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
Optional for Students
- Any source and brief extract of the IEEE 70xx
References and background for students:
Lesson Materials
The materials of this learning event are available under CC BY-NC-SA 4.0.
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
Expected time schedule and concepts organization
| 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 |
More information
Click here for an overview of all lesson plans of the master human centred AI
Please visit the home page of the consortium HCAIM
Acknowledgements
 |
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.
The materials of this learning event are available under CC BY-NC-SA 4.0
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The HCAIM consortium consists of three excellence centres, three SMEs and four Universities
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