《Financial Data Mining and Machine Learning》Syllabus

Course Number:           2110020703

Course Name:               Financial Data Mining and Machine Learning

Instructors:                    Jiang Yanxin

Required Text:       Liu Peng, Tao Jianhui. Fundamentals of Data Mining (2nd Edition) [M]. Beijing: Tsinghua University Press, 2023.

Course Description:      This course is an elective course for the finance management major, supported by the Seentao Platform. It is a course that combines theory with practice, with the Seentao Platform featuring a Python editor and 8 executable files for intelligent finance. Based on financial data and supported by Python technology, this course aims to achieve the application of data mining and machine learning in the field of finance.

                                                                                                   Credits/Teaching Hours

Outline of UG CPC Topics Covered in this Course:       (2 credits/32 Teaching Hours)

Chapter 1: Application of Data Mining and Machine Learning in Finance

2 Teaching Hours

Section 1: Definition of Big Data, Big Data in Daily Life

Section 2: Application Areas of Data Mining and Machine Learning

Section 3: Typical Application Scenarios and Prospects of Data Mining and Machine Learning in Finance

Chapter 2: Fundamentals of Data Mining and Concepts of Machine Learning

   4 Teaching Hours

Section 1: Concepts of Data Mining and Machine Learning

Section 2: Classification of Data Mining, Machine Learning Methods

Section 3: Data Preprocessing

Section 4: Data Preprocessing (Practice)

Chapter 3: Classification Problems in Data Mining                     6 Teaching Hours

Section 1: Concept of Classification, Classification Steps, Application of Classification in Finance

Section 2: Classification Evaluation Metrics

Section 3: Decision Tree Algorithm

Section 4: Logistic Regression Algorithm

Section 5: Credit Card Fraud Detection (Practice), Employee Turnover Prediction (Practice)

Chapter 4: Clustering Problems in Data Mining             4 Teaching Hours

Section 1: Concept of Clustering, Clustering Steps, Application of Clustering in Finance

Section 2: Clustering Evaluation Metrics

Section 3: K-means Algorithm

Section 4: Customer Value Analysis (Practice)

Chapter 5: Regression Problems in Data Mining             6 Teaching Hours

Section 1: Concept of Regression, Regression Steps, Application of Regression in Finance

Section 2: Regression Evaluation Metrics

Section 3: Linear Regression Algorithm

Section 4: Product Price Prediction (Practice), House Price Prediction (Practice)

Chapter 6: Time Series Problems in Data Mining            6 Teaching Hours

Section 1: Concept of Time Series, Application of Time Series in Finance

Section 2: Time Series Evaluation Metrics

Section 3: ARIMA Algorithm

Section 4: Cash Flow Prediction (Practice), Inventory Demand Prediction (Practice)                                                                                                   

Chapter 7: Text Mining Problems in Data Mining            4 Teaching Hours

Section 1: Concept of Text Mining, Text Mining Process, Application of Text Mining in Finance

Section 2: Sentiment Analysis Algorithm

Section 3: Stockholder Sentiment Analysis (Practice)

Total (Teaching Hours)                                                                  48  

Summary of UG CPC Topics Covered in this Course:            (48Teaching Hours)