If you wish to build a career in or pivot it towards Data Science, Machine Learning, and Artificial Intelligence and can invest 11 months to master it, this program is for you. Blending industry and academic material, it is the only comprehensive and integrated program of its kind. It is the perfect program for students and working professionals looking to add Data Science and related skills to their profiles.

Most of the existing programs do not integrate all three aspects (Data Science, Machine Learning, Artificial Intelligence) for you to become a practitioner in the field. Additionally, nearly none of the other programs cover what this program does, what we like to call the edges of Data Science – from data engineering to deployment and prototyping.

Most programs out there only focus on building models in the development environment, which is not of much value when it comes to implementation. This program is deeply integrated with the Data Science MicroMasters® Program from MIT, which provides you with deep knowledge that is unparalleled in online or in-person learning. This is augmented by extensive hands-on industry case studies, real-life data sets, Big Data Engineering, and MLOps.

Nearly all Data Science and related programs showcase case studies, data sets, and analysis from a developed world context and viewpoint. While this is essential for learning, it does not allow you to understand the problems of data as they pertain to the Indian business context.

Our program contains an entire module dedicated to understanding the technology, its applications, and its implications in the Indian context. We believe this is essential for anyone interested in working with data involving Indian companies and the Indian government or government agencies.

1. Neural Networks
The program comprehensively covers Artificial Neural Networks (ANN) using Multi Layered Perceptrons for classification problems, Convolutional Neural Networks (CNN) for video analytics, Recurrent Neural Networks (RNN) for NLP and time series data, with relevant libraries (Keras, Tensorflow and PyTorch) and hands-on cases.
2. Probability and Statistics
The foundation of all ML and AI is in probability/statistics, optimization and linear algebra. Most so-called ‘data scientists’ do not understand these basics and thus try to implement libraries in a GIGO (Garbage-In-Garbage-Out) mindset.

Our program covers these topics in detail but not as separate ‘theory’ material. These are treated as concepts and learning that we connect back closely to actual models and real-world applications.

3. Python and R
Python – the fastest growing software in the world is our primary tool for the program. You can also access most ML-AI libraries (Tensorflow, Keras and Pytorch) through Python.

We also teach R because it is a software that is currently being used by many organizations. Additionally, R serves as a powerful tool for statistical analysis and visualization.

4. Tensorflow or PyTorch
We believe that as future creators you need to be tools agnostic. The program helps you master both Tensorflow and PyTorch because each comes with its own set of pros and cons. Tools will keep changing and evolving. But core concepts, programming skills, and business applications remain set in stone.

1. Programming
   We don’t expect you to have any prior knowledge of programming. But if you have coded in the past, it will help you pick up the coding parts of the program faster. The program will start by setting the foundation. Please note, admission into the program is competitive, and knowledge of programming might help your application stand out.
2. Mathematics/Statistics
   Working with data and analyzing it does not necessarily mean that you need a background in math/statistics. However, you will need to be comfortable around numbers – because that is the language of data science.

This is a transformational program – we expect it to change your life. So be ready for the heavy lifting!

3. Computer Science
If you have attended classes in algorithms, data structures, programming, etc. in your undergraduate studies, you will have an advantage. In any case, having a STEM background will help you. There are of course exceptionally successful data scientists who come from a background in other engineering disciplines, economics, math, statistics, finance, and similar numbers-based fields.

1. Create a New Algorithm
   This is arguably the only program that teaches you how to do that. Just be focused and understand the nuances of the material covered – learn from our leading global faculty – and soon you will be able to create an algorithm from scratch.
2. Create a New Library
   We help you gain the programming chops and the algorithms knowledge to create a new library in all areas of Data Science, Machine Learning, and Artificial Intelligence. This will ensure that you are not dependent on libraries created by others, nor are you one of those data scientists who never can create production-ready code.

3. Create a Product Prototype

Remember the edges of Data Science, Machine Learning, and Artificial Intelligence? We believe that the edges have fat tails and you need to know those as much as you need to know the algorithms. Thus, we will teach you how to deploy your product using Flask, Django, and R-Studio.

If you can’t work with the data, how would you effectively implement it in your algorithms? Through this program, we will take you to the edges of data, ensuring that you can work with structured and unstructured data, in the cloud and on your systems, and are able to clean it effectively for use in Machine Learning & AI algorithms.