Foundations of Data Science
Students explore the principal ideas and techniques used in data science, including supervised and unsupervised learning, prediction and classification, parametric and non-parametric approaches, and ensemble methods. The module combines conceptual understanding with practical problem-solving in Python and develops the ability to communicate insights from data clearly.
Prerequisites
To take this module you must first have completed Foundations of Econometrics.
Delivery
Live online lectures and tutorials, conducted at scheduled times and recorded for review.
Study Hours
Approximately 200 hours of study, of which 25 are contact hours and 175 are private study.
Software Used
Python
Credits
20 CATS credits
Module Lead
Programme Faculty
Key Outcomes
- —Handle and analyse diverse data types
- —Apply supervised and unsupervised machine learning methods
- —Build prediction and classification models
- —Use parametric, non-parametric and ensemble approaches
- —Evaluate and select appropriate models
- —Communicate insights from data clearly
Lecture Programme
- WK 01
Introduction to Data Science
- ·The data science workflow
- ·Applications and common challenges
- WK 02
Data types
- ·Working with different forms of data
- ·Preparing data for analysis
- WK 03
Statistics and machine learning basics
- ·Supervised and unsupervised learning
- ·Prediction and classification
- WK 04
Model evaluation and selection
- ·Parametric and non-parametric methods
- ·Ensemble methods
- ·Comparing model performance
Assessment
Online coursework submission with support from the module lecturer and tutor.
Build your qualification