Study information

Introduction to Data Science - 2024 entry

MODULE TITLEIntroduction to Data Science CREDIT VALUE15
MODULE CODEECMM443 MODULE CONVENERDr Xiaoyang Wang (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, providing student with the tools to formulate data science problems and construct pipelines to begin to solve them technically.
 
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.
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. Design a data science pipeline for a given problem in a chosen domain.
 
2. Investigate a dataset using mathematical and visualisation tools.
 
 
Discipline Specific Skills and Knowledge
 
3. Apply principles of statistica;l pattern recognition to a given problem.
 
4. Articulate a decision problem to be solved with data science affecting business or society
 
Personal and Key Transferable / Employment Skills and Knowledge
 
5. Critically read and report on research papers.
 
6. Present the results of a piece of data science work in the form of a report.
SYLLABUS PLAN - summary of the structure and academic content of the module
Example topics (with associated exercises and seminar discussions):
  • Data in society and business
  • Statistics
  • Linear algebra
  • Probability
  • Data visualisation
  • Clustering, classification and regression
  • Decision trees
  • Neural networks

 

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 33 Guided Independent Study 117 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching 33 Lectures, Practicals, Seminars
Guided Independent Study 50 Assessment
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
Wqorkshop exercises 2 hours per week 1-6 Model answers and verbal feedback
       
       
       
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 20 Written Exams 0 Practical Exams 80
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Exam 80 1 hour 1-4 Written
Continuous assessment 20 49 hours 1-6 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  Coursework 1-6 Completed iver the Summer with a deadline in August
       

 

RE-ASSESSMENT NOTES

Reassessment will be by coursework and/or test in the failed or deferred element only. For referred candidates, the module mark will be capped at 50%. For deferred candidates, the module mark will be uncapped.

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: 

  • Connolly, Thomas and Begg, Carolyn, “Database Systems: A Practical Approach to Design, Implementation and Management”, Pearson, 2015. ISBN: 1292061189.

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 Thursday 14th March 2024 LAST REVISION DATE Thursday 16th May 2024
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.