Module 1 (6 Hours)
Introduction to Data Analytics in Finance: Overview of financial data and its types (e.g., stock prices, bond yields, forex data).
Key concepts in financial analytics: risk, return, correlation, volatility. Data preprocessing: cleaning, normalization, and handling missing data.
Tools and technologies for financial data analytics (e.g., Python, R, Excel, SQL).

Module 2 (6 Hours)
Financial Data Visualization and Exploration: Techniques for visualizing financial data (line charts, bar charts, histograms, scatter plots).
Descriptive statistics for financial data (mean, median, variance, skewness, kurtosis). Financial data aggregation and grouping. Introduction to Python libraries for data analysis (Pandas, Matplotlib, Seaborn).

Module 3 (6 Hours)
Time Series Analysis and Forecasting: Key concepts in time series analysis: trends, seasonality, and noise.
Autoregressive (AR), Moving Average (MA), and ARMA models. Forecasting stock prices using ARIMA models. Introduction to machine learning-based time series forecasting (e.g., LSTM networks).

Module 4 (6 Hours)
Week 4: Risk Management and Volatility Modeling Understanding financial risk and its measurement (VaR, CVaR). Volatility modeling: GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models. Estimating and forecasting volatility in financial markets. Simulating risk using Monte Carlo methods.

Module 5 (6 Hours)
Portfolio Theory and Optimization: Modern Portfolio Theory (MPT): Efficient frontier, risk-return tradeoff.
Capital Asset Pricing Model (CAPM). Portfolio optimization using mean-variance optimization. Introduction to the Black-Litterman model for portfolio allocation.

Module 6 (6 Hours)
Machine Learning for Financial Modeling: Overview of machine learning in finance: supervised vs. unsupervised learning.
Regression models for predicting stock prices and other financial indicators. Classification models: logistic regression, decision trees, random forests, and support vector machines (SVM). Evaluating model performance (accuracy, precision, recall, F1 score).

Analyze financial data using statistical and machine learning techniques.

Apply time series forecasting methods to predict stock prices, interest rates, and other financial metrics.

Understand risk management tools and models used in finance.

Optimize portfolios using financial data and machine learning.

CO1: Proficiency in using data analytics techniques to clean, analyze, and visualize financial data
CO2: Ability to model financial time series and forecast stock prices, interest rates, and volatility.
CO3: Understanding and implementing models to assess and manage financial risk.
CO4: Using quantitative methods to optimize investment portfolios and manage asset allocations.
CO5: Applying machine learning algorithms for prediction, anomaly detection, and fraud detection in finance.

R. R. Durrett, Data Science for Economists, Springer

Yves Hilpisch, Python for Finance, O'Reilly (WILEY UK)

Mark J. Bennett, Dirk L. Hugen, and Timothy W. L., Financial Analytics with R, Cambridge University Press

Kaggle, Financial datasets and tutorials on data analysis and machine learning, (Online Resources)