This course introduces students to the data science workflow, focusing on transforming raw data into meaningful information. Topics include data ethics, what constitutes a good data science model, data visualisation, and data storytelling. Practical skills will be developed using tools like PowerBI or Tableau.
Initially this will be based of F78DS, and will follow that syllabus
1. Analyse the ethical considerations and legislation surrounding data usage (1.1 Principles of data ethics. , 1.2 Legislation related to data privacy and protection GDPR, etc.. , 1.3 Ethical considerations in data collection and analysis. , 1.4 Importance of data anonymisation. , 1.5 Techniques for data anonymisation. , 1.6 Balancing data utility with privacy.)
2. Data-Driven Decision-Making (2.1 The data science workflow, 2.2 Interpreting the results of data science. , 2.3 Evaluating Data-Driven Decision-Making models.)
3. Data Visualisation (3.1 Principles of effective data visualisation. , 3.2 Introduction to PowerBI and Tableau. , 3.3 Basic chart types and their uses. , 3.4 Interactive dashboards in PowerBI and Tableau. , 3.5 Customizing visualisations. , 3.6 Best practices for dashboard design.)
4. Data Storytelling (4.1 Identifying the audience and their needs. , 4.2 Structuring a compelling narrative: beginning, middle, and end. , 4.3 Developing key messages and takeaways from data analysis. , 4.4 Simplifying complex data for diverse audiences. , 4.5 Highlighting key insights without oversimplification. , 4.6 Balancing data accuracy with narrative clarity.)
5. Project (5.1 Project set-up, 5.2 Integration of all course concepts via Group Project, 5.3 Assessment)
By the end of the course, students should be able to do the following:
Curriculum explorer: Click here
SCQF Level: 11
Credits: 15