Machine learning for social data science
Module title | Machine learning for social data science |
---|---|
Module code | SSIM916 |
Academic year | 2023/4 |
Credits | 15 |
Module staff | Dr Travis Coan (Lecturer) |
Duration: Term | 1 | 2 | 3 |
---|---|---|---|
Duration: Weeks | 11 |
Number students taking module (anticipated) | 25 |
---|
Module description
Effective analytics in the era of “big data” requires researchers to have a wide range of tools at their disposal. This module focuses a set of tools that are essential for applied and academic social data scientists: machine learning methods for structured and unstructured data. This module provides a practical introduction to the ways in which machine learning methods are applied regression problems, classification tasks, and unsupervised clustering of large datasets.
Although there are no formal pre-requisites for taking the module, some (even limited) programming experience will be helpful. You will use the Python programming language to implement most of the tools introduced throughout the term. No prior knowledge of Python is assumed. You are encouraged to reach out to module convener with questions regarding Python or programming more generally.
Module aims - intentions of the module
There are two primary aims of the module. First, the module will provide an applied introduction to the use of machine learning methods to help answer social science questions. You are introduced to how these tools are used to generate predictions for continuous outcomes (i.e., regression), classify categorical outcomes, and cluster information. We will compare machine learning-based solutions to these problems to more standard statistical approaches to identify the strengths and weaknesses of each approach. Second, the module introduces you to the Python programming language. Python is a popular language for scientific computing and knowledge of Python will place you at a competitive advantage in industry, government, or when pursing further education.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Understand and apply a variety of machine learning methods to answer questions in social science and public policy
- 2. Critically evaluate the strengths and weaknesses of specific machine learning methods for answering research questions in the social sciences.
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 3. Employ text analytic methods to empirically evaluate theories and hypotheses in the social sciences.
- 4. Demonstrate a strong command of research design and analysis through written assessments.
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 5. Gain a solid foundation in the Python programming language.
- 6. Work independently and within a limited period to complete a specified task.
Syllabus plan
Although the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover the following topics:
- Machine learning methods for regression tasks
- Supervised learning methods for classification
- Performance metrics for regression and classification tasks
- Unsupervised learning and clustering
- An introduction to neural networks
- An introduction to transfer learning
- Bayesian approaches to solving machine learning problems
Learning activities and teaching methods (given in hours of study time)
Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
---|---|---|
22 | 128 |
Details of learning activities and teaching methods
Category | Hours of study time | Description |
---|---|---|
Scheduled Learning & Teaching activities | 22 | Weekly 2-hour lectures / seminars or 1 hour lecture + 1 hour seminar |
Guided Independent Study | 40 | Completing assigned readings |
Guided Independent Study | 58 | Preparation for and completion of practical assignments |
Guided Independent Study | 30 | Practicing techniques used in computer tutorials |
Formative assessment
Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
---|---|---|---|
2 short practical exercises | Between 2-4 tables, graphs, etc. with short descriptions | 1-6 | Written |
Summative assessment (% of credit)
Coursework | Written exams | Practical exams |
---|---|---|
100 | 0 | 0 |
Details of summative assessment
Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
---|---|---|---|---|
Assessment 1. Problem set covering several of the methods discussed in the module. | 50 | 1500 words with tables, figures, charts from analysis | 1-6 | Written |
Assessment 2. Problem set covering several of the methods discussed in the module. | 50 | 1500 words with tables, figures, charts from analysis | 1-6 | Written |
0 | ||||
0 | ||||
0 | ||||
0 |
Details of re-assessment (where required by referral or deferral)
Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
---|---|---|---|
Assessment 1 | Problem set covering several of the methods discussed in the module. 1500 words with tables, figures, charts from analysis | 1-6 | August/September re-assessment period |
Assessment 2 | Problem set covering several of the methods discussed in the module. 1500 words with tables, figures, charts from analysis | 1-6 | August/September re-assessment period |
Indicative learning resources - Basic reading
- Guido, S. and A. C. Mueller (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly.
- Swaroop C H, A Byte of Python. https://python.swaroopch.com.
Indicative learning resources - Web based and electronic resources
- ELE – College to provide hyperlink to appropriate pages
- Learn Python interactively online using Code School’s free Python course: https://www.codecademy.com/learn/python.
- Maths Refresher Course (Gary King) http://projects.iq.harvard.edu/prefresher
Indicative learning resources - Other resources
- Google Colaboratory -- https://research.google.com/colaboratory.
Credit value | 15 |
---|---|
Module ECTS | 7.5 |
Module pre-requisites | none |
Module co-requisites | SSIM Programming for Social Data Science |
NQF level (module) | 7 |
Available as distance learning? | No |
Origin date | 08/03/2022 |