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

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; Association Rule Mining (Apriori algorithm).
Classification: Theoretical background; Decision Trees; Random Forest; KNN; Support Vector Machines; Neural Networks and Deep Learning; Model selection and evaluation.
Regression: Theoretical background; Linear models; Lasso Regression; Gaussian Process Regression; Model selection and evaluation.
Reinforcement Learning: Theoretical background, Q-learning, Deep Reinforcement Learning.
Ensemble Methods: Bagging; Boosting; Voting.
Natural Language Processing: Theoretical background, Sentiment Analysis, Text-Processing, NER, TF-IDF, Large Language Models.
Handling Imbalanced Data: Biasing the algorithm, Under Sampling, Oversampling, SMOTE.
Computational Complexity and High Performance Computing: Analysis of algorithms; Batch/Online algorithms
Machine Learning in Cloud: Fine tuning pre-trained models; SageMaker.

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 the different stages of data science projects.
• Implement optimised machine learning models and solutions and interpret, evaluate and critique the results.
• Develop comprehension of the core topics of data science, machine learning and artificial intelligence.
• Collaboratively implement and present a data science project, using optimised machine learning techniques, and interpret the results.

Indicative reading list

• Burns, S. (2019). Python machine learning: Machine learning and deep learning with python, scikit-learn, TensorFlow : Step-by-step tutorial for beginners. publisher not identified.
• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. The MIT Press.
• Grus, J. (2019). Data science from scratch: First principles with python (Second ed.). O'Reilly Media.
• Swamynathan, M. (2019). Mastering machine learning with python in six steps: A practical implementation guide to predictive data analytics using python (2nd ed.). Apress L.P. https://doi.org/10.1007/978-1-4842-4947-5

View reading list on Talis Aspire

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