Introductory description

This module builds on the material introduced in Foundations of Data Science 1 to provide an enhanced set of data analysis tools including pattern prediction using machine learning. These enhanced tools provide a richer suite of techniques to explore and question real-world data.

This module is designed for those who have taken Foundations of Data Science 1, but who are otherwise not taking a mathematics or statistics course.

Prerequisite module Foundations of Data Science 1.

Pre-registration required. This module requires pre--registration. This takes place in Week 1 Term 1. This module takes place in Term 2. To pre-register please visit the module page and complete the pre-registration form.

Availability This module can be taken either at level 1 or level 2. Students interested in the level 2 version should consider ST239 Principles of Data Science 2. Students may not take both version of this module. Students in Year 3 must take ST239 Principles of Data Science 2.

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 statistical areas such as testing hypotheses, estimation, predication, inference and classification problems. These areas will be explored using real data sets and Python. Python will be leveraged as a tool to deliver a pipeline from data, to analysis, to conclusions to results presentation that supports decision-making under uncertainty.

Learning outcomes

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

• Apply Python to test hypotheses, estimate unknowns, make predictions and draw inferences.
• Evaluate the outcomes of data analysis to make judgements under uncertainty.
• Communicate the outcomes of an analysis for a variety of audience coherently and clearly in different forms.
• Describe the main principles underlying inference from 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.