Data Analysis for Finance

This subject introduces students to principles and applications of data analysis for finance. Concepts covered include data collection, processing and management, relevant theory in statistics, econometrics and machine learning, programming in relevant languages and data presentation. Specific topics include data sourcing, processing and cleaning, summarizing and visualizing data; multiple regression, time-series models, panel data techniques and causal inference; machine learning and classification methods, model selection and assessing model performance, unsupervised learning and textual analysis. Students will become proficient in relevant programming languages such as Python or R.