Study information

Introduction to Data Science - 2021 entry

MODULE TITLEIntroduction to Data Science CREDIT VALUE15
MODULE CODEECMM443 MODULE CONVENERDr Rudy Arthur (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated) 30
DESCRIPTION - summary of the module content
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. 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.
 
 
AIMS - intentions of the module
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. 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), 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.
 
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, exploring 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, 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
Example topics (with associated exercises and seminar discussions):
 
The Data Revolution
 
Exploring Data with Python
 
Machine Learning and Statistics
 
Data in Society and Business
 
Social Networks and Text Analysis
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 36 Guided Independent Study 114 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching 36 Lectures, Practicals, Seminars
Guided Independent Study 51 Coursework
Guided Independent Study 63 Self-study and Background 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 60 Written Exams 0 Practical Exams 40
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework  60 Report and presentation 1,2,3,4,5,6,7,8,9,10 Written
Class Test 40 1 hour 2,4,5,6,7,8 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 Summer reassessment period with an August deadline
Class test Class test, 1 hour 2,4,5,6,7,8 Summer reassessment period with an August deadline

 

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 has been 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
Set Downey, A.B. Think Stats. O'Reilly Media. 2014
Set Mayer-Schonberger V. & Cukier K. Big data: a revolution that will transform how we live work and John Murray 2013
Set Marr, B. Big Data in Practice Wiley 2016
Set Downey, A.B. Think Python Green Tea Press/O'Reilly 2015
Set Schutt, R. and O’Neill, C. Doing Data Science: Straight Talk from the Frontline O'Reilly 2014
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 10th July 2018 LAST REVISION DATE Tuesday 18th May 2021
KEY WORDS SEARCH data science, machine learning, statistics, data governance, data visualisation, data exploration, social networks, text analysis

Please note that all modules are subject to change, please get in touch if you have any questions about this module.