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.

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.

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.

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.

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. 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.

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.

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.

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.

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.

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.

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.