Data Science for Finance

Description:

This 3-day hands-on training bridges the gap between financial domain expertise and data science techniques. Participants will explore the foundational concepts of data science and their application in finance, including risk modeling, credit scoring, fraud detection, and algorithmic trading. Through a mix of real-world datasets and use cases, learners will use Python and free libraries such as pandas, scikit-learn, and TensorFlow to implement machine learning and deep learning models. Each module includes both conceptual understanding and practical implementation, empowering participants to apply data-driven insights to real-world financial challenges.

Duration: 3 Days

Course Code: BDT48

Learning Objectives:

After this training, participants will be able to:

1. Describe the role of data science in solving financial problems.
2. Apply data preprocessing and feature engineering techniques on financial datasets
3. Implement machine learning models for classification, regression, and anomaly detection.
4. Analyze model performance using appropriate metrics and improve predictive accuracy.
5. Develop and evaluate deep learning models for financial forecasting and risk modeling.

• Basic knowledge of finance and statistics
• Familiarity with Python programming
• No prior machine learning experience required

• Finance professionals and analysts exploring data science
• Aspiring data scientists seeking finance-specific applications
• Business analysts looking to apply machine learning in financial decision-making

Course Outline:

Module 1: Introduction to Data Science in Finance

• Overview of data science lifecycle
• Key challenges in financial data analysis
• Exploratory data analysis using pandas and matplotlib
• Financial datasets: market data, transactions, credit data
• Hands-on: Cleaning and visualizing financial time-series and tabular data

Module 2: Machine Learning Applications in Finance

• Supervised vs unsupervised learning in finance
• Classification use cases: credit scoring, fraud detection
• Regression use cases: stock price prediction, financial forecasting
• Clustering use cases: customer segmentation and profiling
• Hands-on: Building and evaluating models using scikit-learn
• Hands-on: ROC curves, confusion matrix, R-squared, and error metrics

Module 3: Deep Learning and Advanced Use Cases

• Introduction to neural networks for financial data
• Time-series modeling with LSTM for forecasting
• Anomaly detection using autoencoders
• Use case: Loan default prediction using TensorFlow/Keras
• Hands-on: Deep learning model implementation and evaluation
• Best practices for model deployment and explainability (LIME, SHAP)

Training material provided: Yes (Digital format)