Business Analysis and Finance: The Application of Python Data Analysis in Financial Decision Making

Course 12: Business Analysis and Finance: The Application of Python Data Analysis in Financial Decision Making

I. Course Description

Data analysis is an indispensable skill in modern society, because we live in a data-driven world. Whether business, health, finance, education, public policy, all need data to guide decision-making. Through data collection, processing, analysis and interpretation, data analysis can help people discover trends, rules, explore potential business opportunities, and provide scientific decision support for various industries. Data analysis technology can be applied in many fields, such as market research, financial analysis, customer relationship management, human resource management, and so on.

This course is a practice-oriented machine learning and data science course focusing on the application of Python in financial data analysis. Students will learn key steps from data collection and visualization to modeling and prediction, mastering the skills that data scientists need in financial analysis. The course covers data sources and experiments, probability theory, regression methods, decision tree, random forest and other data science methods, and is applied to real cases of financial market data. Through practical cases, hands-on experiments and projects, students will learn how to use Python to analyze and solve data science problems in the financial field to deeply understand the role of data analysis and statistics in financial decision-making and prepare for entering the data-driven financial industry.

II. Professor Introduction

Haiyuan Wang – Professor at Columbia University

Haiyuan Wang Professor He has made remarkable achievements in the field of data science and financial quantitative analysis, and his research direction covers predictive indicators and longevity risk management. He teaches multiple data science and applied analysis courses at Columbia University and aims to develop data-driven talents in the finance field.

He has more than 14 years of experience in modeling and development, has a deep understanding of statistics, machine learning and optimization, has held important positions in top financial institutions including vice president and lead in New York with fixed income quantitative research, and loan modeling, algorithm trading and data analysis at Global Atlantic Financial Group and Morgan Stanley.

III. Syllabus

1. Introduction to Data Science and Financial markets
2. Data, charts, and statistical basis
3. Data source: experiment and simulation
4. Probability theory: Gaussian distribution and binomial distribution
5. Confidence interval
6. hypothesis test
7. Regression method
8. Decision tree and random forest
9. Other methods and tools in data science
10. Advanced application of data analysis in finance