1) Orientation to the industry landscape

This is an induction module wherein the student will be acquainted with the current market scenario and what it takes to make a mark in the data analytics domain. Students will learn how analytics enables companies across the globe to understand customer trends and patterns and how it impacts overall revenue. Research shows that 95% of companies consider managing unstructured data one of the biggest challenges. Thus, there are ample opportunities to fill that gap and become a data expert who understands how to handle data and derive insights from them. This module will help students evaluate their skills and intent to become successful data scientists. The students will cultivate skills that allow them to:

• Simplify complicated ideas and explain them to laypersons,
• Inculcate curiosity about a business, understand market trends, and know how to hit the bull’s eye with their research and solutions,
• Be keen on working with AI and machine learning tools, and
• Look at the bigger picture and understand the organization’s architecture rather than just focusing on individual tasks.

Simultaneously, students will learn how data science has the power to mitigate risks and, when done incorrectly, can incur heavy losses. You will learn to adopt data science tools for various domains/fields.

This module will help students understand which type of analytical tool suits which type of industry, thereby addressing knowledge gaps about employment opportunities in Data Science.

2) Building Blocks (Basics of Mathematics & Statistics, Fundamentals of Programming)

Students will be introduced to the basics of statistics and understand how it helps dephase patterns through numbers. This module is essential to support candidates with no prior knowledge of statistics.

Simultaneously, for students with no background in programming, this module will help acquaint them with the basics of programming. It includes basics such as graphics, operating systems, logical functions, theoretical computer science, computer architecture, and algorithm design.

Lastly, this module will help students recapitulate the basics of calculus, linear algebra, and statistics.

3) RDBMS + ETL – SQL for Data Science – Introduction to Cloud Computing

In this module, students will learn Relational Database Management System [RDBM] and ETL [Extract, Transform and Load]- a program designed to create, update, and manage relational data. Students will learn how an ETL tool extracts data from different RDBMs, transforms it, and loads it to the data warehouse system. In most cases, this happens on an SQL interface which will be explained in this module.

Simultaneously, students will learn about Cloud Computing and its connection to RDBM.

1) Business Problem Solving: Predictive Modeling using Python

Using data to make accurate predictions is one of the core roles of a data expert. Using programming languages like Python, students will learn how to build a predictive model based on the organization’s historical data and known results. These models have the power to predict future events accurately, helping businesses devise more data-oriented strategies within specific conditions. Students will learn to read data patterns and trends and use them to build predictive models.

2) Machine Learning using Python (Supervised and Forecasting Methods)

This module will train students to use machine learning in real-world scenarios. They will learn how machine learning is applied across various domains and industries and why Python libraries are the best for machine learning.

They will be familiarized with diverse types of machine learning, like supervised learning and algorithms, including time series forecasting, which helps improve forecasting accuracy while minimizing the loss function.

3) Unsupervised learning using Python and MLOps (Clustering, PCA, and Recommendation System)

In this module, students will learn about the following type of machine learning, i.e., unsupervised machine learning. They will be made to understand the concepts of clustering, Principal Component Analysis (PCA), and Recommendation Systems.

Throughout this module, students will understand how data interpretation is made, what methodologies are used to analyze data, and how AI is implemented to create a system that can make accurate product recommendations based on user search behavior or previous browsing patterns.

1) Text Mining and NLP using Python

This module will introduce students to the concepts of Natural Language Processing (NLP) and Text Mining using one of the most popular programming languages, Python. NLP and text mining are different concepts. NLP analyzes text, speech, or grammatical syntax to understand human language. Text mining, on the other hand, extracts information from structured and unstructured content. It focuses more on the content structure than meaning.

Students will learn machine learning algorithms used in text mining and NLP and techniques and methodologies to write their own data analysis algorithms using Python.

2) Value Proposition of Analytics in distinct functions (Marketing, Risk Management, and Operation)

This module will introduce students to various data analytics applications across distinct functions, like marketing, risk management, operations, etc.

• Marketing: Students will learn to evaluate marketing activities based on data and understand how businesses use analytical processes to evaluate customer-based data and design solutions accordingly. This module includes three major types of marketing analytics: descriptive, predictive, and prescriptive.
• Risk Management: Risk analytics is probably one of the most sought-after skills among companies looking for skilled data scientists. Students will learn how to measure, assess, and manage risk by analyzing available data and learn to analyze data and predict business risks with maximum accuracy. This module will train students to understand patterns in data and trends and do competitor analysis.

• Operations: Students will learn to make better business decisions with data, predict outcomes, and design models based on future demands and uncertainties.

3) AI & Deep Learning using Python – Computer Vision, Text Mining – Elective

This elective module will give students more clarity on the core concepts and their differences. Students will understand how AI and Deep learning part and sub-part of Machine Learning are and how these concepts impact our daily lives on a micro and macro level.

You will learn about:

• Computer Vision
• Text Mining

1) Industry Capstone Project work – Dissertation – Final Viva

Students will work under the guidance of their mentor/teacher to complete their dissertations, based on which they will be reviewed. An incomplete dissertation or incorrect project work will lead to failure in completing the program.

Students can opt for a more practical approach in the final phase through Capstone Project work. They will get an option to choose from multiple project options:

• Sports event analysis and reporting
• Consumer electronics pricing data analysis & visualization
• Telcom churn prediction (Classification & Machine Learning)
• Predicting credit card spending (Regression Methods)
• Peer group lending analysis & prediction (Regression Methods)
• Marketing & sales data manipulation and analysis
• Airlines data analysis and reporting
• Sports equipment retail data analysis and visualization
• Peer group lending analysis & prediction (Regression Methods)

2) Industry Capstone Project work – Dissertation – Final Viva

Students will learn problem-solving that allows them to break down and structure complex problems into small, logical steps or tasks.