WM931-15 Data Science & Machine Learning
Introductory description
Data Science and Machine Learning have become key drivers of business change and value generation in the modern digital economy. The ability to derive insights, recommendations and automate actions from a wide range of datasets (traditional and non-traditional - i.e. Big Data) is integral to the competitive advantage of many of the world's largest businesses. This module provides practical exposure to these methods, as well as the underlying theories and concepts.
Module aims
This module aims to enable participants to select, implement and evaluate machine learning algorithms in data science. In particular, the module highlights several of the most common, and in-demand, modern algorithms including classification, regression, clustering, dimension reduction and ensemble methods. Alongside technical knowledge, participants should develop an understanding of the applicability of different types of machine learning to common problems, and best practice for data science and Big Data analytics projects.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Data Science Foundations: Core concepts of Data Science & Machine Learning; Data pre-processing & feature engineering.
Unsupervised learning: K-means clustering; DBSCAN; Principal Component Analysis
Classification: Theoretical background; Naïve Bayes; Decision Trees; Support Vector Machines; Model selection and evaluation.
Regression: Theoretical background; Linear models; Ridge Regression; Lasso Regression; Gaussian Process Regression; Model selection and evaluation.
Ensemble Methods: Bagging; Boosting; Voting.
Computational Complexity and High Performance Computing: Analysis of algorithms; Apache Spark and PySpark; Batch/Online algorithms
Learning outcomes
By the end of the module, students should be able to:
- Interpret and evaluate various use-cases and the applicability of data science and machine learning.
- Develop a comprehensive understanding of best practices for data processing and feature engineering.
- Implement, interpret and critique current, professional standard learning models.
- Automate deployment-ready data science pipelines and algorithms.
- Evaluate and interpret the results of machine learning models and tune them to optimise performance.
- Develop comprehension of the core topics of data science, machine learning and artificial intelligence.
Interdisciplinary
Statistics and computer science topics
International
Data science topics/skills are of high international demand
Subject specific skills
Data science, machine learning, statistics, ensemble learning, software development, data analysis
Transferable skills
Programming, statistics and modelling, team work, critical analysis
Study time
| Type | Required |
|---|---|
| Lectures | 12 sessions of 1 hour (8%) |
| Seminars | 10 sessions of 1 hour (7%) |
| Practical classes | 8 sessions of 1 hour (5%) |
| Online learning (independent) | 15 sessions of 1 hour (10%) |
| Assessment | 105 hours (70%) |
| Total | 150 hours |
Private study description
No private study requirements defined for this module.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group A
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Model Development | 10% | 7 hours 30 minutes | No |
|
In teams, participants create a data science solution on a real-world dataset and present their approach |
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| Post Module Assignment | 80% | 90 hours | Yes (extension) |
|
A two part submission - the first an essay-style question on a data science/machine learning topic; the second a working program that can model a given dataset |
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| Feature engineering programming task | 10% | 7 hours 30 minutes | No |
|
Limited time test of programming and data skills via a feature engineering task |
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Assessment group R
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Post Module Assignment | 100% | Yes (extension) | |
|
A two part submission - the first an essay-style question on a data science/machine learning topic; the second a working program that can model a given dataset |
|||
Feedback on assessment
For In-module work – test scores, verbal feedback after presentation
For post module work - Annotated scripts returned to students, generic written feedback to
group.
Courses
This module is Optional for:
- Year 1 of TWMS-H1SH Postgraduate Taught Cyber Security Management (Full-time)
- Year 1 of TESA-H7PK Postgraduate Taught e-Business Management