Programme outline
This course addresses three key issues relating to the active use of data: (i) the theory and practice of data curation, data scrubbing, data preparation, and data (ii) data visualisation (dashboards and infographics) with effective and aesthetic techniques, (iii) data governance, data stewardship, and data ethics.
Basic knowledge of a programming language like Python/R is expected for this class.
Learning objectives
By the end of the course, students will be able to
- Describe basic data science/analytics terminology.
- Apply the full life cycle of data gathering, wrangling/preparation, analytics and visualisation.
- Translate data science concepts to real-world use cases and apply them in a professional setting.
Topics
- Data Acquisition and Inspection
- Introduction to Databases
- Understanding Data Wrangling
- Data Cleaning Techniques
- Data Transformation: Normalisation, Scaling, Encoding
- Data Manipulation
- Data Wrangling Applications and Case Studies
- Introduction to Data Visualisation
- Types of Visualisations and Choosing the Right Representation
- Design Principles and Visualisation Customisation
- Understanding Data Ethics and Governance
Assessment
Participants will be assessed through regular quizzes conducted throughout the course to reinforce learning and gauge individual understanding. In addition, group projects will be used to evaluate participants’ ability to collaborate, apply concepts and demonstrate practical problem-solving skills. For participants who are taking this as a credit-bearing course, there will be written examination during mid-term (week 6) and final term (week 14).
Note: SUTD Academy reserves the right to cancel the class due to unforeseen circumstances.