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.
Installation; IDEs (IDLE, Jupyter Notebook); a programmer’s logic view of a computer; basic data types int, float, string, boolean; variables; the None type; print and input functions, escape sequences; operators – arithmetic, assignment, relational, Boolean and identity operators; the format function; the sys.argv object; string functions; iterability, subscripting and indexing of objects; Unicode strings; the list data type, list functions, list comprehension; list operators, the tuple data type; the class type; the range class; the for loop; the while loop; nested loops; the enumerate function; the zip function; introduction to itertools; python in-built functions; the if, elif, else statements; mandatory code indentation in Python; user-defined functions – declaration, arguments, body, return, variable number of arguments, named arguments; lambda functions; nested functions; generator functions; the dictionary data type; items, keys, values of a dictionary, dictionary methods; list of dictionaries; sorting a list of dictionaries; mutable and immutable data types; the numpy package; ndarrays; numpy methods; the pandas package; the dataframe type; the series type; the groupby type; dataframe indexing – iloc and loc; dataframe operations; filtering a dataframe using a boolean array; pandas functions; dataframe columns and index; multi-index; pivot tables; dataframe apply method; pandas merge; sorting of sortable types; the set data type.
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 the students with modern machine learning models. Most of the courses in this area either focus on the concept or application of machine learning algorithms. This is an introductory course for theoretical machine learning. Furthermore, in this course, most models will be explained with great details such as problem statements, solutions, algorithms, implementation, and solving real-world problems with actual data.
- Statistical Learning
- Data Prepossessing / Cleaning
- Linear Regression
- Resampling Methods
- Linear Model Selection and Regularization
- Non-Linear Regression
- Tree-Based Methods
- Support Vector Machines
- Unsupervised Learning
Learning outcome: By the end of this course, students will be able to formulate real-world problems and their solutions using mathematics and move on to advance machine learning techniques. The course will also prepare students with advanced skills for programming with machine learning models.
3) Core Finance
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.
- 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.
Shaping and clean-up of raw data
Metrics of numeric variables, Metrics of categorical variables
Quartile analysis, Z-Score analysis, test of normality, skewness, generating data for a distribution, outlier analysis, crosstab and pivot tables, covariance, correlation, linear regression, exponential regression
Relational databases and database objects; CRUD operations of a database; Anatomy of a SELECT statement; Basic SELECT statements; Clauses: SELECT, FROM, SELECT DISTINCT, WHERE, GROUP BY, ORDER BY, UNION, LEFT JOIN, IN, INNER JOIN, RIGHT JOIN, FULL JOIN LIMIT; Functions and keywords: DATEDIFF, COUNT, COUNT(*), SUM, MIN, MAX, AVG, BETWEEN, NULL; Operators: *, >, >=, <, <=, =, BETWEEN, IN, AND, OR, NOT, AS, IS
The philosophy of Data Visualization – the WHY, WHAT and HOW of Visualization; Univariate Visualization, Bivariate Visualization, Multivariate visualization.
Visualizations covered across Microsoft Excel, Python (matplotlib), Power BI, Tableau:
Histogram, box and whisker plot, bar and column charts (simple, clustered, stacked, 100% stacked), line chart, area chart, dual-axis line and column chart, heat map, diverging bar chart, waterfall chart, funnel chart, scatter plot, pie chart, donut chart, treemap, sunburst chart, geographical map charts, pareto chart, cards, tornado (butterfly) chart, animated race chart, dashboards
- 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