The fascinating world of data and AI has brought forth many scientific tools, algorithms, processes, and knowledge extraction systems to identify meaningful patterns from structured as well as unstructured data. The boom in data analytics in the last couple of years is only growing and will reach the next level with so many innovations in the artificial intelligence domain.
If Data Analytics is something you fancy and want to get a solid foundation on this topic, then you must have a portfolio of data analytics projects to showcase. If you are wondering how to start with data analytics, we have here data analytics project ideas that are good for beginners as well as those who are in intermediate or higher levels. If you are a student, then our ideas could also be used for data analytics projects for students.
Data analytics projects (Easy, Medium, Hard)
To get started with data analytics project topics, you would first need to understand what level you are comfortable in and then decide whether you want to get on with data analytics projects for beginners, intermediate, or higher levels. Let us take a look at what it entails to do a project in these 3 levels:
Beginner level – If you are someone who is just starting with data analytics, you must go through the data analytics project examples in the beginner section. These projects do not employ heavy application techniques, and their simple algorithms would let you move forward smoothly.
Intermediate – Here, medium to large data clusters are taken and you need to have a sound foundation of data mining projects along with machine learning techniques. If this is something you are well-versed with, then you can work on the projects outlined in the intermediate section.
Expert – This section is for industry experts where neural networks and high-dimensional data are worked with. If you have the blend of creativity and expertise required for such projects, then the data analytics mini project in the advanced section is for you.
Easy or beginner level projects
Intermediate level projects
Movie recommendation system – The concept of recommending movies is complex and is based on the abstract click method. It requires a huge implementation of machine learning and accessing humungous datasets that include users’ movie browsing history, preferences, etc. You would need to use collaborative filtering to get a hang of user’s behavior and the R Framework along with the MovieLens dataset is a good fit for such projects. To channel through the datasets, you could make use of surprise model selection and matrix factorization too. Brands like Netflix use this method and it is a lot of grueling work even for industry experts.
Credit Card Fraud Detection – Another data analytics project in r will need you to work with decision trees, gradient boosting classifiers, logistic regression, and artificial neural networks. By using the card transactions dataset, you can classify transactions on a credit card into fraudulent or genuine categories.
Customer Segmentation – This is one of the most popular data analytics projects for companies as they need to create various groups of customers at the beginning of any of their campaigns. This project is an implementation of unsupervised learning and uses clustering to identify different segments of customers so that companies can target the customer base they need to. Customers are divided into groups based on age, gender, preferences, spending habits, etc. This is done to market to each group more effectively. You can use K-means clustering and visualize gender and age distributions.
Become a Business Analytics expert with MAHE
MAHE, through Online Manipal, provides a two-year MSc in Business Analytics program to educate aspiring business analysts. There is also the Online MBA – Business Analytics which is another master degree on rapidly expanding subject of business analytics, and it is designed for professionals who want to advance their careers in it. Students will learn how to use data to create company strategies, solve organizational problems, and provide accurate, data-driven customer service during the program.
Information related to companies and external organizations is based on secondary research or the opinion of individual authors and must not be interpreted as the official information shared by the concerned organization.
Additionally, information like fee, eligibility, scholarships, finance options etc. on offerings and programs listed on Online Manipal may change as per the discretion of respective universities so please refer to the respective program page for latest information. Any information provided in blogs is not binding and cannot be taken as final.
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