Computer Science

ECMM429 - Introduction to Data Science (2019)

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MODULE TITLEIntroduction to Data Science CREDIT VALUE15
MODULE CODEECMM429 MODULE CONVENERProf Hywel Williams (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS
Number of Students Taking Module (anticipated) 20
DESCRIPTION - summary of the module content

***DATA SCIENCE AND DATA SCIENCE WITH BUSINESS STUDENTS ONLY***

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.

AIMS - intentions of the module

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 an intensive one-week residential block, 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 and visualisation. You will undertake individual and group presentations exploring the potential impact of data science in your own organisation and in wider society. Guest lectures from industrial data science practitioners and additional training opportunities will enrich the core  syllabus. Subsequent to the initial teaching week, you will complete the module through individual study and coursework, supported by the module staff.

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

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

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.

 

SYLLABUS PLAN - summary of the structure and academic content of the module

Topics (with associated exercises and seminar discussions):

  • 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
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 36.00 Guided Independent Study 114.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning & Teaching 12 Lectures
Scheduled Learning & Teaching 18 Practical work
Scheduled Learning & Teaching 6 Seminar and presentations
Guided Independent Study 50 Coursework
Guided Independent Study 64 Backgroup reading
     

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Feedback on practical work 18 hours All Oral
Feedback in seminar discussions 6 hours All  Oral
       
       
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 70 Written Exams 0 Practical Exams 30
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework 1 40 Report and Presentation 1,3,4,8,9,10 Written
Coursework 2 30 Report and Code 2,4,5,6,7,8 Written
Class Test (Pythorn and R) 30 2 x 30 minutes 2,4,5,6,7 Written
         
         

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Coursework 1 Report and Presentation 1,3,4,8,9,10 Before next academic year
Coursework 2 Report and Code 2,4,5,6,7,8 Before next academic year
Class test (Pythorn and R) 2 x 30 minutes 2,4,5,6,7 Before next academic year

 

RE-ASSESSMENT NOTES

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%.
 

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading:

 

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

 

Web based and Electronic Resources:

 

Other Resources:

 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Mayer-Schonberger V. & Cukier K. Big data: a revolution that will transform how we live work and John Murray 2013 [Library]
Set Schutt, R and O’Neill, C Doing Data Science: Straight Talk from the Frontline. O'Reilly 2014 [Library]
Set Downey, A.B. Think Stats. O'Reilly Media. 2014 [Library]
Set Grolemund, G. R for Data Science. O'Reilly Media. 2016 [Library]
Set A B Downey Think Python Green Tea Press/O'Reilly 2015 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 10 July 2018 LAST REVISION DATE Tuesday 18 December 2018
KEY WORDS SEARCH data science, machine learning, statistics, data governance, data visualisation, data exploration, social networks, text analysis, machine vision