Top 5 Data Science Projects with Source Code to kick-start your Career

Are you a Data Science aspirant and looking forward to some challenging and real-time Data Science projects? Then you are at the right place to gain mastery in the field of Data Science. In this article, we will discuss the best Data Science projects that will boost your knowledge, skills and your Data Science career too!!

These real-world Data Science projects with source code offer you a propitious way to gain hands-on experience and start your journey with your dream Data Science job. Now let’s quickly jump to our best Data Science project examples with source code.

5 Best Data Science Projects for Beginners

Below are the top Data Science project ideas to master the technology:

• Movie Recommendation System Project
• Customer Segmentation using Machine Learning
• Sentiment Analysis Model in R
• Uber Data Analysis Project
• Credit Card Fraud Detection Project in R

1. Movie Recommendation System Project

The aim of this interesting Data Science project including code is to build a recommendation system that recommends movies to the users.

Let’s understand this with an example. Have you ever been on an online streaming platform like Netflix or Amazon Prime? If yes, then you must have noticed that after some time these platform starts recommending you different movies and TV shows according to your genre preference. This project in R programming is designed to help you understand the functioning of how a recommendation system works.

Check detailed article & implementation with project code – Data Science Movie Recommendation Project in R

2. Customer Segmentation using Machine Learning

Customer segmentation is one of the most essential applications for all customer-facing industries (B2C companies). It uses the clustering algorithm of Machine Learning that allows companies to target the potential user base and also they can identify the best customers.

It uses clustering techniques through which companies can identify the several segments of customers allowing them to target the potential user base for a specific campaign. Customer segmentation also uses K-means clustering algorithm which is essential for clustering unlabeled dataset.

Wait! Explore complete illustration & implementation of project with code – Customer Segmentation Data Science Project using Machine Learning

3. Sentiment Analysis Model in R

Almost every data-driven organization is using the sentiment analysis model to determine the attitude of its customers toward the company products.

In brief, it is the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the consumer’s attitude towards a particular product or topic is positive, negative, or neutral. You will have to utilize the tiny text package to analyze the data and give scores to the corresponding words that are already present in the dataset.

Don’t forget to carry out this project by learning its implementation – Sentiment Analysis Data Science Project in R

4. Uber Data Analysis Project

Data is the oil for uber. With data analysis tools and great insights, Uber improve its decisions, marketing strategy, promotional offers and predictive analytics.

With more than 15 million rides per day across 600 cities in 65 countries, Uber is growing rapidly with Data Science starting from data visualization and gaining insights that help them to craft better decisions. Data Science tools play a key role in every operation of Uber.

Wondering, how to execute Uber data analysis project? A perfect guide for you – Data Science Uber Analysis Project with R

5. Credit Card Fraud Detection Project in R

The credit card fraud detection project uses machine learning and R programming concepts.

The aim of this project is to build a classifier that can detect credit card fraudulent transactions using a variety of machine learning algorithms that will be able to discern fraudulent from non-fraudulent ones. Learn how to implement machine learning algorithms and data analysis and visualization to detect fraudulent transactions from other types of data from – Data Science Project on Credit Card Fraud Detection

We have discussed some real-time Data Science projects for resume building, now, we will learn about the programming languages that are essential for completing Data Science projects.

Programming Languages required in Data Science Projects

There are more than 250 programming languages known to the world, but we have to select the tools according to our project requirements. Some of the most popular programming languages (and tools/frameworks) that are commonly used in almost every Data Science projects are – R Programming, SAS, Python, SQL and many more.

Here is the list of top 6 programming languages used in the Data Science project that you must refer to before moving forward.

Moving ahead in Data Science projects article, now, it’s time to explore the steps to become a data scientist.

Steps to becoming a proficient Data Scientist

1. Master of Programming Skills – R, Python, and SAS are the most commonly used tools by the data scientists. Confused, from where to start? Explore R vs Python vs SAS for Data Science and choose the most suitable tool to start your Data Science learning.
2. Play with Data – This field uses scientific methods and algorithms. Apply this approach in processing, cleaning and verifying the data.
3. Good hands-on Machine Learning Skills – Data scientists generate insights using Machine Learning techniques.
4. The most important thing is Projects – Data Science projects play a vital role in boosting your Data Science career.

Now, let’s check the benefits of working on the above Data Science Projects for final year students.

Benefits of Working on Data Science Projects

• These data analytics projects for students offer you real-world experience to boost your career.
• After mastering the core concepts, you need to work on projects to implement your learning and to gain good confidence.
• You can showcase the projects in your resume (CV) and nowadays recruiters evaluate a candidate’s potential by his practical work.
• Working on a real-time project as a newbie is a good way to boost your knowledge and skills.

Summary

We have learned about the best 5 Data Science projects with source code for beginners and advanced learners. Now, the ball is in your court, start working on these projects with the help of source code in order to gain mastery in Data Science and get placed in your dream job!!

Don’t worry! We have the solution for that too. Here are the 60 Chances to Crack Data Science Interview and getting hired as a data scientist.