《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 professional platform course for the Financial Management major, supported by the Xindao Cloud Platform. It is a theoretical and practical course that combines the use of the Xindao Cloud Platform, which includes a built-in Python editor and 8 intelligent financial executable files. Based on financial data and supported by Python technology, the course aims to implement

                                                                                                   Credit/Lecture Hours

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

I. Chapter 1: Applications of Data Mining and Machine Learning in Finance

1. Definition of Big Data and Big Data in      Daily Life

2. Application Areas of Data Mining and      Machine Learning

3. Typical Application Scenarios and      Prospects of Data Mining and Machine Learning in the Financial Field

(2 Lecture hours)

II. Chapter 2: Basics of Data Mining and Concepts of Machine Learning

1. Concepts of Data Mining and Machine      Learning

2. Classification of Data Mining and Methods      of Machine Learning

3. Data Preprocessing

4. Data Preprocessing (Practice)

 (4 Lecture hours)

III. Chapter 3: Classification Problems in Data Mining

1. Classification Concepts, Steps, and      Applications in the Financial Field

2. Classification Evaluation Metrics

3. Decision Tree Algorithm

4. Logistic Regression Algorithm

5. Credit Card Fraud Detection (Practice)      and Employee Attrition Prediction (Practice)

                                                (6 Lecture hours)

IV. Chapter 4: Clustering Problems in Data Mining

1. Clustering Concepts, Steps, and      Applications in the Financial Field

2. Clustering Evaluation Metrics

3. K-means Algorithm

4. Customer Value Analysis (Practice)

 (4 Lecture hours)

V. Chapter 5: Regression Problems in Data Mining

1. Regression Concepts, Steps, and      Applications in the Financial Field

2. Regression Evaluation Metrics

3. Linear Regression Algorithm

4. Product Price Prediction (Practice) and      House Price Prediction (Practice)

                                            (6 Lecture hours)

VI. Chapter 6: Time Series Problems in Data Mining

1. Time Series Concepts and Applications in      the Financial Field

2. Time Series Evaluation Metrics

3. ARIMA Algorithm

4. Cash Flow Forecasting (Practice) and      Inventory Demand Forecasting (Practice)

 (6 Lecture hours)

VII. Chapter 7: Text Mining Problems in Data Mining

1. Text Mining Concepts, Process, and      Applications in the Financial Field

2. Sentiment Analysis Algorithm

3. Sentiment Analysis of Stock Investors      (Practice)

 (4 Lecture hours)

               Total (Lecture Hours)                                                                     32             

Summary of UG CPC Topics Covered in this Course:                   (32Lecture Hours)