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Find out how to hand a perfect data science internship in this ultimate guide for beginners

You undoubtedly struggled to find the ideal data science internship as a beginner in tech. Newcomers to the IT business frequently have questions about which companies to apply to and what to do next. Regrettably, this concern is justified given that internships may make or break your career in data science.

What is a Data Science Internship?

Any program that allows a novice in data science to gain practical experience, hone their abilities, and comprehend the breadth of the discipline is considered a data science internship. It typically lasts three to four months, although depending on the organization, some may last up to a year.

You will gather, examine, and compile data with more seasoned experts as a data science intern, and you will produce polished reports on your findings. In addition to volunteer work or paid employment, these activities eventually result in important industry experience useful to employers.

Skills Required

There are a few abilities you must have mastered before beginning your quest. You’ll have a better chance of getting a data science internship if you possess these abilities before submitting your application. Moreover, the majority of employers will demand that candidates have some previous knowledge, and a select few could conduct tests before hiring them. Hence, a couple of them are shown below.

Knowledge of Programming and Scripting Languages

Although it’s not essential to data science, programming may be useful for managing and visualizing enormous amounts of unstructured data. The most popular computer language for data science is Python, while R offers more flexibility. The languages Julia, Matlab, Java, SAS, and C++ are also used in data science. Nonetheless, keep in mind that you are applying as an intern; nobody wants you to be an expert right away.

    Knowledge of the Core Data Science Tools

    It will be possible to automate some processes and organize data by writing scripts and learning algorithms, but those are not the only skills required for a data science internship. Also, you’ll need to correctly analyze your data, create charts, and use prediction models. Your data science tools will be useful in this situation. Data scientists now have alternatives for gathering, assembling, cleaning, and manipulating data thanks to technology. One of these choices is Microsoft’s Power BI, a revolutionary program that converts massive amounts of data into appealing charts and dashboards. Excel or Tableau are good substitutes that are similarly effective.

      Statistics

      Although it could seem difficult, especially if you’re self-taught, statistics isn’t an insurmountable challenge. You will be able to relate to and evaluate your data more successfully if you have a foundation in statistics. Data science relies on statistics and mathematics to support some of its fundamental ideas, such as logistic regression and clustering. You have a better chance of landing data science internships and can see your career path more clearly if you have a basic grasp of the field. Don’t be concerned if you don’t have a degree in statistics since you can begin a career in data science.

      Tips

      What should you do next to guarantee that you secure desirable internships? If you aren’t applying the talents you learn, then learning a lot of them won’t be of much use to you.

      Actively Pursue Personal Projects

      As they say, practice makes perfect, and data science is no exception. To get picked for good data science internships, you need something to present in your resume or portfolio, generating the necessity for personal projects.

        Create an ATS-Compliant Resume and Cover Letter

        After your tasks are prepared, you may start writing your CV. This could seem simple at first, but seemingly insignificant errors could end up costing you. Failure to understand how to create an ATS-friendly resume is one such error.

          Attend Data Science Events and Tech Workshops to Expand Your Network

          Even in the IT industry, your network determines your net worth. In addition to applications, recommendations, and referrals are another excellent strategy to get data science internships. Having a large network increases your chances of receiving a favorable recommendation, and networking may be done effectively by going to events. Attending non-tech events is not forbidden, but networking with individuals in your industry or closely related professions will be more beneficial.

            Make Contact with Startups

            There is a widespread misunderstanding among techies that startups don’t require data scientists. This is untrue and limiting, though. Apply to larger corporations, but don’t be afraid to ask about possible data science internships at local startups and smaller businesses.

              Use GitHub and Kaggle Often

              You're reading How To Land A Data Science Internship? The Ultimate Guide

              A Quick Guide To Data Science And Machine Learning

              This article was published as a part of the Data Science Blogathon.

              Introduction

              Do you know that data is the ultimate goal for every organization, and hence actually I believe that it is the ruler? Without data, nothing can be achieved. From a business perspective to solving problems for end-to-end applications we require data.

              This data needs to be in order to derive some purpose from it. Because forms of data can be texts, images, videos, infographics, gifs, etc. Some data are structured while most of them are unstructured. Collection, analysis, and prediction are the necessary steps that are to take into consideration with this data.

              Image Source

              Now, what exactly are Data Science and Machine learning?

              I’ll just define it for you in a simple way. All the context related to this can be similar if you search somewhere else. So data science is the science of deriving insights from data for the purpose of gaining the most important and relevant source of information. And with a reliable source of information making predictions by the use of machine learning. So I guess you might have very well understood this definition. Now my point here is that with data science you can bring meaningful insights.

              Why there is a need for data science and machine learning?

              Data has been there for a very long time. During earlier times analysis of data was done by statisticians and analysts. Analysis of data was done primarily to get the summary and what were the causes. Mathematics was also the core subject of interest when used for this work.

              How data science and machine learning solutions?

              Data science uses statistical methods, maths, and programming techniques to solve these problems. The programming techniques are extensively used for analysis, visualizing, and making predictions. So you see it does all the work of a statistician, programmer, and maths. The study of all these major areas makes the best way of dealing with such big data. Machine learning is integrated by making models from various algorithms. 

              This is done for model building in data science which helps for future predictions. These predictions depend upon the new data which is given to the model without explicitly telling it what to do. The model understands it and then gives us the output or solution. For example, banks use machine learning algorithms to detect if there is a fraud transaction or not. Or if this customer will default in paying his credit card dues.

              Cancer detection in the health care industry uses data science and machine learning to detect if patients are prone to cancer or not. So there are a lot of examples around us where companies are widely using this. Online food delivery companies like zomato or swiggy use for recommending us food to order based on what have we ordered in the past. This type of machine learning algorithm is a recommendation system. They are also used by YouTube, Spotify, Amazon, etc.

              The Data science life cycle.

              There are various steps involved in solving business problems with data science.

              1. Data acquisition – this process involves the collection of data. Depends on are objectives or what is the problem that needs to be solved. By this means, we tend to gather the required data.

              2. Data pre-processing – this stage involves processing data in a structured format for ease of use. Unstructured data cannot be used for any analysis because it will give wrong business solutions and can have a bad impact on consumers.

              3.Exploratory data analysis (EDA) – it is one of the most important stages where all the summarizations of data by statistics and math’s. Identifying the target(output) variable and predictor(independent) variables. Visualization of data and then sorting all the necessary data that will be used for predictions. Programming plays a vital role in this. A data scientist spends almost 75% of their time on this to understand their data very well. Further in this stage data is divided into training and test data.

              4. Model building – After EDA we select the most appropriate methods to build our model. This is done with the use of machine learning algorithms. Selection of algorithms like regression, classification, or clustering. As machine learning algorithms are of 3 types. Supervised learning, unsupervised learning, and reinforcement learning. There are different sets of algorithms for all these types. Selecting them depends mainly on what is a problem are we trying to solve.

              5. Evaluation of model – model evaluation is done to see how efficient our model is doing on the test data. Minimizing errors and also tuning of the model.

              6. Deployment of model – model deployment is done as now it is fit to cater to all the future data for making predictions.

              Note: There are re-evaluation techniques involved even after deployment to keep our model up-to-date.

              How all this is done?

              Data science tools and frameworks are specifically used for this process. Some popular tools like jupyter, tableau, tensor flow. Programming languages such as Python and R are important to do these tasks. To know and learn any one language is sufficient. Python and R are widely used for data science because there are additional libraries that make it easy for any data science project. I prefer Python as it is open-source, easy to learn, and has huge community support across the world. Statistics, math, and linear algebra are some core subjects you need to understand before getting involved in any data science or machine learning project.

              In the future, these sources of data will keep on expanding and there will be a need to harvest all of these. An important part or information to get from this data will only derive the need for data scientists and machine learning engineers.

              Mohammed Nabeel Qureshi

              Related

              The Ultimate Guide To Digital Marketing

              Do you want to reach your customers online? Digital marketing enables you to deliver marketing messaging to your ideal customers on a variety of platforms online.

              In this guide, we’re going to look at the various aspects of digital marketing and how you can use them to drive qualified traffic to your business.

              What Is Digital Marketing?

              Digital marketing is the promotion of your business, brand, products, or services to your target audience on desktop, mobile, and smart TV screens. There are several types of digital marketing you can use, all of which we will discuss shortly.

              But first, let’s talk about why you should invest in digital marketing.

              Why Is Digital Marketing Important?

              In the performance section of the CMO Survey, recipients were asked, “To what degree has the use of digital marketing contributed to your company’s performance during last year? (1=not at all, 7=very highly)”

              The results?

              “Across all sectors and firm sizes, this cannot be overstated with the average reporting a contribution of 5.5 (on a 7-point scale) to their companies’ success.”

              The 2023 Gartner Digital Marketing Survey echoed a similar positive response about digital marketing performance, saying that “70% or more said their organizations had met or exceeded a range of 2023 goals, though that positive outlook may reflect performance against goals revised midyear versus set prior to the pandemic.”

              How Does Traditional Marketing Compare To Digital Marketing?

              Should your business invest in digital marketing over traditional marketing? Traditional marketing includes radio, non-digital billboards, print publications, and offline events.

              According to the CMO Survey, digital marketing spend was expected to increase by 14.3% in 2023. Traditional marketing spend, on the other hand, was expected to decrease by 0.2%.

              Marketing Charts published data from PwC’s annual Global Entertainment & Media Outlook report.

              What Are The Different Types Of Digital Marketing?

              Here are some of the various types of digital marketing businesses and brands use to reach more customers online.

              Affiliate Marketing

              According to a survey by PepperJam and Forrester, the top-ranked digital marketing channel for customer acquisition in 2023 was affiliate marketing.

              This is a winning marketing strategy for businesses that want to transform customers and fans into referral partners who can increase the business’ revenue.

              Find out more with the following resources:

              Content Marketing

              Content marketing allows businesses to communicate marketing messages through a variety of different formats.

              Content marketing encompasses blog posts, knowledge base articles, support documentation, white papers, case studies, and similar content resources.

              According to Statista, over 60% of B2B and 40% of B2C content marketing budgets were expected to increase in 2023. Further, 69% of companies said they would invest their budget in content creation and production.

              A comprehensive content marketing strategy gives your email, search, and social media marketing departments assets to promote.

              Learn more in the following resources:

              Email Marketing

              Email marketing allows businesses to reach people who have consented to receive marketing messages by email. Businesses can collect emails on their website, blog, and social media channels.

              Research from Litmus has shown for every dollar spent on email marketing, brands can expect to receive $42 in return.

              If you want to convert more of your website traffic, use email marketing to keep people’s attention on your brand until they are ready to make a purchase.

              Learn more with the following resources:

              Guerilla Marketing

              Guerilla marketing allows businesses to gain free public relations for out-of-the-box, viral marketing campaigns that people want to talk about.

              Many guerilla marketing campaigns happen offline, like flash mobs and large graffiti art walls.

              Since people will talk and share the experience online, it becomes a part of your brand’s digital marketing strategy.

              Influencer Marketing

              Influencer marketing allows businesses to reach new audiences through popular social media users. Brands campaign with celebrities online to market products via sponsored and paid partnership posts.

              A key finding from the State of Influencer Marketing is that “In 2023, sponsored posts received an average of 7,806 impressions (unique views). In 2023, sponsored posts averaged 4,827 impressions. A 57% increase [over 2023].”

              Influencer marketing can play a vital role in increasing a brand’s visibility amongst a niche audience.

              Learn how with the following resources:

              Mobile Marketing

              In SimpleTexting’s SMS marketing report, 62% of consumers surveyed said they opted into text message marketing from at least one business within the last year.

              If you want to connect with consumers on their mobile devices, learn more about mobile marketing from the following resource.

              Podcast Marketing

              Podcast marketing allows businesses to reach audiences who listen to podcasts on Spotify, Apple, and other platforms.

              In addition to gaining additional brand visibility, your business can use podcasts as assets to acquire links for search engine optimization purposes.

              Find out how with the following podcast resources:

              Public Relations

              Public relations marketing allows businesses to gain visibility for their brand through the media. The goal of PR is to get journalists and influencers talking about your brand, products, and services.

              The Global Public Relations Market Report predicts that the global PR market will grow from $88.13 billion in 2023 to $97.13 billion in 2023. The growth is linked to companies recovering from the effects of COVID-19.

              When PR marketing is executed effectively, it works to supplement a brand’s social media presence and link-building efforts.

              Learn more about PR from the following resources:

              Search Engine Marketing

              Search engine marketing allows businesses to reach people through search engines like Google, Bing, and Yahoo.

              According to BrightEdge research, 68% of trackable traffic comes from organic (53%) and paid search (15%).

              Organic search rankings, therefore, play a large role in generating qualified traffic for your website.

              Learn more with the following resources.

              Social Media Marketing

              Social media marketing allows businesses to reach their customers on channels like Facebook, Instagram, LinkedIn, Twitter, Pinterest, and TikTok.

              We Are Social’s Digital Report for 2023 found that there are 4.2 billion people worldwide who actively use social media. Of those social media users, 27.5% use social media to research products to buy. 44.8% use social media to search for more information about brands.

              With such a large portion of the global population on social media, most brands will be able to connect with their target audience through one or more social networking sites. Social media can also help you in your SEO efforts.

              Learn how in the following resources:

              Streaming TV Marketing

              Streaming TV marketing allows businesses to reach consumers while they watch their favorite TV programs online. Thanks to digital streaming, ad platforms can offer specific targeting and results measurement.

              According to We Are Social’s Digital Report for 2023, 70% of internet users aged 16 to 64 worldwide stream TV content via the internet.

              Ready to reach the cord-cutters who switched from regular TV to digital streaming?

              Learn more with the following resources:

              Video Marketing

              Video marketing allows businesses to reach their audiences with video content. In most cases, this is achieved via YouTube and other video hosting services.

              In the State of Video Marketing report by Wyzowl, 78% of video marketers said that video helped increase sales. 83% of video marketers said video increases the average time spent on the site, and 86% said video increased their website traffic.

              Use video to fill your content library, promote as a part of your link-building assets, and share as a part of your social media campaigns.

              Learn more from the following resources:

              Voice Marketing

              Voice marketing allows businesses to reach people using voice-enabled services like Alexa (Amazon), Siri (Apple), and Google Assistant.

              It also refers to marketing via live or prerecorded audio content on audio-only platforms like Spotify, Clubhouse, and Facebook Live Rooms.

              The Narvar Consumer Report found that up to 45% of voice-enabled device owners use voice searches to shop.

              Audio-only recordings can be used as additional content in your library, podcasts, and much more. Learn how with the following resources.

              There is no perfect formula for digital marketing – your strategy will depend entirely on your unique customers, market space, competitors, products or services, and more.

              Hopefully this gives you a good place to start building the digital marketing strategy that works for you!

              Featured Image: Bloomicon/Shutterstock

              Body Type Diet: The Ultimate Guide

              Ectomorph

              If you’re an ectomorph, you might wonder about the ideal diet for your body type. Ectomorphs have a thin body type and find it difficult to gain weight. They also have a fast metabolism, which makes it even more difficult to retain an ideal body weight.

              There are a few things that ectomorphs should keep in mind when choosing a diet. First, ectomorphs need to make sure they’re eating enough calories. It’s easy for ectomorphs to undereat, which can lead to health problems down the road. Second, ectomorphs should focus on getting plenty of protein. Protein is essential for muscle growth, and ectomorphs need to pack on muscle mass with any possible means to achieve an ideal shape. Last but not least, ectomorphs should snack often. Since they have such high metabolisms, they need to eat more frequently than other body types in order to keep their energy levels up.

              The bottom line is that there’s no “perfect” diet for ectomorphs. However, by following these guidelines, you can create a diet that will help you reach your goals and stay healthy at the same time.

              Mesomorph

              A mesomorph is an ectomorph with more muscle and less fat. A muscular build, broad shoulders, and a narrow waist characterize them. Mesomorphs are the body type most likely to be successful in bodybuilding.

              Mesomorphs tend to have an easy time gaining and losing weight. They can put on muscle quickly, but they must be careful not to overdo it, as they can also put on fat easily. Mesomorphs should focus on moderate-intensity exercises and eat a balanced diet.

              If you’re a mesomorph, you’re lucky — you have the ideal body type for bodybuilding. You have all the ingredients for success with your naturally muscular build and broad shoulders. But even though you may have an easier time than other body types when packing on muscle, you still need to be careful not to overdo it. You can easily put on too much fat if you’re not careful.

              The key for mesomorphs is to find the right balance between exercise and diet. You need to exercise enough to stimulate muscle growth but not so much that you end up burning off all your hard-earned muscle. And while you may be able to get away with eating more calories than other body types, you still need to make sure that most of those calories are coming from healthy sources. A diet that’s too high in fat or sugar will sabotage your efforts in

              Endomorph

              If you’re an endomorph, you tend to have a higher body fat percentage and a larger build, and you might find it harder to lose weight and may struggle with cravings. While endomorphs can still enjoy a healthy diet, they need to keep certain things in mind.

              Here are some tips for eating if you’re an endomorph

              Limit your intake of simple carbohydrates like sugar and white flour. Simple carbs can lead to weight gain, so eating complex carbs like whole grains is best.

              Increase your protein intake, which helps you feel fuller and longer and can help boost your metabolism. Lean meats, fish, tofu, and legumes can be promising to meet your protein requirement.

              Watch your portion sizes. Endomorphs tend to gain weight easily, so it’s important to be mindful of how much you eat. Try using a smaller plate or bowl to help control portions.

              Make sure to get enough exercise. Exercise helps boost metabolism and can help burn more calories throughout the day. If you have an Endomorphs body, you need moderate-intensity exercise for at least thirty minutes daily.

              How to Determine Your Body Type

              There are different ways to determine your body type; the most accurate way would be to consult a professional. However, there are also some DIY methods that you can use to get an idea of your body type.

              One way to determine your body type is by looking at yourself in the mirror. Take a close look at your overall shape and size. Are you more round or more straight? Do you have a lot of curves, or are you more straight up and down? This can give you a good idea of which category you fall into.

              Another way to determine your body type is by taking measurements of your waist, hips, and bust. Again, this will help you to figure out which category you fall into. Here are the general guidelines −

              If your waist is larger than your hips and bust, you are pear-shaped.

              If your hips are larger than your waist and bust, you are apple-shaped.

              If your bust is larger than your waist and hips, you are hourglass-shaped.

              The Best Diet for Your Body Type

              If you’re anything like the average person, you’ve probably wondered at some point what the best diet for your body type is. And with all of the different diets, it could be difficult for a newbie to know where to start.

              But don’t worry; we’ve got you covered. In this section, we’ll take a look at the different body types and what kind of diet is best for each one. So, without further ado, let’s get started.

              Exercise for Your Body Type

              If you want to maximize your workout results, it’s important to tailor it to your body type. Find out your type and how to work out for your body type below.

              Ectomorphs

              Ectomorphs are typically thin and have trouble gaining weight, and they may have a high metabolism and find it hard to put on muscle. When working out, ectomorphs should do more compound exercises to work on multiple muscle groups at once. They should also focus on gradually increasing their weight to avoid injury.

              Endomorphs

              Endomorphs are typically larger-boned and have more body fat than ectomorphs. They may find it difficult to lose weight, even with diet and exercise. When working out, endomorphs should focus on HIIT (High-Intensity Interval Training) workouts that help them burn fat. They should also focus on compound exercises that target multiple muscle groups at once.

              Mesomorphs

              Mesomorphs are in-between ectomorphs and endomorphs. They tend to be naturally muscular with a medium build. When working out, mesomorphs can benefit from both HIIT workouts and weightlifting workouts. They should mix up their routine to avoid plateauing.

              Conclusion

              We hope this guide to the body type diet has been helpful in introducing you to a new way of thinking about your health and nutrition. By taking into account your physical characteristics, lifestyle, and goals, the body type diet can be an effective tool for creating a sustainable healthy eating plan that works with your unique needs. It is important to remember that everyone’s journey will look different when it comes to finding success with their diets, so don’t get discouraged! Developing good habits takes time, but by following our tips and tricks, you can achieve great results in no time.

              The Data Science Behind Ipl

              This article was published as a part of the Data Science Blogathon.

              Introduction

              IPL, I am sure that, things like fun, entertainment, and sports have come to your mind. I am sure that you never relate science and education with IPL, but even you must be surprised to know that science play a big role in things like IPL. How a winning IPL team can be formed by spending the least possible through Data Science. Let’s find out in today’s article.

              In this article, I will explain the data science behind IPL and introduce an interesting career option behind it. Let’s start with the basic “What is Data🤔?”.

              What is Data?

              Data is basically information about anything. For example the no. of fruits on a tree or the flavor of your ice cream or even the no. of stars in the universe or how much % of peoples like the government. All of this is nothing but data. There is an immense amount of data all around us in our lives, but simply having data around us is of no use to us. It is important for us to know what data is useful and what data should analyze and how be recognizing patterns, we can make use of that data. Let’s think, What are we going to do by counting the no. of leaves on the tree? What use would that be😅?  It is useless data and is of no use to us. But the % of peoples that favor the government is useful data. It would be useful in politics. It can help the government understand what they should change and how they can transform themselves. This data would come in handy during the elections but it isn’t sufficient to simply record that data if you don’t analyze it, compare it, and improve it. The recording, studying, and observing data and then using it to arrive at the decision, is called Data Science.

              Using Data Science, we interpret any data and derive useful information from it, and use it in our decision-making process. It is possible to use Data Science in any aspect of life.

              In cricket and IPL, Data Science is used in a somewhat unique and interesting manner. In 2008, IPL came, which completely revolutionized the cricket world, because before IPL never had such an immense amount of money invested into cricket. Considering the auction 2023, totaled ~400 crore INR spent on the players. So much money is being spent in IPL. Data Collection and Data Analysis in IPL has breached the next level, because as IPL spending lot of money on players, it has become necessary for IPL teams to find out that, “Should they spend on a particular player or not?” or “How valuable is the player going to be for the team?” 

                                                 How should they judge in detail, “Which player should they buy and which one they shouldn’t it?”, “How much money should be spent on which player?” or “What are the values of the different players?”. 

              You will not believe that, but IPL teams have started hiring proper companies who are experts in such Data Analysis. Performance Analytics Companies that analyze how good players are, and develop strategies for that players. These Data Analysis companies analyze data about players in detail to understand who is good at what aspect. In IPL a metric that they use, is MVPI or The Most Valuable Player Index, which is a weighted composite score of the different attributes of a player.

              Let’s see some of the Batsman Metrics : 

              I. Hard-hitting Ability: How many sixes and fours a batsman scores, the following equation is used.

              Hard-hitting Ability = (Fours + Sixes) / Ball played by batsman

              How many fours and sizes has batsman hit in his IPL career divided by the no. of ball he played. This calculates the hard-hitting ability of the players.

              II. Finishing Ability: Not out innings divided by the total innings played.

              Finishing Ability = Not out innings / Total innings played.

              III. Consistency of Player: Total Run / No. of times out.

              IV. Running b/w the wickets: (Total run – (Fours + Sixes)) / (total ball played – boundry balls).

                                                                If this fourth metric is better in batsman than the hard-hitting metrics, then you can easily guess that he is not good at hitting boundaries but is good at getting singles, twos, and threes on other balls.

              Similarly, some Bowling Metrics are : 

              I. Economy: Run scored / (No. of ball bowled by bowler / 6).

              II. Wicket taking ability: No. of balls bowled / Wicket taken.

              III. Consistency: Run conceded / Wicket taken.

              IV. Crucial Wicket Taking Ability: No. of times four or five-wicket taken / No. of inning played.

              This whole data help us to understand the weak and the strong area of different players, whether a player is good at hitting boundaries or at the running between the wickets, whether a bowler performs better against left-handed batsman or right-handed batsman, whether a batsman perform better against spinner or fast bowlers. Analysis can also work out in “What Stadium and in which weather does a player performs better?”

              In one interview, Virender Sehwag encapsulated the importance of Data Science very nicely. He said that “Every game you play, they will record your good performance, your bad performance, you played against which bowler, you scored against which team and which bowler, and the whole data will easily show you that you are good against Pakistan but you’re not good performed against Bangladesh, you’re good against South Africa but you’re not good against England. In 2003 when our computer analytics guy come in and he showed me videos and different kinds of data analysis, I got amazed!!!😲”.

              During the auctioning of players, the IPL teams that do not have a lot of money would definitely want to know whether the player that they are buying is worth the money, they spent for their team or not. Because more often it happens that the most expensive player in IPL auction is not the top-performing player of IPL always. The best example of this would be the first season of IPL that is 2008 where Rajasthan Royals had lifted the trophy and Rajasthan Royals was one of the cheapest teams in that season. It means that the money that they had spent on the players was way lesser than what others teams had spent. It was one the cheapest team, but they still won the IPL😎. Check out the IPL 2008 auction list below.

              Auctioning of players and forming teams is not just one area where Data Science is used, after this Machine Learning techniques are also used to predict the match results. Different models are created with the help of programming and computers in which, inputs like the position of a player, location of the match, the weather of the day, etc are all added as variables and on the basis of previous matches, these models predict the future results of the matches. If you provide the data input of the previous matches, such as the venues of the matches as well as teams that played, players that were present as well as the type of players that were present, then in the future it could be predicted the result of the matches presently being played.

              Obviously, it will not be 100% accurate but it could be quite useful. Programming languages like ‘Python’ and libraries like ‘Pandas’, ‘Matplotlib’, and ‘Seaborn’ are used for data preprocessing and data analysis.

              Some Interesting Analysis⚡

              I. One of the analyses that analyzed that IPL matches between 2008 – 2023, reveals that Eden Garden and M Chinnaswamy Stadium are the best venues for chasing the score, so if a match is being held in either of these two venues and a team wins the toss, fielding would be a better option. Let’s do the same analysis on IPL matches. You can download the dataset from here. Here we are using IPL Matches  2008-2024 dataset.

              Dataset Description: It contains a total of 17 columns. Let’s take a look at them.

              Attribute Information :

              id

              city

              date

              player_of_match

              venue

              neutral_venue

              team1

              team2

              toss_winner

              toss_decision

              winner

              result

              result_margin

              eliminator

              method

              umpire1

              umpire2 

              In the code section, I will directly show the main part of the code. To know detailed descriptions you can directly download the Jupyter Notebook.

               Let’s load the libraries:

              import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline

              Read the dataset:

              Let’s remove the useless columns from the dataset:

              df.drop(labels = ['id', 'date', 'player_of_match', 'neutral_venue', 'result', 'result_margin', 'eliminator', 'method','umpire1', 'umpire2'], axis = 1,inplace = True)

              Let’s analyze the better option after winning the toss:

              match_win_target = match_loss_target = match_win_chassing = match_loss_chassing = 0 for i in range(len(df)) : if df.toss_decision.iloc[i] == 'bat' : # target diya if df.toss_winner.iloc[i] == df.winner.iloc[i] : match_win_target += 1 else : match_loss_target += 1 else : # target chase kiya or Fielding li if df.toss_winner.iloc[i] == df.winner.iloc[i] : match_win_chassing += 1 else : match_loss_chassing += 1 print('{} times captain choose batting option and win the match.'.format(match_win_target)) print('{} times captain choose batting option but loose the match.'.format(match_loss_target)) print('{} times captain choose fielding option and win the match.'.format(match_win_chassing)) print('{} times captain choose fielding option but loose the match.'.format(match_loss_chassing))

              Let’s create a specific column and describe, how the team wins the match(by giving the target or by chasing the score):

              for i in range(len(df)) : if df.toss_decision.iloc[i] == 'bat' : if df.toss_winner.iloc[i] == df.winner.iloc[i] : # captain choose batting option and win the match then it will count as target. df['target'].iloc[i] = 1 else : # captain choose batting option and loose the match then it will count as chasing. df['chase'].iloc[i] = 1 else : if df.toss_winner.iloc[i] == df.winner.iloc[i] : # captain choose fielding option and win the match then it will count in chasing. df['chase'].iloc[i] = 1 else : # captain choose fielding option and loose the match then it will count in target. df['target'].iloc[i] = 1

              Let’s extract some more useful information from the data:

              targetlist = [] chaselist = [] for i in top15_stadium : print('Analysis on "{} Stadium"'.format(i)) x = np.sum(df[df.venue1 == i].target) y = np.sum(df[df.venue1 == i].chase) print(x, 'times team gave good target and win the match.') print(y, 'times team easily chase the score and win the match.') targetlist.append(x) chaselist.append(y) print()

              Let’s visualize the above data for better understanding:

              top15_stadium = ['Eden Gardens, Kolkata', 'Feroz Shah Kotla, Delhi', 'Wankhede Stadium, Mumbai', 'Rajiv Gandhi International Stadium, Uppal, Hyderabad', 'M Chinnaswamy Stadium, Bangalore', 'MA Chidambaram Stadium, Chepauk, Chennai', 'Sawai Mansingh Stadium, Jaipur', 'Punjab Cricket Association Stadium, Mohali, Chandigarh', 'Dubai International Cricket Stadium, Dubai', 'Sheikh Zayed Stadium, Abu Dhabi','Maharashtra Cricket Association Stadium, Pune', 'Punjab Cricket Association IS Bindra Stadium, Mohali, Chandigarh', 'Sharjah Cricket Stadium, Sharjah', 'Dr DY Patil Sports Academy, Mumbai', 'Subrata Roy Sahara Stadium, Pune'] data = {'target': [30, 34, 36, 27, 26, 35, 15, 15, 19, 13, 7, 9, 7, 7, 11], 'chase': [47, 39, 37, 37, 37, 22, 32, 20, 14, 16, 14, 12, 11, 10, 6]} df1 = pd.DataFrame(data,columns=['target', 'chase'], index = top15_stadium) df1.plot.barh(figsize = (15,10)) plt.style.use('seaborn-bright') plt.title('Top-15 Stadiums') plt.ylabel('Stadiums') plt.xlabel('No. of Matches Win') plt.xticks(np.arange(0, 54, 3)) plt.show()

              The above plot reveals that “How many times team give good target or easily chase the target in the particular stadium.” Let’s look at the horizontal bar of “Eden Garden, Kolkata” stadium, this bar reveals that more than 45 times easily chase the score and win the match and approx 30 times the team gave good target and win the match. From this, we can easily conclude that this stadium is better for chasing the score, so if a match is being held in this venue and a team wins the toss, fielding would be a better option. Similarly, we can easily analyze the whole plot.

              Let’s convert the above data in terms of % for better understanding:

              target1 = [] chase1 = [] for i in top15_stadium : print(i) x = np.sum(df[df.venue1 == i].target) y = np.sum(df[df.venue1 == i].chase) total = x + y t = ((x / total) * 100) c = ((y / total) * 100) target1.append(round(t, 2)) chase1.append(round(c, 2)) print('{:.2f}% probablity that if you choose to bat, then you will win the match.'.format((x / total) * 100)) print('{:.2f}% probability that if you choose to field, then you will win the match.'.format((y / total) * 100)) print()

              Let’s visualize the above data for better understanding:

              top15_stadium = ['Eden Gardens, Kolkata', 'Feroz Shah Kotla, Delhi', 'Wankhede Stadium, Mumbai', 'Rajiv Gandhi International Stadium, Uppal, Hyderabad', 'M Chinnaswamy Stadium, Bangalore', 'MA Chidambaram Stadium, Chepauk, Chennai', 'Sawai Mansingh Stadium, Jaipur', 'Punjab Cricket Association Stadium, Mohali, Chandigarh', 'Dubai International Cricket Stadium, Dubai', 'Sheikh Zayed Stadium, Abu Dhabi','Maharashtra Cricket Association Stadium, Pune', 'Punjab Cricket Association IS Bindra Stadium, Mohali, Chandigarh', 'Sharjah Cricket Stadium, Sharjah', 'Dr DY Patil Sports Academy, Mumbai', 'Subrata Roy Sahara Stadium, Pune'] data = {'Bat_first': target1, 'Field_first': chase1} df2 = pd.DataFrame(data,columns=['Bat_first', 'Field_first'], index = top15_stadium) df2 df2.plot.barh(figsize = (15,10)) plt.style.use('seaborn-bright') plt.title('Top-15 Stadiums') plt.ylabel('Stadiums') plt.xlabel('Probability to win') plt.xticks(np.arange(0, 75, 3)) plt.show()

              Here the above plot reveals that “What is the probability of winning if you choose to bat first or field first in top-15 stadiums.” Let’s look at the horizontal bar of “Subrata Roy Sahara Stadium, Pune”, this bar reveals that in this stadium if you choose to bat first after winning the coin toss then more than 63% of chances that you will win that match, on the other hand, if you choose to field first then there is only 35% chance that you will win the match.

              From this, we can easily conclude that this stadium is better for giving the target to the opposition team, so if a match is being held in this venue and a team wins the toss, batting would be a better option. Similarly, we can easily analyze the whole plot. For more details, you can directly download the jupyter notebook.

              II. Another analysis took into account the batting average and strike rate of all the IPL players, and concluded that all the players below the age of 35 had a batting avg. of 24.51 and an avg. strike rate of 126.84 and on the other hand players above 35 years had an avg. strike rate of 112.1 much lesser and batting avg. of 21.34. This show that younger player should be preferred if a team has to improve its performance.

              The final conclusion🤩

              You might wonder with all the data we are analyzing, how much of this data and its analysis is actually useful? and How much of it is random? Previously we talk about which stadium is better for chasing, and what will happen in what kind of weather. It could be that all of these are random things. It could be coincident that, it was easier to chase and win in that particular stadium. I am sure that this question definitely pops up in your mind. It is a legitimate one because, in a lot of data analysis, randomness is also taken into account. If there is randomness, it could be put into the algorithms so that it also be accounted for while analyzing the data. And this generates even more accurate results.

              Mind-Blowing Fact🤯 : In fact, when Kolkata Knight Riders won the trophy in 2014 the Auction Analysis of SAP got a lot of credit for their victory. Kolkata Knight Riders had hired that SAP Data Analysis Company to analyze the data and to explain in detail “What kind of team should be formed, Which player should be sent, where, when, and what should the strategies be?”. On the basis, of this analysis, KKR finally won the trophy.

              Let’s Wrap Up!

              But in the end, I will only like to say that this is all a game of probabilities, you can definitely increase your chances of winning the match by taking all those things into account but there’s never a 100% guarantee. Because after all, the IPL players are human beings😁, not machines.

              Golden Words😁❕

              If you really enjoyed it, don’t forget to share it with your friends. If you have any query don’t hesitate to leave a response below. You can also connect me on LinkedIn. And finally, … it doesn’t go without saying😉…

              Thanks for reading!

              -ronyl

              The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. 

              Related

              The Ultimate Guide To Gdpr & Facebook Messenger Marketing

              When you venture into new territory you start by learning the basics. When it comes to Facebook Messenger marketing – it’s all new territory.

              Add to that the changes that went into effect in May, care of the European Union’s General Data Protection Regulation (GDPR).

              GDPR outlines rules that you follow to ensure you remain compliant or face fines that start at €10 million.

              So consider this your go-to guide for Facebook Messenger marketing and GDPR issues.

              After we review the rules of the road, we’ll take a little test drive.

              I’ll show you how to unlock powerful new marketing achievements with Facebook Messenger marketing while remaining fully GDPR compliant.

              The Basics of the General Data Protection Regulation in the European Union

              Let’s start with a quick review of the basics of the GDPR and the rules for marketing through Facebook Messenger.

              This ensures you can stay on the right side of the law and that you don’t violate any Facebook requirements.

              There’s a lot of information covering the details of the GDPR, some of it easier to digest than others.

              Luckily, the basic concept is fairly simple.

              To meet the new standards, your communications must be:

              Consensual.

              Secure.

              Removable!

              This means you are required to obtain consent to receive messages using an opt-in model.

              It also means that you’re agreeing to keep all customer personally identifiable information safe.

              And, super important: you have to be able to honor all requests to have customer data removed.

              A good Facebook Messenger marketing platform will have all the above functionality built in.

              The GDPR applies to every citizen in the EU, regardless of where your business is based.

              If you do business in the EU or have personal information about EU citizens, then these rules apply to you.

              If a little more background on GDPR would be helpful, check out my top picks for helpful GDPR guidance:

              Facebook Messenger Rules for GDPR Compliance

              Facebook also sets rules and issues guidelines that apply to Messenger marketing.

              So keeping them in mind is crucial if you want to make the most of the platform without getting in trouble with the social media giant.

              Here are four Messenger marketing rules you must follow:

              1. Get User Opt-In

              As Facebook has said, you must give people control over the messages they receive.

              It’s easy to create an opt-in chatbot where your contacts confirm what kind of communications they’ll receive.

              2. Follow Facebook Messenger’s 24+1 Rule

              When Facebook released its API for Messenger, it established some ground rules for businesses to prevent spam on the platform.

              One of those rules is called the 24+1 rule.

              A business page can send a new contact any number of messages during the first 24-hours of contact. Once that period is over, the page can send one promotional message after that.

              3. Utilize Subscription Messaging

              When sending automated messages in Facebook Messenger, you specify the purpose of the message, choosing from around 30 different purposes like:

              Event reminder.

              Appointment update.

              Shipping update.

              Promotional message.

              Non-promotional subscription update.

              Catch that non-promotional subscription purpose?

              Businesses can apply to the powers of Facebook Messenger for subscription messaging status, and when approved, can send opted-in subscribers updates.

              There’s actually a December 31 deadline to apply for subscription messaging status.

              4. Always Let Folks Know How to Unsubscribe by Typing ‘Stop’ Any Time

              If you use MobileMonkey (disclosure: my employer), you don’t have to stress about managing the unsubscribe process; you get a comprehensive solution right off the shelf.

              Otherwise, you’ll need to develop that feature and ensure subscribe requests are managed in a timely fashion.

              Demo of a Facebook Messenger & GDPR Compliant Chat Blast

              The easiest way to understand the steps you need to take to remain compliant is to see its creation in action.

              Let’s look at what building a GDPR and Facebook Messenger compliant chat blast looks like in practice, using the MobileMonkey chat blaster tool.

              Step 1: New Contacts Are Made When Someone Messages Your Facebook Business Page

              When someone sends your page a message, they become a customer profile.

              Facebook’s API provides standard info about all your new Messenger contacts:

              First and last name.

              Gender.

              Locale.

              Time zone.

              When they became a contact.

              Because Facebook’s rule is that you can send an automated message to your new contacts, use the opportunity to invite them to get subscription messaging updates.

              Step 2: Welcome New Contacts with an Opt-In Invitation

              Here’s a quick look at our opt-in process. It’s the same steps as you’d take to create a free Facebook Messenger chatbot with some special notes.

              You’re going to invite people to sign up for updates, giving them options to tap a button for “yes” or “no”.

              If the user taps, “Yes, sign me up!” they have opted in, allowing you to send additional content.

              Here’s what our Messenger opt-in page looks like in chat:

              When you create chat messages in MobileMonkey, it’s similar to crafting an email using MailChimp. You use a visual content builder and add content with widgets.

              The available widgets allow you to add different kinds of content with a simple drag-and-drop approach, letting you reorder the content with a drag and swipe.

              The widget you use to ask the user a question and supply them buttons for their response is the Quick Question:

              Quick Question is my favorite widget because it encourages user interaction – easy responses via the tap of a button.

              Take note of the attribute “BLOGSUBSCRIPTION” that saves to contact profiles. We’ll use that attribute to create the custom audience segment.

              Step 3: Create a Custom Audience Segment of Subscribers to Send Messages To

              Now, it’s time to curate a custom Messenger audience. This creates a contact list of message recipients.

              Use the Audience builder to filter the contacts using the custom variable that identifies subscribers who chose to opt-in:

              From the audience builder, create a new audience and filter your contacts with the attribute of made in step 2 and the value “yes, sign me up!” that reflects an opt-in subscriber.

              Step 4: Use the Chatbot Builder to Create Content to Chat Blast, Including Unsubscribe Language

              For the next step, we’re going to send a message to subscribers that adheres to Facebook Messenger subscription messaging and GDPR guidelines.

              Use the chatbot builder and, like when you created the opt-in page, create a page using widgets.

              Within a text widget, you can personalize your message by addressing your contact by name by inserting the “first name” dynamic attribute:

              Be sure to include clear reminders on how the individual can unsubscribe as well:

              Check it out here for an example chat blast that’s personalized with the customer name and includes a statement on how to unsubscribe.

              How does unsubscribe work in Facebook Messenger?

              MobileMonkey handles it for you. Don’t worry about programming anything.

              Just let people know that they can type “stop” any time to unsubscribe from messages.

              Step 5: Power on the Chat Blaster, Sending the Blast to Your Select Audience

              Create a new chat blast from the chat blaster, give it a descriptive name and use the drop-down selectors to choose the audience you’re sending the blast to and the page of content you’re sending.

              All your audiences and pages will be available to pick from a simple and searchable drop-down menu.

              Here’s an example of what a Messenger chat blast set up looks like:

              You need to identify the purpose of the content.

              Facebook requires you to provide the purpose so messages can follow rules surrounding promotional and non-promotional messaging.

              There are more than 20 available categories, so you would choose the one that best aligns with the message:

              Now you can see how straightforward it is to create a chat blast.

              It only takes a couple of minutes and zero coding on your part.

              The Ultimate Engagement Marketing Channel: Facebook Messenger

              When it comes to engagement, Messenger is hard to beat.

              Because people message your page first, people are open to receiving follow up content. That’s one reason engagement is so high.

              Also, the unsubscribe process is quick and reliable.

              When a person gets a spam email, there’s no telling if the sender will honor a removal request.

              On Facebook Messenger, all unsubscribes are overseen and managed by the software ensuring your business is compliant.

              Keep in mind these simple tips and your messages will pass Facebook Messenger and GDPR muster, too.

              More Facebook Marketing Resources:

              Image Credits

              All screenshots taken by author, September 2023

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