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Top 5 Data Science Projects For Beginners

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Any aspiring data scientist should have many data science projects as part of his/her CV. Interviewers evaluate your technical knowledge, not by degrees you possess but based on what you can do and bring value to the organization. Hence, it is essential to work on data science projects right from the initial stages of your studies. We shall now discuss some of the best data science projects for beginners. These projects can help you gain fundamental knowledge that can stand you in good stead later on in your career.

Different levels of data science projects

We shall classify data science projects as belonging to three levels

  • Beginner level – Naturally, these data science project ideas are reasonably easy to work with because you do not need to use any complex data science technique. Students in the elementary level can solve those using simple methods like classification algorithms or basic regression.
  • Intermediate level – Compared to the data science projects for beginners, these projects are more challenging. They contain data sets that require serious pattern recognition skills. You need to have an engineering background to understand and take on such projects. Machine learning projects form a vital part of such intermediate level data science projects.
  • Advanced level – As the name suggests, you need high levels of understanding to prepare such projects. It is best suited for people having adequate knowledge of data science aspects such as neural networks, recommender systems, and deep learning. These projects include high dimensional data as well. These data science project examples are creative and should form part of your CV when you graduate as a qualified data scientist.

We shall now discuss some of the simple but exciting data science projects for beginners.

Top 5 data science projects for beginners

1.    Iris Data Set

The credit for introducing this multivariate data set goes to a British biologist Ronald Fisher in 1936. It is a simple example of linear discriminant analysis. Edgar Anderson collected the data required for the quantification of the morphological variation of three related species of the Iris flower.

The dataset comprises of 50 samples from each of these three species of Iris, the Iris Setosa, The Iris Virginica, and the Iris Versicolor. The dataset involves measuring of four features from the sample, the sepal length, the sepal width, the petal length, and the petal width. Fisher used a combination of these four features to develop a linear discriminant model for distinguishing one species from the other.

The Iris data set is probably the easiest and most versatile dataset in pattern recognition literature. It is simple because the project involves the study of only 150 rows and 4 columns. The columns constitute the distinguishing features whereas the rows contain data from the 50 samples from each of the three species of Iris.

  2.    Loan Prediction Data Set

The Iris dataset is a straightforward data science project for beginners as it involves only 4 columns and 150 rows of data. Every problem in life would not be as simple. Let us now move one step ahead on the difficulty level and look at the Loan Prediction Data Set.

Insurance and banking companies make the maximum use of data analytics today. Banks have a variety of loan products with different eligibility criteria. Various factors go into deciding whether the applicant gets the loan or not. The customers need to provide personal details such as their gender, marital status, educational qualifications, income, existing liabilities, number of dependents, loan amount, credit history, and so on. The Loan Prediction Data Set considers 13 such common criteria with a primary objective of predicting whether the customer is eligible for the loan or not.

The target is the Loan Status. Naturally, you will find some columns without any data as it is common to have some missing values. It is a classification problem where different variables influence the outcome. For example, a person might have a good income but a bad credit history. Hence, there are fewer chances of getting the loan.

Similarly, a person can have a large number of dependents to support. All these factors play a crucial role in predicting the outcome of the loan application. You can utilize as many samples as you want. The more the number of samples you analyze, the better will be your loan prediction.

3.    The sinking of the Titanic

The Titanic Data Set is amongst the popular data science project examples. The project’s objective is to predict the survival of the passengers on board the RMS Titanic. You had the data of all passengers aboard the Titanic when it sank in the North Atlantic Ocean after colliding with a giant iceberg on a chilling 15th April night in 1912. Most of the data science universities have this project as one of their machine learning projects.

This dataset comprises 891 rows and 12 columns and provides an excellent combination of variables based on the personal characteristics of the passengers such as gender, age, class of ticket, and so on. One of the reasons for the loss of life was the inadequate numbers of lifeboats available on the ship at that time. There is an element of fortune as well. However, some groups of people had a greater chance of survival than the others. Such groups include women, children, and the passengers holding upper-class tickets.

This data science project requires you to analyze what sorts of people were more likely to survive. It also involves the use of machine learning tools for predicting which passengers survived the tragic accident. You need knowledge about binary classification, Python, and R-basics to solve this problem.

4.    Bigmart Sales Data or Walmart Sales Forecasting Data set

After banking and insurance industry, the retail sector has tremendous scope for use of data science. You need enormous analytical skills to optimize business processes. Every aspect of retail sales of giant retailers like Walmart needs data analytics for performing tasks like the placement of products, managing inventory, customizing offers, bundling or products, and so on.

The dataset provides historical sales data of a minimum of 45 Walmart stores, each of them having various departments. The project aims to predict the department-wise sale of each of these stores using the historical sales information spanning 143 weeks.

Usually, you find Walmart conducting promotional events before the major holidays like Christmas, Thanksgiving Day, and so on. The sales during such periods are naturally higher when compared to the regular weekly sales. Walmart offers various discounts as well during such markdown events. This difference in the prices and the sales levels adds a couple of layers of difficulty to the problem.

This problem is a regression analysis problem that requires analyzing data over 8523 rows of 12 variables.

5.    Boston Housing Data Set

The Boston Housing Data Set is another popular data science project for beginners. Compared to the tests described above, this project is a simple regression analysis problem. The US Census Service for housing in Boston, MA collected the data for a study aimed at ascertaining whether the availability of clean air influenced the market value of houses in Boston.

This project is a relatively simpler one because of the smaller database in comparison. This dataset seeks the discovery of ideal explanatory variables. The objective is to predict the median value of occupied homes in Boston.

As this dataset comprises of mere 506 rows and 14 columns, it allows you to attempt any data science analysis technique to solve the problem.

Inference

Any aspiring data scientist should be creative enough to prepare projects that can help him stand out from the intense competition. It is better to start practicing your projects from the initial stages itself. You need a lot of practice to excel in these projects. Therefore, starting early in your career can help a lot. One of the best ways to build a robust portfolio is to participate in data science challenges.

It is better for data science student to start with simple projects and proceed gradually towards the tougher ones. As you progress to the intermediate level, the difficulty quotient increases. The number of variables is directly proportional to the difficulty level. Thus, we can see from the above five projects, the Titanic dataset and the Walmart Sales Forecasting datasets are the most challenging. Similarly, the Iris dataset is the simplest one of all because it contains only four variables.

Conclusion

We have seen five simple but favorite data science projects for beginners. Every data science student must have encountered these projects at one point or the other during their study period. Regular practice can make you perfect and provide you with the required confidence to approach any data science project with ease. You can refer to the official website of Digital Vidya to learn more about data science and data analytics.

Today, almost all the businesses in the world employ data analytics strategies to stay ahead of the competition. Therefore, you have a tremendous demand for data scientists. These projects can go a long way in beefing up your CV thereby helping you in your career prospects.

Srinivasan, more popularly known as Srini, is the person to turn to for writing blogs and informative articles on various subjects like banking, insurance, social media marketing, education, and product review descriptions. Writing articles on digital marketing and social media marketing comes naturally to him. Similarly, he has the capacity and more importantly, the patience to do in-depth research before committing anything on paper.

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