After clearing the Foundation exam, all students are required to take 4 Core Courses
1) Data Management & Programming using Python
Course objective: The primary objective of this course is to provide a firm foundation in the application of data science principles to various fields of study, especially, Economics and Finance. The course will be exclusively taught on Python and particularly caters to programming novices.
- Basic data types, operators, expressions and statements
- Advanced data types such as lists, ndarrays, dictionaries, sets
- Programming building blocks such as the if statement, for and while loops and functions
- The Python pandas package and its critical objects such as the DataFrame, Series, Index, MultiIndex, GroupBy.
Learning Outcomes: At the end of this course students would have grasped the essentials of core Python concepts and be extremely comfortable processing data on Python.
2) Machine Learning
Course objective: The purpose of this course is to familiarize students with modern machine learning models. Most of the courses in this area either focus on the theory of machine learning or explaining tools of the popular models of machine learning models. This course bridges this gap by combining both aspects of teaching machine learning. This is an introductory course for theoretical machine learning.
- Supervised and unsupervised learning models.
- Clustering, recommender systems, neural networks and deep learning, tree-based learning and other advanced methods of learning.
Learning outcome: By the end of this course students will be able to apply machine learning tools to understand real-world problems such as detection of price manipulation in markets, stock selection using clustering, classifying Mutual Funds using recommender models, bank loan default and credit rating forecasting using deep learning.
The Finance Core portion is designed as a natural extension to the Foundation course. The Foundation course gives a gentle introduction to various key areas in finance, whereas the Core course goes deeper into explaining the intricacies of the financial domain.
1. Build on the concepts that were introduced under the Foundations section.
2. The Finance Core portion is designed to help candidates build a sound and in depth understanding of financial concepts. Concepts covered in Core portion are used frequently in the industry. Following are the topics discussed under the Core section
- Understanding of Debt markets – bond pricing, credit spreads, spot rates etc.
- Understanding of equity markets – Index construction, beta, etc.
- Risk Management – VaR, credit risk analytics, risk mgmt in banks
- Capital Budgeting – project evaluation criteria /capital rationing etc.
- Understanding Capital Structure decisions & Dividend Policies
- In-depth Financial Statement Analysis -Corporates – Income statement, Balance sheet, Cash flow statements, ratios etc.
- Introduction to Financial statements of Banks
- Credit Analysis & Rating methodology – LGD/PD, Business, Financial and Structural Risk
- Business valuation – understanding valuation approaches Asset Approach / Income Approach / Multiples approach. Valuation using various equity valuation models.
1. Candidates will build expertise and confidence in key areas of finance
2. Course will provide Data Science candidates the financial acumen which they can apply to not just data science roles but also to core finance roles offered in the industry
4) Data Wrangling and Analysis
Course objectives: Data wrangling is important to learn if you want to be able to gather, select, and transform data to answer a question or solve a problem. This is essential for two reasons. First, the work of data wrangling can speed the time to develop and test models. Second, it allows for faster analysis and more accurate conclusions. Data wrangling makes data more usable in an organization. Understanding it can help your work better and make sounder decisions based on the data.
- You will be able to build interactive dashboards using Tableau & PowerBI.
- Describe the data ecosystem, tasks a Data Analyst performs, as well as skills and tools like SQL which are required for successful data analysis
- Relational Database fundamentals including SQL query language, Select statements, sorting & filtering, database functions, accessing multiple tables
- Python programming basics including data structures, logic, working with files, invoking APIs, and libraries such as Pandas and Numpy