What is Encapsulation in OOPS [Explained With Examples]

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! 

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.  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. 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.

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.  Get data science certification online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. 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.  Contact us today to boost your learning experience with the 60+ industry projects and 5+ capstone projects each track in the course offers!

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.  Get data science certification online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. 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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