Data Mining Practical Guide Course Content

CHAPTER 1. Data Mining introduction

1.1 What is (not) Data Mining?

1.1.1 Defining Data Mining
1.1.2 Difference between Data Mining and Statistics
1.1.3 Difference between Data Mining and Data Reporting
1.1.4 Difference between Data Mining and Data Analysis

1.2 What can I do with Data Mining?

CHAPTER 2. Data Mining applications

2.1 Data Mining algorithms

2.1.1 Basic Data Mining algorithms: description, examples and use
2.1.2 Combining Data Mining algorithms: description, examples and use

2.2 How can I identify Data Mining applications for my business?

CHAPTER 3. The process of Data Mining: planning Data Mining projects

3.1 Stage 1: Data pre-processing or data preparation

3.1.1 Where is my data?
3.1.2 Can I trust my data?
3.1.3 Measuring my data quality
3.1.4 Improving my data quality
3.1.5 Data creation and data setting

3.2 Stage 2: Data processing or data mining

3.2.1 Parameters for Data Mining
3.2.2 Combining Data Mining algorithms based on business needs
3.2.3 Pitfalls

3.3 Stage 3: Data post-processing or result interpretation

3.3.1 How do I interprete results?
3.3.2 How do I extract actionable results?
3.3.3 Data visualisation

CHAPTER 4. Risk analysis of Data Mining projects

4.1 Identifying risks

4.2 Controlling risks

CHAPTER 5. Issue handling and problem solving in Data Mining projects

CHAPTER 6. CRoss Industry Standard Process for Data Mining (CRISP-DM)

CHAPTER 7. Data Mining business cases: examples and exercises