Computer Science

ECMM455 - Introduction to Data Science (Professional) (2023)

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MODULE TITLEIntroduction to Data Science (Professional) CREDIT VALUE15
MODULE CODEECMM455 MODULE CONVENERDr Rudy Arthur (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11 0 0
Number of Students Taking Module (anticipated) 30
DESCRIPTION - summary of the module content
 
*** 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 and text mining. 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.
 
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), 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.
 
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 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.
 
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) such as:
  • The Data Revolution
  • Exploring Data with Python
  • Machine Learning & Statistics
  • Data in Society & Business
  • Social Networks & Text Analysis
 
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 36.00 Guided Independent Study 40.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities

36.00

Guided Independent Study

40.00

Placement / Study Abroad

0.00

 

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 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Report 75% ~3000-word report
1, 2, 3, 4, 5, 6, 7, 8, 9, 10
 
 Written
Presentation 25% ~5-min presentation
1, 2, 6, 7, 8, 9
 
 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 Report and presentation 1,2,3,4,5,6,7,8,9,10 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 Downey, A.B. Think Python Green Tea Press/O'Reilly 2015 [Library]
Set Schutt, R. and O’Neill, C. Doing Data Science: Straight Talk from the Frontline O'Reilly 2014 [Library]
Set Mayer-Schonberger V. & Cukier K. Big data: a revolution that will transform how we live work and John Murray 2013 [Library]
Set Downey, A.B. Think Stats. O'Reilly Media. 2014 [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 06 August 2019 LAST REVISION DATE Wednesday 18 January 2023
KEY WORDS SEARCH data science, machine learning, statistics, data governance, data visualisation, data exploration, social networks, text analysis