Introduction to Data Science - 2019 entry

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 and 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, and learn about ethical and governance aspects of data science.  Practical exercises, individual study and group work will consolidate your learning and provide the foundations for later study.

This module 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 evolve to reflect current issues in data science, but are likely to 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. The module will also cover ethics and governance around data science.

 Lectures will be accompanied by data analysis exercises and seminar discussions. A series of guided practical exercises will develop skills in programming (in Python and/or R), data handling and visualisation. You will undertake discussions and group presentations to explore aspects of data science and its impacts on society.

Assessment will include assessed practical exercises, presentations, and an investigation of a chosen aspect of data science.
 

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

Module Specific Skills and Knowledge

Discipline Specific Skills and Knowledge

Personal and Key Transferable / Employment Skills and Knowledge

Example topics (with associated exercises and seminar discussions):


The Data Revolution

Exploring Data with R and/or Python

Machine Learning and Statistics

Data in Society and Business

Social Networks and Text Analysis

High Performance Computing and 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 has been 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: