Administrative Information
| Title | The Data Analysis Process |
| Duration | 45 |
| Module | A |
| Lesson Type | Lecture |
| Focus | Practical - AI Modelling |
| Topic | Data Mining, Data Analysis |
Keywords
Data Mining,Information Mining,CRISP-DM,IEEE 70xx,
Learning Goals
- To be able to demonstrate knowledge of the data analysis process
- To understand differences between methodologies and standards
Expected Preparation
Learning Events to be Completed Before
Obligatory for Students
- Slides of the lecture
Optional for Students
- Any source and brief extract of the IEEE 70xx
References and background for students
- N/A
Lesson materials
Instructions for Teachers
Topics to be covered
- Introduction to what a data is
- Why Data is important (1 min)
- Data, Information, Knowledge (1 min)
- A possible definition for Data (1 min)
- Quantitative vs Qualitative Data (1 min)
- Quantitative vs Qualitative Analysis (1 min)
- The stage of Data Analysis
- The Data Analysis process (5 min)
- Defining the question (2 min)
- Collecting and Extracting the Data (2 min)
- Cleaning and Transforming the Data (2 min)
- Analyzing the Data (2 min)
- Share the results (2 min)
- Recent Trends in Data Mining
- Data Mining and Common Uses (1 min)
- Data Mining & Machine Learning (1 min)
- Data & Patterns (1 min)
- Data Mining techniques (1min)
- Data Mining Recent Applications (1 min)
- CRISP-DM (CRoss Industry Standard Process for Data Mining) methodology (1 min)
- CRISP-DM (CRoss Industry Standard Process for Data Mining) methodology
- Introduction (2 min)
- Business Understanding (1 min)
- Data Understanding (1 min)
- Data Preparation (1 min)
- Modeling (1 min)
- Evaluation (1 min)
- Deployment (1 min)
- Is CRISP-DM Agile or Waterfall? (2 min)
- The IEEE 70xx standard
- IEEE Standard Model Process for Addressing Ethical
- Concerns during System Design (10 min)
Time schedule
| Duration (min) | Description | Concepts | Activity |
|---|---|---|---|
| 5 | Introduction to what data is | Data, Information, Knowledge, Quantitative vs Qualitative Data, Quantitative vs Qualitative Analysis |
Lecture |
| 15 | The stages of data analysis (e.g., extraction, exploration, visualization) | Multi-stage Data Analysis process, Collection, Extraction, Exploration, Cleaning, Analysis, Visualization, Sharing Results |
Lecture |
| 5 | Recent trends in data mining | Data Mining modern applications | Lecture |
| 10 | CRISP-DM (Cross Industry Standard Process for Data Mining) methodology | CRISP-DM phases, Business Understanding, Data Understanding, Modeling, Evaluation, Deployment |
Lecture |
| 10 | The IEEE 70xx standard | Ethical Issues, System Design | Lecture |
Acknowledgements
Used sources
- Research papers
- Technical papers by some companies
- Reference Manuals
- IBM CRISP-DM
- IEEE 70xx
- Online Tutorials and Articles (e.g., KDNuggets, g2.com, Forbes, Data Science Process Alliance, etc.)
- Other reference books
The Human-Centered AI Masters programme was Co-Financed by the Connecting Europe Facility of the European Union Under Grant №CEF-TC-2020-1 Digital Skills 2020-EU-IA-0068.