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# System prepended metadata

title: 'RDS course: Module 1 plan'

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# RDS course: Module 1 plan

### High level syllabus

Module 1: Introduction to data science
	
- Sub-module A (Taught): 
    - What is data science? How it differs (or does not differ) from other fields, overview of the variety of cultures within it and its origins
        - Definition of data science vs. data analysis, ML, AI and origins
        - Cultures: Theorise and estimate, compute and test
    - What is RDS/RSE, what is special about it. 
        - Roles and skills within research projects
        - Difference between DS and RDS
    - Project management and life cycle: Basic stages in a data science project and common hurdles in each stage.
        - Project planning
        - Scoping: 
            - Translating a research question into a data science task. 
            - Ambiguity and complexity in scoping with some real case studies.
            - Collaborating with clients, adoption
        - Getting and wrangling data
        - Feature engineering, selection
        - Model training and evaluation
        - Production
        - Monitoring performance and updating
        - Handover 
    - Intro to EDI for data science: 
        - Forms of bias and oppression in data science and society, matrix of opprression
        - Common pitfalls
        - Forms of privilege
        - How to challenge power and privilege
        - Examples of EDI in data science projects with varying degrees of success.
    - How to work collaboratively in data science projects and reproducibility principles - using material from The Turing Way.


- Sub-module B (Hands-on project):
    - Scope a project using a real-world scenario/research question: Understand the associations between SES/material circumstances and health using the EQLS dataset (a survey micro-dataset):
        - What is SES/material circumstances?
        - What is health?
        - How do we establish an association? What are the different ways we can think about the problem and what theories can be used?
        - What does the dataset contain and how can we use it to answer the question?
        - How do we translate the question to a data science task?
        - What is the purpose of doing it and how will it be used?
        - How can we challenge the question and dataset? What is missing/controversial/biased, what are the EDI concerns? Multiple EDI issues embedded in the problem for attendees to point out and discuss.
    - Setup a GitHub repo and use it to document the conversation and outcome of the activity. Learn how to use Github as the basis of collaborative work.
