured databases. SQL gives you access to various statistics and data, which is excellent for data science projects.
Databases are crucial for data science, and so is SQL for querying the database to add, remove, or manipulate items. SQL is generally used for relational databases. It is supported by a large pool of developers working on it.
Mostly used for: SQL is the go-to language for working with structured, relational databases and querying them.
The good and bad: SQL, being non-procedural, doesn’t require traditional programming constructs. It has a syntax of its own, making it a lot easier to learn than most other programming languages. You don’t need to be a programmer to master SQL. As for cons, SQL features a complex interface that might seem daunting to beginners initially.
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6. MATLAB
MATLAB has for long been one of the go-to tools when it comes to statistical or mathematical computing. You can use MATLAB to create user interfaces and implement your algorithms. Its built-in graphics are varied enough and extremely useful for designing user interfaces. You can use the in-built graphics for creating visualizations and data plots.
This language is particularly useful for data science because it is instrumental in solving Deep Learning problems.
Mostly used for: MATLAB finds its way most commonly in linear algebra, numerical analysis, and statistical modelling, to name a few.
The good and bad: MATLAB offers complete platform independence with a huge library of in-built functions for working on many mathematical modelling problems. You can create seamless user interfaces, visualizations, and plots to help explain your data. However, being an interpreted language, it will tend to be slower than many other (compiled) languages on the list. Further, it’s not a free programming language.
7. Scala
This is a very powerful general-purpose programming language that has libraries specifically for data science. Since it is easy to learn, Scala is the ideal choice of many data science aspirants who’ve just started their journey.
Scala is convenient for working with large data sets. It works by compiling its code into bytecode and then runs it on a VM (Virtual Machine). Because of this compilation process, Scala allows for seamless interoperability with Java – opening endless possibilities for data science professionals.
You can use Scala with Spark and handle siloed data without any hassles. Further, owing to the concurrency support, Scala is the go-to tool for building Hadoop-like high-performance data science applications and frameworks. Scala comes with more than 175k libraries offering endless functionalities. You can run it on any of your preferred IDEs such as VS Code, Sublime Text, Atom, IntelliJ, or even your browser.
Mostly used for: Scala finds its use for projects involving large-scale datasets and for building high-functionality frameworks.
The good and bad: Scala is definitely an easy-to-learn language – especially if you’ve had any experience with programming earlier. It is functional, scalable, and helps in solving many Data Science problems. The con is that Scala is supported by a limited number of developers. While you can find Java developers in abundance, finding Scala developers to help you might be difficult.
8. JavaScript
Although JavaScript is most commonly used for full-stack web development, it also finds application in data science. If you’re familiar with JavaScript, you can utilize the language for creating insightful visualizations from your data – which is an excellent way to present your data in the form of a story.
JavaScript is easier to learn than many other languages on the list, but you should remember that JS is more of an aid than a primary language for data science. It can serve as a commendable data science tool because it is versatile and effective. So, while you can go ahead with mastering JavaScript, try to have at least one more programming language in your arsenal – one that you can use primarily for data science operations.
Mostly used for: In Data Science, JavaScript is used for data visualizations. Otherwise, it finds use in web app development.
The good and bad: JavaScript helps you create extremely insightful visualizations that convey data insights – this is an extremely pivotal component of the data analysis process. However, the language doesn’t have as many data science-specific packages as other languages on the list.
In Conclusion
Learning a programming language is like learning how to cook. There’s just so much to do, so many dishes to learn, and so many flavors to add. So, just reading the recipe will be no good. You need to go ahead and make that first dish – no matter how bad or good it turns out to be. Likewise, no matter which programming language you decide to go ahead with, the idea should be to keep practicing the concepts you learn. Keep working on a small project while learning the language. This will help you see the results in real-time.
If you’re in need of professional help, we’re here for you. upGrad’s Professional Certificate Programme in Data Science for Business Decision Making is designed to push you up the ladder in your Data Science Journey. We also offer the Executive PG Program in Data Science , for those interested in working with mathematical models for replicating human behaviour using neural networks and other advanced technologies.
If you’re looking for a more comprehensive course to dive deeper into the nuances of Computer Science, we have the Master of Science in Computer Science course. Check out the description of these courses and select the one that best aligns with your career goals!
If you’re looking for a career change and are seeking professional help – upGrad is just for you. We have a solid 85+ countries learner base, 40,000+ paid learners globally, and 500,000+ happy working professionals. Our 360-degree career assistance, combined with the exposure of studying and brainstorming with global students, allows you to make the most of your learning experience. Reach out to us today for a curated list of courses around Data Science, Machine Learning, Management, Technology, and a lot more!
Read Moreby Rohit Sharma
28 Jun'216.36K+
Top Python Design Patterns You Should Know
Design patterns are vital for programmers. They improve the efficiency of your programming as you can solve complex problems with a few lines of code by using design patterns. If you’re interested in learning Python, learning Python design patterns is a must. Learning them will make it easier for you to tackle various problems and make your code more functional.
You shouldn’t consider design patterns as completed designs that you can convert into code directly. They are templates that explain how you can solve a specific problem efficiently. If you are a beginner in python and data science, upGrad’s data science programs can definitely help you dive deeper into the world of data and analytics.
There are many Python design patterns you should know about. The following points will explain them better:
Types of Design Patterns
There are primarily three categories of design patterns:
Creational design patterns
Structural design patterns
Behavioural design patterns
They all have sub-categories that help you solve particular kinds of problems. It’s vital to be familiar with the different types of Python design patterns as each one works for a specific issue. Design patterns make it easier for you to communicate with your team, complete your projects earlier, and find any errors quickly.
Here are the primary categories and subcategories of Python design patterns:
1. Creational Design Patterns
Creational patterns give you the necessary information about the object or class instantiation. The most popular implementations of creational design patterns are class creational patterns and object creational patterns. Object creation patterns can utilize delegation, while class creation patterns can employ inheritance similarly.
Singleton Method
The singleton method ensures that a class has only a single instance and gives a global access point for the same. This way, you can be sure that a class has only one instance.
Prototype Method
The prototype method allows you to replicate objects without requiring your code to depend on their classes. It enhances your efficiency greatly and gives you an alternative to inheritance.
Builder Method
The builder method allows you to construct advanced objects in steps. This way, you can make various kinds of a single object while using the same code.
Abstract Factory Method
The abstract factory method allows you to create families of objects related to each other without giving particular concrete classes.
Factory Method
The factory method gives you an interface to create objects in a superclass. However, it enables subclasses to modify the object type you can create.
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2. Structural Design Patterns
A structural design pattern organizes various objects and classes to build bigger structures and offer new functionalities. It focuses on improving the efficiency and flexibility of your classes and objects.
Structural design patterns use inheritance to create the necessary interfaces. They also identify the relationships that simplify the structure.
FlyWeight Method
The flyweight method allows you to fit more objects into the available RAM by letting them share common components of state instead of storing all of the data in one object.
Proxy Method
With the proxy method, you can add a placeholder for a specific object. The proxy would handle access to the object so you can act before or after the request reaches the same.
Facade Method
The facade method gives you a simple interface to a framework, library, or advanced class set. It lets you isolate the code from the subsystem.
Decorator Method
The decorator method lets you add new behaviours to different objects dynamically without modifying their implementation. It does so by placing them inside wrapper objects that have the behaviours. Python is among the most suitable programming languages to implement this design pattern.
Composite Method
The composite method specifies an object group that you can treat just like you would treat a single instance of those objects. In other words, this method lets you compose objects into tree-type structures.
Bridge Method
The bridge method allows you to split large classes into two distinct hierarchies, implementation, and abstraction. Another highlight of this method is that you can develop them independently from each other.
Adapter Method
The adapter method allows collaboration between objects with incompatible interfaces. It follows the single responsibility principle and the open/closed principle. You should use the adapter method through the client interface, as it will allow you to change the adapters without modifying the client code.
3. Behavioural Design Patterns
Behavioural design patterns allow you to find the patterns for communication among objects and implement them as required. These patterns are related to the algorithms and the responsibilities assigned between objects. Following are the various classifications of behavioural design patterns:
Visitor Method
With this method, you can separate the algorithms from the objects they operate on. This method follows the single responsibility principle, which means you can move a behavior’s multiple versions into a class. However, it requires you to update every visitor when you add or remove a class from the hierarchy.
Template Method
The template method specifies an algorithm’s skeleton in the superclass while letting the subclass override particular steps of the algorithm without requiring any changes in the structure. A great advantage of this method is it enables you to pull the duplicate code into the necessary superclass.
Strategy Method
The strategy method lets you define the family of algorithms. You can put them in different classes and make the objects interchangeable by using this method. It enables you to isolate certain implementation information and makes it easy to introduce various strategies without requiring you to change the code.
State Method
This method enables an object to modify its behaviour if its internal state changes. This allows you to employ the state in the form of a derived class of the state pattern. It operates changes in the state by using methods from the pattern’s superclass.
Observer Method
The observer method allows you to specify a subscription system that notifies various objects about any events happening to the objects they observe. It defines one to multiple dependencies, so if an object’s state changes, every one of its dependents gets a notification.
Memento Method
With the memento method, you can save and restore the last state of an object without exposing its implementation details. It focuses on capturing and externalizing an object’s internal state without disturbing the code’s encapsulation. The undo and redo options present in various software solutions such as text editors, IDEs, and MS Paint, are an excellent example of the memento method’s implementation.
Mediator Method
The mediator method lets you reduce coupling between a program’s components. It does so by allowing them to communicate indirectly by using a particular mediator object. This method simplifies the modification and extension of components as they don’t remain dependent on other classes. The mediator method has four components, the mediator, the concrete mediator, the colleague, and the concrete colleague.
Iterator Method
The iterative method lets you go through a collection’s elements without exposing the elements’ details. It enables you to access the components of advanced data structures sequentially, without repetition. You can go through various kinds of data structures while using the iterator method, such as stack, graphs, trees, and many others.
Command Method
The command method enables you to parameterize clients with logging or queuing of requests. This means the button you used for one function can be used for another one. The command method encapsulates the necessary information to trigger an event or perform a particular action.
Chain of Responsibility Method
The chain of responsibility method is the object-oriented form of if…elif…elif…else. It enables you to pass requests through the handlers’ chain. You can rearrange the condition-action blocks during run-time by using the chain of responsibility method. It focuses on decoupling the senders from the receivers of a request form.
Become a Python Professional
The various Python design patterns we discussed in the previous section were just the tip of the iceberg. Python is a broad programming language with multiple functionalities and applications.
While studying Python, you must learn it in the context of its application. That way, you will learn the subject efficiently and will be able to test your skills quickly. Currently, one of the most in-demand and widespread applications of Python is in data science.
If you’re interested in learning Python and utilizing it as a professional, it would be best to join a data science course.
At upGrad, we offer the Executive PG Program in Data Science with IIIT-B. The course lasts for 12 months and offers you six different specializations:
Data engineering
Business analytics
Business intelligence/data analytics
Natural learning processing
Deep learning
Data science generalist
Not only does this course teach you the basic and advanced concepts of Python, but it also covers other relevant technologies to help you become a skilled data scientist. They include machine learning, data visualization, natural language processing, and a lot more.
upGrad has a learner base of 40,000+ students in more than 85 countries. The program offers peer-to-peer learning, allowing you to network globally with fellow professionals and students.
During the course, you’ll receive 360-degree career support and one-on-one mentorship from industry experts.
Summary
Python design patterns offer you a ton of advantages. They let you make the coding process more efficient by solving problems quickly. Design patterns also simplify your code and make it easier to share it with other professionals, which is particularly useful during collaborations.
What are your thoughts on design patterns? Let us know by dropping a comment below.
Read Moreby Rohit Sharma
21 Jul'215.56K+
Data Engineer Salary in US in 2024 : Based on Experience, Job Role, Skill and Education
Data is omnipresent and is being created and processed by the second in almost every industry. This copious amount of data requires data scientists and engineers to interpret meaningful insights and drive business performance.
As per the Data Science Interview Report, data engineering was the fastest-growing position in the data science domain in 2020. Interviews for the job role increased by 40% in different industries, especially in FAANG companies. According to IDG Cloud Survey, nearly 38% of all IT environments are currently on the cloud and are expected to reach 59% in 1.5 years. This surge in cloud computing is expected to open a wide range of avenues for data engineers and catapult their demands.
Data has pioneered into new-age sectors like artificial intelligence, machine learning, and Big Data and is expected to have a huge impact on the way companies do business. If you upskill and become an expert in data science, upGrad’s online data science programs can definitely help you dive deeper into the world of data and analytics.
Considering this rapid growth in demand, data engineers are compensated handsomely across industries. However, there are several other factors influencing the data engineer’s salary. Let us get into further details about data engineers and their remuneration.
What does a Data Engineer do?
Data Engineers are vital for an enterprise to collect, process, and develop algorithms for raw data to make it resourceful. They optimize how data is collected and processed. They also handle the process of retrieving data, creating dashboards, generating reports, and other relevant documents.
The primary responsibilities of data engineers include:
Designing data infrastructure
Building data
Arranging data pipelines for Data Scientists.
Accumulating and segregating data for functional and non-functional requirements.
Data engineers are required to have a wide range of technical skills like programming, automation, and database design for efficient data processing. In some organizations, they are expected to communicate the data trends.
Their roles are focused on three specific interests:
Generalist: The role of a generalist is seen in smaller companies where the data engineers are required to play several roles. Generalists take care of each step in the data process, starting from managing to analyzing.
Pipeline-centric: This role is seen in medium-sized companies where data engineers associate with data scientists to interpret the collected data meaningfully. Pipeline-centric data professionals must have a stronghold on computer science and distributed systems.
Database-centric: In huge companies where there is a constant flow of data, data engineers switch to analytic database systems. Database-centric data engineers work on multiple databases and generate table schemas for development.
Data Engineer Salary: How much does a Data Engineer earn?
As per Payscale, the average salary of a data engineer is $92,496 per annum. The compensation ranges between $65,000 to $132,000 based on the location, experience, levels, and skills of the data engineer. For instance, data engineers at the senior levels are offered $1,48,216, and those at mid-levels or level 2 are paid $116,591 per year.
A study suggests the demand for data engineers has been growing since 2016. As one of the fastest-growing domains in data science, data engineering witnesses approximately 50% growth every year in job opportunities. There was an 88.3% surge in job listings in 2019 alone.
Factors affecting the salary of Data Engineers
While there is no doubt that most organizations — large, medium, small, and startups — are willing to offer competitive compensation packages to data engineers, these professionals can enhance their earning potential in a number of other ways:
Experience
The years of experience that a data engineer brings to a job play a key role in determining his compensation. An entry-level data engineer is offered a starting salary of $90,615 per annum in the US while, on average, they earn about $108,291 per year. Senior-level data engineers, on the other hand, can earn an average of $124564 per year, with the base salary hitting nearly $179k at some companies, depending on their skills and certifications.
Education
Data Engineers usually possess a degree in computer science, electrical engineering and have business studies as their major. According to reports, 61% of data engineers possess a bachelor’s degree while 21% have a master’s degree.
Data engineers with a master’s degree from renowned institutions are given more preference and offered higher compensations. An Executive PG Program in Data Science can also increase your earning potential and make you eligible for sought-after roles.
A lot of companies look for data engineers with a diploma in certified data engineering courses like Cloudera, Google Cloud Certification, CPEE (certificate in Engineering Excellence), and IBM certification. Data Engineers with knowledge in SQL, Python, Big Data, Apache Hadoop, and ETL have a high demand in the market.
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Job Roles
Compensation packages for data engineers also vary depending on their roles and positions in an organization. Let us look at different roles you can pursue as a data engineer:
Data Analyst: The primary roles of data analysts include procuring, analyzing, and interpreting data to make them resourceful. They also help the clients with minor business decisions with the help of advanced computerized models that help in comparing data and predicting outcomes. The base salary package of an entry-level data analyst is $67,492 per annum as against their senior counterparts, who earn $84,295 annually.
Business analyst: Business Analysts help companies improve and scale their operations by studying their business models in detail and upgrading them with new technologies to keep in tune with the current market trends and expectations.The package offered to a business analyst can range between $69,536 – $86,509 per year based on the years of experience. Interviews for business analysts saw a 20% increase in 2020, thereby substantiating their growing demand.
Data Architect: Data Architects generate drafts for data management. They architect a plan to collaborate, centralize, safeguard, and maintain a company’s data sources after a detailed analysis. Data architects are paid an average of $121198 per year. Naturally so, data architects at the entry-level are paid less than those at the top of the hierarchy.
Levels
Different levels in data engineering correspond to their experience, roles, and overall command in the workplace. Data engineers at higher levels on their career ladder earn significantly higher than those at entry levels.
Data Engineer I: $109K
Data Engineer II: $121K
Data Engineer III: $127K
Principal Data Engineer: $151,886
(Salary Source – Glassdoor )
In companies where a data engineer performs the additional role of a manager, i.e., if they transition to the managerial track, they are offered a higher compensation.
Industry
The salary of data engineers also varies with their demand in different industries. Retail, media, and technology sectors are leading industries where data engineers are highest in demand and are compensated accordingly. These are followed by finance and professional services companies.
The following list provides the details of the industries and the corresponding average packages offered to data engineers:
Retail: $114,152 per year
Media: $112,864 per year
Technology: $105,173 per year
Professional Services: $98,633 per year
Finance: $82,262 per year
Here is the list of top companies and their packages offered to data engineers.
Amazon: $123,736 per year
Hewlett-Packard: $86,164 per year
Facebook: $134331 per year
Google: $161544 per year
IBM: $107951 per year
Different cities also offer lucrative packages to data engineers depending on their demand and earning potential. It is estimated that cities like California, Washington, New York, New Hampshire, and Massachusetts offer the highest salaries to data engineers. As per Hired’s State of Software Engineer’s report 2019, the average package of data engineers has grown by 7% in New York and 6% in the Bay Area.
Skills
Data Engineering is an amalgamation of software engineering and data science. A data engineer with strong knowledge in each of these disciplines is hired by leading companies. In addition to these two, data engineers are also required to be well-versed in programming languages like PHP, Scala, R, Go, and other relevant languages.
These skills offer leverage to data engineers for salary negotiations and can fetch an additional 10-15% in the salary package. As per PayScale, the following skills provide a considerable boost in the package:
Scala: 17%
Apache Spark: 16%
Data Warehouse: 14%
Java: 13%
Data modelling: 12%
Apache Hadoop: 11%
Linux: 11%
ETL: 7%
Amazon Web Services (AWS): 10%
Big Data Analytics: 6%
Future Scope of Data Engineering
As per the 2020 technical job report by DICE, data engineering is the most rapidly growing sector, having witnessed a 50% year-over-year surge in job opportunities between 2019 and 2020. In addition to this, the earning potential of data engineers is further expected to increase since most companies are shifting to the cloud. Not to mention, data engineering has surpassed data scientist roles by 2:1, and companies now pay them 20-30% more, something that is bringing data engineers closer to being tagged as the highest paid professionals in the technology sector.
The following statistics by popular tech platforms reveal a consistent growth in data engineering:
The Hired State of Software Engineers Report shows a 45% year-on-year growth in the domain.
LinkedIn’s Emerging Job Report recorded a 33% year-on-year job growth.
The Burning Glass Nova Platform reports a 88% year-on-year growth in data engineering jobs.
These are indicative of the rapid pace at which data engineering is overtaking the data science sector.
Following the heavy influx of data scientists in industries, companies have realized the importance of a regulated data infrastructure to provide effective data analysis. So, businesses are now spending time and effort to hire data engineers who have a sound understanding of systematic cloud infrastructure and data architecture.
Big data engineering services in companies like Accenture and Cognizant have led to an 18% yearly growth in the market and are expected to reach 31% by 2025.
Transform your career with upGrad’s online Data Science Programs
Considering the impressive trend for data engineering and that the position is well-positioned to be the next massive thing in the tech industry, there hasn’t been a better time to upskill yourself to land a lucrative position in data science.
And upGrad offers a unique opportunity to transform your career with its Executive PG Programme in Data Science from IIIT Bangalore. It is a 12-month course that teaches you highly sought-after skills like Python, Tableau, Apache Hadoop, AWS, and MySQL, among others.
In addition to this, students stand to learn industry-relevant skills through specialization tracks which include Data Science Generalist, Deep Learning, Natural Language Processing, Business Intelligence/Data Analytics, Business Analytics, and Data Engineering.
The course is designed for freshers and mid-level managers who can engage in collaborative projects on the global platform and indulge in peer-to-peer learning with students and mentors from diverse backgrounds.
upGrad global learner base of over 40,000 is spread across 85+ countries. Its in-person learning platform is supplemented by 360-degree career assistance and personalized, subjective feedback from experts to facilitate improvement.
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Read Moreby Rohit Sharma
30 Jul'215.35K+
What is Web Scraping & Why Use Web String?
Websites are loaded with valuable data, and procuring data involves a complex process of manually copy-pasting the information or adhering to the format used by the company — irrespective of its compatibility with the users’ system. This is where web scraping pitches in.
Web Scraping — What is it?
Web Scraping is the process of scooping out and parsing data from a website which in turn is converted to a format that makes it resourceful to the users.
Although web scraping can be done manually, the process becomes complex and tedious when a large amount of raw data gets involved. This is where automated web scraping tools come into effect as they are faster, efficient, and relatively inexpensive.
Web Scrapers are dynamic in their features and functions as their utility varies according to the configurations and forms of websites. Learn data science from top universities from upGrad to understand various concepts and methods of data science.
How to Web Scrape useful data?
The process of web scraping begins with providing the users with one or more URLs. Scraping tools generate an HTML code for the web page that needs to be scrapped.
The scraper then scoops out the entire data available on the web page or only the selected portions of the page, depending upon the user’s requirement.
The extracted data is then converted into a usable format.
Why don’t some websites allow web scraping?
Some websites blatantly block their users from scraping their data. But why? Here are the reasons why:
To protect their sensitive data: Google Maps, for instance, does not allow the users to get faster results if the queries are too many.
To avoid frequent crashes: A website’s server might crash or slow down if flooded with similar requests as they consume a lot of bandwidth.
Different categories of Web Scrapers
Web scrapers differ from each other in a lot of aspects. Four types of web scrapers are in use.
Pre-built or self-built
Browser extensions
User Interface (UI)
Cloud & local
1. Self-built web scrapers
Building a web scraper is so simple that anybody can do it. However, the knowledge of handling scraping tools can be obtained only if the user is well versed with advanced programming.
A lot of self-built web scrapers are available for those who are not strong in programming. These pre-built tools can be downloaded and used right away. Some of these tools are equipped with advanced features like Scrape scheduling, Google sheet export, JSON, and so on.
2. Browser Extensions
Two forms of web scrapers that are widely in use are browser extensions and computer software. Browser extensions are programs that can be connected to the browser like Firefox or Google Chrome. The extensions are simple to run and can be easily merged into browsers. They can be used for parsing data only when placed inside the browser, and advanced features placed outside the browser cannot be implemented using scraper extensions.
To alleviate that limitation, scraping software can be used by installing it on the computer. Though it is not as simple as extensions, advanced features can be implemented without any browser limitations.
3. User Interface (UI)
Web scrapers differ in their UI requirements. While some require only a single UI and command line, others may require a complete UI in which an entire website is provided to the user to enable them to scrape the required data in a single click.
Some web scraping tools have the provision to display tips and help messages through the User Interface to help the user to understand every feature provided by the software.
4. Cloud or Local
Local scrapers run on the computer feeding on its resources and internet connection. This has the disadvantage of slowing down the computer when the scrapers are in use. It also affects the ISP data caps when made to run on many URLs.
On the contrary, cloud-based scraping tools run on an off-site server provided by the company that develops the scrapers. This ensures to free-up computer resources, and the users can work on other tasks while simultaneously scraping. The users are given a notification once the scraping is complete.
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Web scraping using different methods
The four methods of web scraping that are widely in use are:
Parsing data from the web using string methods
Parsing data using regular expressions
Extracting data using HTML parser
Scraping data by interacting with components from other websites.
Parsing data from the web using string methods
This technique procures data from websites using string methods. To search the desired data from HTML texts, the find () tool can be used. Using this tool, the title tag can be obtained from the website.
If the index of the first and last character of the title is known, a string slice can be used to scrape the title.
The tool. find () will return the first substring occurrence, and then the index of the starting <title> tag can be obtained by using the string ” <title> to get. find ().
The data of interest is the title index and not the index of the <title>. To obtain an index for the first letter in the title, the length of the string “<title> can be added to the title index.
Now, to get the index of the final part </title>, the string “</title>” can be used.
Now that the first and closing part of the title is obtained, the entire title can be parsed by slicing the HTML string. Here’s the program to do so:
>>> url = “http://olympus.realpython.org/profiles/poseidon“
>>> page = urlopen(url)
>>> html = page.read().decode(“utf-8”)
>>> start_index = html.find(“<title>”) + len(“<title>”)
>>> end_index = html.find(“</title>”)
>>> title = html[start_index:end_index]
>>> title
‘\n<head>\n<title >Profile: Poseidon’
Notice the presence of HTML code in the title.
Parsing Data using Regular expressions
Regular Expressions, a.k.a regexes are patterns that are used for searching a text inside a string. Regular expression parsers are supported by Python through its re module.
To start with regular expression parsing, the re module should be imported first. Special characters called metacharacters are used in regular expressions to mention different patterns.
For example, the special character asterisk (*) is used to denote 0.
An example of using findall () to search text within a string can be seen below.
>>> re. findall (“xy*, “ac”)
[‘ac’]
In this python program, the first argument and the second argument denote the regular expression and the string to be checked, respectively. The pattern “xy* z” will match with any portion of the string that starts with “x” and ends with “z”. The tool re. findall () returns a list that has all the matches.
The “xz” string matches with this pattern, and so it is placed in the list.
A period(.) can be used to represent any single character in a regular expression.
Extracting data using HTML parser
Though regular expressions are effective in matching patterns, an HTML parser exclusively designed to scrape HTML pages is more convenient and faster. The soup library is most widely used for this purpose.
The first step in HTML parsing is installing beautiful soup by running:
$ python3 -m pip install beautifulsoup4.
The details of the installation can be viewed by using Run pip. Here is the program to create the beautiful soup object:
import re
from urllib.request import urlopen
url = “http://olympus.realpython.org/profiles/dionysus”
page = urlopen(url)
html = page.read().decode(“utf-8”)
pattern = “<title.*?>.*?</title.*?>”
match_results = re.search(pattern, html, re.IGNORECASE)
title = match_results.group()
title = re.sub(“<.*?>”, “”, title) # Remove HTML tags
print(title)
Run the program for beautiful soup using python. The program will open the required URL, read the HTML texts from the webpage as a string, and delegate it to the HTML variable. As a result, a beautiful soup object is generated and is given to the soup variable.
The beautiful soup object is generated with two arguments. The first argument has the HTML to be scraped, and the second argument has the string “html. parser” that represents Python’s HTML parser.
Scraping data by interacting with components from other websites.
The module ” url lib” is used to obtain a web page’s contents. Sometimes the contents are not displayed completely, and some hidden contents become inaccessible.
The Python library does not have options to interact with web pages directly. A third-party package like Mechanical Soup can be used for this purpose.
The Mechanical soup installs a headless browser, a browser with no graphic UI (User Interface). This browser can be controlled by python programs.
To install Mechanical soup, run the following python program.
$ python3 -m pip install MechanicalSoup
The pip tool displays the details of the installed package.
Purpose of web scraping
The following list shows the common purposes for which web scraping is done.
Scraping the details of stock prices and loading them to the API app.
Procure data from yellow pages to create leads.
Scraping data from a store finder to identify effective business locations.
Scraping information on the products from Amazon or other platforms for analyzing competitors.
Scooping out data on sports for betting or entertainment.
Parsing data on finance for studying and researching the market.
Conclusion
Data is everywhere, and there is no shortage of resourceful data. The process of converting raw data into a usable format has become simple and faster with the advent of new technologies in the market. Python’s standard library offers a wide variety of tools for web scraping, but those offered by PyPI simplifies the process. Scraping data can be used to create many exciting assignments, but it is particularly important to respect the privacy and conditions of the websites and to make sure not to overload the server with huge traffic.
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Read Moreby Rohan Vats
31 Jul'21