Data Science & AI in Finance Analytics
This course offers a fully self-paced learning experience through pre-recorded video lectures that can be accessed and replayed indefinitely, with no live online sessions required. Enrollees receive free access to the Python environment setup, downloadable Excel exercise files, and complete Python scripts for every practical project. The program also includes one attempt at the final course assessment exam upon completion of the video modules.
Course syllabus
- 01
Technical analysis of share prices using the Relative Strength Index, Rate of Change indicators, and Head and Shoulders patterns in Python. Review share prices from BSE and NSE-listed companies and learn how to select and evaluate stock data from exchange websites.
- 02
Share price prediction using Ant Colony Optimisation, Prophet, ARIMA, and GARCH models. Apply these models to stock exchange data, measure market volatility across stocks, gold, and oil, and analyze relationships using Wavelet Coherence, Cross Spectrum Coherence, and Cross Power Spectrum Coherence models.
- 03
Calculate risk and return in Python using average return, standard deviation, cumulative return, and Sharpe ratio. Apply these core securities-market measures to Indian stock market data to evaluate company performance and the relationship between risk and return.
- 04
Evaluate share prices using Event Study and Interrupted Time Series Analysis to understand how events and other external factors influence the prices of the same companies across different time periods.
- 05
Understand loan approval patterns using machine learning models to identify factors that influence customer loan disbursement. Perform sentiment analysis on financial news and create word clouds to study the relevance of news content across approximately 10,000 words.