Study information

Working with Data - 2024 entry

MODULE TITLEWorking with Data CREDIT VALUE15
MODULE CODEMTHM501 MODULE CONVENERProf Mark Kelson (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 5 (Oct start) 0 (Jan start) 0 (Oct start) 5 (Jan start) 0
Number of Students Taking Module (anticipated) 50
DESCRIPTION - summary of the module content

The ability to extract information from data as a basis for evidence-based decision is becoming increasing important across a wide variety of sectors in the world of big data, including industry, finance, health, and the environment. This module will equip you with the tools required to collate, import and manipulate data together with methods for basic inference. You will be introduced to different types and sources of data and the tools for performing initial data analysis including producing simple graphical summaries of data and more sophisticated methods for visualising structures in data. These techniques are crucial both as the basis for communication and informing more complex modelling.

Pre-requisites: None

AIMS - intentions of the module

The aim of this module is to equip students with the skills they will need to manipulate, analyse and interpret data appropriately. There will be an introduction to the technical skills that are required to import data from various sources and to format it in a way that allows efficient analysis. An important part of this will be the ability to merge information from multiple sources in order to answer questions and gain extra insight. The module will introduce techniques for performing data cleansing and knowledge discovery, ranging from simple summary statistics to sophisticated graphical representation of patterns in data. Learning these skills will be based on a combination of taught material and ‘hands-on’ sessions using R/RStudio. In addition to the technical aspects of working with data, the module will cover issues that affect the way we use data to inform decision-making; these include issues associated with data collection, bias, uncertainty, and missing data. The aim is that students have an appreciation of these concepts, and ways in which their effects might be mitigated, and are able to communicate possible issues with the analysis of data when writing reports and making recommendations based on statistical analyses.

Activities will include data wrangling, data analysis and report writing and presentation. Assessment will be based on a series of practical examples using real-world data examples that aim to demonstrate the full range of skills require to make effective use of data.

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 Demonstrate an understanding of how the source of data, and how it was collected, can have an effect on subsequent analyses;
2 Demonstrate the ability to import, manipulate and summarise data, including an understanding of the relative merits of different methods of formatting;
3 Use R/RStudio effectively in order to facilitate data wrangling and initial data analyses;
 
Discipline Specific Skills and Knowledge:
4 Demonstrate an understanding of data analyses;
5 Demonstrate an understanding of the important and practical use of the graphical representation of summaries of, and patterns in, data;
6 Understand some common pitfalls in data analysis and how to avoid them;
 
Personal and Key Transferable/ Employment Skills and Knowledge:
7 Data analysis skills;
8 Use of R/RStudio and other software;
9 Effective use of learning resources;
10 Report writing and presentation.
 
SYLLABUS PLAN - summary of the structure and academic content of the module
Topics will include:
• Sources of data;
• Types of data;
• Importing and manipulating data;
• Visualising data;
• Data formats;
• Sources of bias and uncertainty;
• Missing data;
• Knowledge discovery;
• Graphical representation of data;
• Summarising data;
• Introduction to the statistical analyses of data;
• Hypothesis testing
• Using data for evidence-based decision-making;
• Communicating results.
 
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 30 Guided Independent Study 120 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching Activities 10 Lectures
Scheduled Learning and Teaching Activities 20 Hands-on practical sessions
Guided Independent Study 36 Background reading
Guided Independent Study 84 Assessed data analyses, report writing and preparation for presentations

 

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
Assessed data analyses and reports from practical sessions (selected ones from the weekly sessions) 4 All Oral and Written

 

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
Coursework – extended piece of data analysis involving data collection, analysis and reporting 100 Max. 10 pages (plus appendices) All Oral and 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 All August Ref/Def Period
       
       

 

RE-ASSESSMENT NOTES

Reassessment will be by coursework in the failed or deferred element only.  For deferred candidates, the module mark will be uncapped.  For referred candidates, the module mark mark 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/

R for Data Science - Garrett Grolemund and Hadley Wickham

Statistics, A very short Introduction - David Hand

The art of statistics: Learning from Data - David Spiegelhalter

Discovering statistics using R – Andy Field, Jeremy Miles, and Zoe Field

R programming for Data Science – Roger Peng

Web Based and Electronic Resources:

Install Rstudio

Explore the exciting world of visualisation by looking at the R Graph Gallery - 'Art from Data'

Sign up to simplystatistics.org and blog.revolutionanalytics.com

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Grolemund, G. and Wickham, H. R for Data Science O'Reilly Media 2016 978-1491910399
Set Hand, D.J. Statistics: A Very Short Introduction 1st Oxford University Press 2008 978-0199233564
Set Spiegelhalter, D. The Art of Statistics: Learning from Data Pelican 2019 978-0241398630
Set Field, A., Miles, J. and Field, Z. Discovering Statistics Using R 1st SAGE Publications Ltd 2012 978-1446200469
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 12th March 2024 LAST REVISION DATE Tuesday 12th March 2024
KEY WORDS SEARCH None Defined

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