Introduction to Data Science (Professional) - 2019 entry

*** This module is a “professional” version of the similar module ECMM443. It is intended to be taught in a short-fat format based around 3-day teaching blocks, as part of the MSc Data Science (Professional) programme. ***

In this module you will learn about the broad and fast-moving field of data science. You will be introduced to the core competencies and application areas associated with data science, including data handling & visualisation, machine learning, statistical modelling, social network analysis, text mining, machine vision and high-performance computing. You will also explore the ways in which data science is transforming business and society. Practical exercises, individual study and group work will consolidate your learning and provide the foundations for later study.

Pre-requisite modules: None.

Co-requisite modules: None.

This module is a core module for MSc Data Science (Professional) students.

This module provides the foundations for the MSc Data Science (Professional). It will cover the breadth of data science to equip students with the context and vocabulary to support more detailed study in future modules. Topics will include: The Data Revolution, Exploring Data, Machine Learning & Statistics, Data in Society & Business, Social Networks & Text Analysis, High Performance Computing & Data Architectures, Machine Vision, Information Security.

Most taught content will be delivered in four intensive 3-day teaching blocks, including lectures and practical work. Lectures will be accompanied by data analysis exercises and seminar discussions. A series of guided practical exercises will develop your skills in programming (in Python and/or R), data handling, statistics and visualisation. You will undertake presentations exploring the potential impact of data science in your own organisation and in wider society. Alongside the teaching blocks, you will complete the module through individual study and coursework, supported by the module staff.

Assessment will include assessed practical exercises and coursework.

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Discuss the roles and impact of data science in industry and society.

2. Demonstrate competence in handling and visualising complex datasets.

3. Describe some of the main topics and techniques used in data science.

Discipline Specific Skills and Knowledge

4. Identify some ethical issues associated with data science in society and business.

5. Use Python and/or R languages to explore data.

6. With some guidance, employ basic data science techniques to explore data.

7. With some guidance, use basic techniques in sub-disciplines of data science, such as machine learning, statistics, network analysis, machine vision and high-performance computing.

Personal and Key Transferable / Employment Skills and Knowledge

8. Communicate ideas and techniques fluently using written means in a manner appropriate to the intended audience.

9. Communicate ideas effectively in oral presentations.

10.  Work effectively as part of a team.

Topics (with associated exercises and seminar discussions) such as:

The Data Revolution

Exploring Data with R and/or Python

Machine Learning & Statistics

Data in Society & Business

Social Networks & Text Analysis

High Performance Computing & Data Architectures

Machine Vision

Information Security

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%. 

Basic reading:

ELE: http://vle.exeter.ac.uk/

Web based and Electronic Resources:

Other Resources:

Reading list for this module: