Introductory description

This module provides an introduction to Data Science. This module provides the opportunity to develop knowledge and explore that thriving area of Data Science. Our world is data rich and you will have the opportunity to combine data exploration with Python programming skills to question, explore and question this data.

This module is designed for those who have taken mathematics to A-level, but who are otherwise not taking a mathematics or statistics course. This module does not assume knowledge of Python and is not designed for those who have a strong knowledge of Python.

Availability This module can be taken either at level 1 or level 2. Students interested in the level 2 version should consider ST238 Principles of Data Science 1. Students may not take both version of this module. Students in Year 3 must take ST238 Principles of Data Science 1.

Module web page

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.

This module covers introductory statistics vital to any subsequent study of data. This includes areas such as causality, data structures, data presentation and visualisation, exploratory analysis, sampling, decisions and reasoning and decision-making with under uncertainty.

This is an indicative module outline only to give an indication of the sort of topics that may be covered.

• What is data science: skills, aims and process.
• Introduction to statistics: types of datasets and variables, notations, summary statistics for centrality and dispersion.
• Data structures: cleaning and wrangling.
• Principles of exploratory data analysis and data visualisation.
• Basics of probability distributions, sampling and simulation.
• Bivariate distributions: A/B testing and tests of association.
• Statistical fitting, least squares method and linear regression (interpretation and model validation).

Learning outcomes

By the end of the module, students should be able to:

• Use Python to explore data, presenting the results in a variety of forms.
• Apply Python to implement appropriate data techniques to gain insights into real-world data.
• Make judgements on the basis of data analysis and present those judgements coherently and clearly.
• Apply appropriate methods to summarise data.

Indicative reading list

Reading lists can be found in Talis

Specific reading list for the module

Research element

There will be the opportunity to conduct an analysis of a contemporary data science, drawing conclusions from this data set and presenting new insights.

Interdisciplinary

Data Science is an interdisciplinary endeavour leveraging mathematics, statistics, computer science to provide new opportunities to explore other disciplines. Data sets and students will be drawn together by a shared interest in data exploration.

Subject specific skills

• Select and apply appropriate data techniques.
• Create structured and coherent arguments communicating them in written form.
• Construct and develop logical arguments with clear identification of assumptions and conclusions.
• Communicate subject-specific information effectively and coherently.
• Analyse problems, abstracting their essential information formulating them using appropriate language to facilitate their solution.
• Select and apply appropriate statistical programming language for data analysis.
• Understand major aspects of data collection, generation, and quality, and how this influences analyses and conclusions.

Transferable skills

• Critical thinking: extracting patterns from incomplete data and using them to form evidence-based conclusions.
• Problem solving: use of logical reasoning to build arguments grounded in evidence and with explicit underlying assumptions.
• Self-awareness: monitoring of your own learning and seeking feedback.
• Communication: verbal discussion of ideas in seminars and among peers; written communication in assignments and the final project.
• Teamwork: collaboration with peers in seminars, during self-study and during the completion of extended tasks.
• Information literacy: evaluation of data and uncertainty in a model-based way.
• Digital literacy: use of computational tools to understand and visualise data, and to produce reports.
• Professionalism: self-motivation, taking charge of your own learning, and prioritising effectively.
• Ethics: reflect on professional responsibilities as a statistician in conjunction with the generation and dissemination of information.