Cutting Edge Curriculum

The comprehensive AMPBA curriculum is a perfect blend of theory and practice offering a judicious mix of analytical skills, business knowledge, and a strategic perspective on the analytics industry. The curriculum is continuously and rigorously updated to bring cutting-edge learning tools and methods to the class. The combination of course modules and the industry-sponsored Capstone Project offers both exhaustive learning and real-world application across domains. In addition to the Capstone Project, students will be completing Foundation Projects too.

Programming Bootcamp

The knowledge of Python and R-programming is essential for AMPBA. Every student will have access* to two Bootcamp modules of asynchronous content with built-in mandatory quizzes and exercises.

Foundational Courses

The Foundation Term courses cover the statistical, methodological, and programming foundations for business analytics and data science. Students need to attempt and pass all courses of the Foundation Term and with a pass grade in the two mandatory courses to proceed to Term 1.

Core Courses

These courses cover in-depth study in statistics, optimisation, big data, machine learning, deep learning, and AI.

Competitive Advantage Courses

These courses teach the participants how to apply data analytics to different business domains such as marketing, finance, fraud, blockchain, supply chain, pricing, and more across industries. Students use data science and business analytics models to solve business problems by applying them to large data sets.

Experiential Courses

Foundation projects and Capstone projects teach participants about the life cycle of a project in Data Science and Management and show them how to apply their skills from “Competitive Advantage” and “Deep Dive” courses to real business problems. 

 

*Access will be provided to the students after payment of Instalment-I fee

Programming Bootcamp*

Introduction to Python

Introduction to R-programming

Foundation Term **

Introduction to Probability

Descriptive Statistics using R

Data Analysis using Python

Core Modules

 Data Collection & Pre-Processing

 Statistical Analysis 1

 Business Communication

 Big Data Management

 Data Visualization

 Optimization

 Introduction to Text Analytics

Advanced Modules

Machine Learning: Unsupervised Learning 1

Statistical Analysis 2

Machine Learning: Unsupervised Learning 2

Machine Learning: Supervised Learning 1

Advanced Optimization and Simulation

Statistical Analysis 3

Deep Learning

Advanced Statistical Analysis

Forecasting Analytics

Machine Learning: Supervised Learning 2

Natural Language Processing

Big Data & Cloud Computing

Network & Graph Analytics

Application Modules

Digital and Social Media Analytics

Supply Chain and Retail Analytics

Applications of AI

Pricing and Retail Analytics

Financial Analytics

Contemporary Modules (across terms)

• Contemporary Topics 1.

• Contemporary Topics 2.

Experiential Learning Modules

• Foundational Project 1

• Foundational Project 2

• Capstone Project.

 

 

Some courses may undergo modification at the time of delivery.

 

*Mandatory to successfully complete the two Bootcamp modules with asynchronous content before the Foundation Term. **Students need to attempt all three courses and obtain a mandatory pass grade in (a) Descriptive Statistics using R and (b) Data Analysis using Python to proceed to Term 1.