Details of the topics and sub-topics in the progression pathway developed for the National Progress Award Data Science's core units

1. NPA Data Science
Kate Farrell
Director of Curriculum Development
and Professional Learning
Dr Jo Watts
Founder, Effini

2. National Progress
Award in Data Science

3. Progression pathway
for the core units:
Data Citizenship and Data Science

4. Progression Pathway
1. What is Data?
2. Interpreting Data
3. What is Data Science?
4. Data Types and Formats
5. Capturing Data
6. Security
7. Privacy
8. Data Manipulation
9. Statistics
10. Ethics and Bias
11. Analysis
12. Quality and Management
13. Visualisation and Storytelling
14. Tools and Languages

5. What is Data?
History and growth of data
Where data comes from
Value of data
Open, Public and Private data

6. Interpreting Data
What is a graph?
Reading graphs
Graph types
Good and bad graphs

7. What is Data Science?
History and growth of data science
Applications of data science
Data science lifecycle
Data scientists and domain experts
Data maturity

8. Data Types and Formats
Quantitative data (discrete and continuous)
Qualitative data (nominal, ordinal and binary)
Data models and the structure of data
Structured, unstructured and semi-structured data

9. Capturing Data
Data capture
Evaluating data
Surveys to gather data

10. Security
Personal data security
Computer setup: Antivirus, Firewalls, VPN
Keeping data secure
Personal password management
Encryption and anonymisation
Backups

11. Privacy
Data privacy
Laws and rights (GDPR)
Maintaining privacy online
Viewing and managing your information

12. Data Manipulation
Single dataset manipulation
Aggregation
Joining datasets
Types of dataset merge
Data reshaping

13. Statistics
Population and samples
Descriptive and inferential statistics
Summarising data
Centrality and dispertion
Distributions

14. Ethics and Bias
Ethical issues in data science
Bias in data

15. Analysis
Understanding data
Data tidying and cleaning
Manipulation and transformation
Identifying patterns
Extracting insights

16. Cleaning data
Tidying data
Missing values
Outliers

17. Quality and Management
Impacts of poor quality data
Data quality dimensions
Caring for data
Data management and governance
Metadata and data dictionaries

18. Visualisation and Storytelling
Choosing the right graph
Graphical best practices
Dashboards
Storytelling with data
Solving problems with data

19. Tools and Languages
Spreadsheet tools
Commercial visualisation packages
Point and click analysis / visualisation
Open source programming languages

20. Kate.Farrell@ed.ac.uk
jo@effini.com