Course Outline

Module 1: Introduction to Data Analytics in Banking and Finance

• Overview of data analytics and its importance in the finance sector
• Key data sources in banking and finance
• Understanding the data analytics process: From collection to decision-making
• Introduction to data governance and compliance in finance
• Case Study: Analyzing customer transaction data to identify trends and inform product development

Module 2: Data Preparation and Management

• Cleaning and transforming financial data for analysis
• Handling missing data and outliers in financial datasets
• Working with large datasets: Best practices for data storage and retrieval
• Introduction to data warehousing and database management in finance
• Case Study: Preparing and managing a dataset for a credit risk analysis project

Module 3: Exploratory Data Analysis (EDA) in Finance

• Descriptive statistics and data visualization techniques
• Identifying patterns and anomalies in financial data
• Using Excel and Python for exploratory data analysis
• Introduction to financial ratios and metrics in data analysis
• Case Study: Conducting exploratory analysis on loan performance data to identify risk factors

Module 4: Predictive Analytics and Modeling

• Introduction to predictive modeling techniques in finance (e.g., regression, classification)
• Building and validating predictive models using financial data
• Applying machine learning algorithms in banking and finance
• Evaluating model performance and accuracy
• Case Study: Developing a predictive model to forecast loan defaults using historical data

Module 5: Data-Driven Decision Making in Finance

• Integrating analytics into financial decision-making processes
• Case studies of successful data-driven strategies in banking
• Communicating insights and recommendations to stakeholders
• Ethical considerations and biases in financial data analytics
• Case Study: Implementing a data-driven approach to optimize investment portfolios