Best Data Science Courses Online in 2024

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.  Learn Data Science Courses online at upGrad 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 Our learners also read: Learn Python Online Course Free 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  Top Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Programs Inferential Statistics Programs 2 Hypothesis Testing Programs Logistic Regression Programs 3 Linear Regression Programs Linear Algebra for Analysis Programs 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 Explore our Popular Data Science Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Certifications Check our US - Data Science Programs Professional Certificate Program in Data Science and Business Analytics Master of Science in Data Science Master of Science in Data Science Advanced Certificate Program in Data Science Executive PG Program in Data Science Python Programming Bootcamp Professional Certificate Program in Data Science for Business Decision Making Advanced Program in Data Science 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.  Read our Popular US - Data Science Articles Data Analysis Course with Certification JavaScript Free Online Course With Certification Most Asked Python Interview Questions & Answers Data Analyst Interview Questions and Answers Top Data Science Career Options in the USA SQL Vs MySQL – What’s The Difference An Ultimate Guide to Types of Data Python Developer Salary in the US Data Analyst Salary in the US: Average Salary 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] upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on How to Build Digital & Data Mindset? document.createElement('video'); https://cdn.upgrad.com/blog/webinar-on-building-digital-and-data-mindset.mp4   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.  Check out the courses provided by upGrad in association with top universities and industry leaders. Some of the courses offered by upGrad are: PG Diploma in Data Science: This is a 12-month course on Data Science provided by upGrad in association with IIIT-B.  Masters of Science in Data Science: An 18-month course provided by upGrad in association with IIIT-B and Liverpool John Moores University.  PG Certification in Data Science: A 7-month long course on Data Science provided by upGrad in association with IIIT-B.

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.

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. 

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.

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 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. 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. 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. 

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. 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. 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!