MBA in Business Analytics

Programme Overview

The curriculum of the online MBA in Business Analytics features two parallel tracks - Business Management and Analytics. The curriculum includes thirteen courses on business management, eight courses on statistics and analytics techniques, five electives, and Experiential projects.

Each course will include some pre-recorded instructor videos, reading material, and practice questions. This will be accompanied with weekly assessments, hands-on assignments, and project work (often in groups) to ensure you will achieve all course-learning objectives. You will have the opportunity to interact with faculty in at least three live sessions per course, where you may seek feedback and clarification. The programme recognises 12 credits for the following certifications in the Term 2 and Term 3 electives: -

• Google Data Analytics Professional Certificate

• IBM Data Analyst Professional Certificate

• IBM Data Science Professional Certificate

• Fractal Data Science Professional Certificate

In addition to these 4 Credit based Industry certifications, enrolled learners will receive a Career Stack License wherein they complete these certifications during their degree, as well. Additionally, enrolled learners will have access to 45 other industry certifications listed here, which can be pursued without credit recognition.

Curriculum

The curriculum of the online MBA in Business Analytics is organized along two verticals—business management and analytics—and an engaging mix of both core and elective courses.

Business Management Vertical

Financial Accounting

You will acquire the capability to analyze financial statements for business decision making. You’ll be introduced to the various aspects of asset valuation and capital budgeting. You will also be exposed to financial applications of analytics, such as risk and fraud predictions, through specialized hands-on courses.

Managing Operations & Supply Chain

Get familiarized with the concepts, techniques and tools to design, analyse, and improve operational and supply chain capabilities of an organization. You will acquire the skills and understanding to manage projects and services. In addition, you will be able to apply analytics to solve operations, supply chain, and logistics problems.

Marketing Management

You will gain a deep appreciation for the science and practice of marketing, both from the perspective of the organization and the customer. You will get acquainted with the various concepts related online and digital marketing, and will also learn to apply analytics to specific marketing, sales, and service situations.

Managing Organizations & People

You will develop a better understanding of human behavior, at individual and collective levels, and the impact of organizational practices on this behavior. You will also be introduced to the principles of managing human resources (HR) in organizations, and apply analytics to make better HR decisions.

Strategic Management & Business Policy

You will be able to evaluate, understand, and devise organizational strategies for superior performance. You will also be able to identify and explore new markets and business opportunities, and run entrepreneurial ventures successfully.

It’s time to learn the fundamental aspects of business management. You will be able to apply micro- and macro-economic principles and methodologies to business decision problems. You will also be introduced to relevant aspects of business law and regulations, and learn to appreciate ethical and responsible business practices.

Analytics Vertical

Statistical Methods for Decision Making

This course introduces you to all the statistical concepts, tools, and techniques used by organizations and managers. By the end of this course, you will understand the significance of statistics and learn about its applicability in business management. You will gain in-depth knowledge for organizing raw data using graphs and tables to simplify data handling and interpretation.

Introduction to Business Analytics using R

This course introduces the fundamental concepts of business analytics to the managers. The objective is to build the foundation of data mining that forms the integral part of business analytics. Students will learn the data mining problem framing, data understanding, and data preparation for analytics-driven decision models in businesses or organizations.

Advanced Business Analytics

The course helps you to understand working with multivariate data for decision-making. You will understand how to develop predictive models and use them for explaining and predicting business decisions.

Predictive Analytics & Forecasting

This course will provide an overview of predictive analysis tools of data mining and their uses with the volume of data and business cases. The course further acquaints you with how to understand customer behavior and motivations, customers’ need, market segmentation, retailing, and business forecasting with the power of predictive data mining tools. Finally, the course will demonstrate a handful set of predictive analytics and data mining tools that can help young managers to make data-driven decisions in today’s business scenario.

Database Management with SQL

This course will introduce you to basic terms and concepts of databases. You will learn about the basic building blocks of databases and the roles of Database Administrators (DBAs) and users. This course introduces you to Structured Query Language or SQL. With the help of SQL, you will be able to execute database queries and add, retrieve, modify, or delete the data as and when desired.

Machine Learning

In this course, you will gain an in-depth introduction to building machine-learning models using Python. You will initially recapitulate the key Python libraries which are useful for Data Science applications. This includes coverage of Python libraries like Matplotlib, NumPy, and pandas. Next, you are introduced to the basics of machine learning, and the various classification and regression techniques are discussed. Also, the implementation of these techniques using the popular scikit-learn package is covered in detail.