Post Graduate Programme in Data Science & AI (Executive)

In this assignment, you will work for a consumer finance company which specialises in lending various types of loans to urban customers, and use EDA to analyse the patterns present in the data. This will ensure that the applicants that are capable of repaying the loan are not rejected. 

In this assignment, you will work on a movies dataset using SQL to extract exciting insights about popular films and the factors that drive a film.

Build a regression model to understand the factors on which the demand for bike sharing systems vary on and help a company optimise its revenue.

In this case study, the company requires you to build a machine learning classification model wherein using some demographics and behavorial data of the leads (potential buyers), it will be able to identify the ones most likely to convert.

Understand how a big data project in the industry is taken up and solved through a comprehensive big data case study on cloud.

Solve intensive programming questions in a competitive lab environment to showcase and evaluate your SQL and Python coding skills.

Telecom companies often face the problem of churning customers due to the competitive nature of the industry. Help a telecom company identify customers that are likely to churn and make data-driven strategies to retain them from the perspective of a business analyst.

Understand the factors that drive the Airbnb business and help the team leads tackle problems and bottlenecks through extensive data analysis on UI tools.

Understand how data projects are taken up in the industry and implement the steps required to successfully manage and complete a data project.

In this assignment, you will build a complete neural network using Numpy. You will implement all the steps required to build a network - feedforward, loss computation, backpropagation, weight updates etc. You will use the MNIST dataset to train your model to classify handwritten digits between 0-9.

Telecom companies often face the problem of churning customers due to the competitive nature of the industry. Help a telecom company identify customers that are likely to churn and make data-driven strategies to retain them from the perspective of a data scientist.

Use the techniques such as POS tagging and Dependency parsing to extract information from unstructured text data.

Perform MapReduce Programming in a big data environment on a large dataset.

Make use of Sqoop, Redshift & Spark to design an ETL data pipeline.

Build an end-to-end real-time data processing application using Spark Streaming and Kafka.