F71DS - Data Storytelling

Course leader(s):

Fraser A. Daly (Edinburgh, January)
Alistair Wallis (Edinburgh, January)
Ian Tan (Dubai, January)
Haslifah Hasim (Malaysia, January)

Aims

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.

Initially this will be based of F78DS, and will follow that syllabus

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.)

4. Data Wrangling and descriptive statistics (4.1 Gather, investigate, adjust and describe data using suitable software)

5. Modelling (5.1 Understand the basics of Statistical and Machine Learning Models, 5.2 Associative Rule Mining & Market-basket analysis, 5.3 Linear Regression, Classification and Clustering)

6. Operationalising models (6.1 Model deployment, MLOps, 6.2 Delivering via API, 6.3 Building API to deliver models, 6.4 Data and Model maintenance, MLOps, 6.5 API, Models and Data)

Learning outcomes

By the end of the course, students should be able to do the following:

understand and explain the roles of data and its governance from a business perspective.
classify participants and analyse suitable resources and tools for a data science project throughout its lifecycle.
identify and implement the processes required for organising and maintaining data in an organisation.
demonstrate, through usage of tools, the techniques to determine the size, quality, storage, scope and classify the data for processing.
conduct various kinds of data analysis and statistical methods for a data science project.
analyse the ethical considerations and legislation surrounding data usage;
design Data-Driven Decision-Making processes for the workplace;
generate effective data visualisations;
compose data stories to communicate insights.

Further details

Curriculum explorer: Click here

SCQF Level: 11

Credits: 15