Centre for Geo-Information Studies
MSc in Data Science
MA in Refugee Studies
MSc NGO and Development Management
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MA Conflict, Displacement and Human Security
M-Level Module – DS7006
Quantitative Data Analysis1
Semester A, 2021-22
1 This module is also part of the Research Skills Training for research degrees students
Welcome to this M-Level module on Quantitative Data Analysis. The module forms
part of the MSc in Data Science and a number of MA programmes. This guide
provides details of scheduled classes, aims and learning outcomes, approaches to
learning and teaching, assessment requirements and recommended reading for this
module. You will need to refer to it throughout the module. Further material may be
distributed during the course of the module via Moodle (https://moodle.uel.ac.uk/).
You should consult the relevant Programme Handbook for details of the regulations
governing your programme.
The formal taught sessions all occur in a single week to allow for immersive learning
of both theory and practice. All sessions require your attendance whether on-campus
or on Microsoft Teams. All materials can be accessed via Moodle. After the teaching
week, students are expected to go through all the work sheets again to reinforce their
learning and complete their portfolio. The reading for each session and overall in the
module spec below needs to be done. Finally there is a self-directed data analysis
project to be carried out to the timetable given below.
The overall curriculum, for practical reasons, segments the teaching and learning
process and preparing for research into a number of modules, and those modules
into sessions. These sessions will necessarily overlap as their content can be treated
from different perspectives and positions. Their content should be treated holistically
rather than piecemeal.
Remember to have your laptop ready for every session.
Dr. Yang Li
Room: KD2.28, Knowledge Dock Building
Telephone: 020 8223 2352
Email: [email protected]
Dr. Yang Li
Room: KD2.28, Knowledge Dock Building
Telephone: 020 8223 2603
Email: [email protected]
Where and when Docklands Campus, Microsoft Teams
|Monday 4th October||10:00-13:002||DL.3.04||14:00-17:00||DL.3.04|
|Tuesday 5th October||10:00-13:00||DL.3.04||14:00-17:00||DL.3.05|
|Wednesday 6th October||10:00-13:00||EB.1.42||14:00-17:00||DL.3.06|
|Thursday 7th October||10:00-13:00||ITC.03||14:00-17:00||EB.1.42|
|Friday 8th October||10:00-13:00||EB.1.62||14:00-17:00||EB.1.42|
All rooms are available for student-centred activity from 09:00 and 13:00
Rooms can be subject to change – please check with on-line calendar
M-level Credits: 30
2 All times are British Summer Time, GMT + 1:00
|Term 1 – 2020/21|
|Professor Allan Brimicombe||Docklands KD2.28, [email protected], ext. 2352|
|Dr Yang Li||Docklands KD2.28, [email protected], ext. 2603|
|session 1||session 2||session 3||session 4||session 5||session 6||session 7||session 8||session 9||session 10|
|Introduction||Introduction||Databases:||Exploratory||Measures of||Hypothesis||Sample Size||More t-test||Building||Building|
|R & SQLite||(nonparametric)||(parametric)||Multiple|
Note: There will be additional exercises on confidence intervals as Additional Topic 1 in Moodle.
3 All times are British Summer Time, GMT +1:00
|Assessment methods which enable student to demonstrate the learning
outcomes for the Unit:
Portfolio of laboratory exercise results.
An individual data analysis project report. 4000 words maximum + graphics,
charts, maps, scripts etc. Marks will be deducted pro-rata for reports that
exceed the word limit.
Portfolio of Laboratory Exercise Results Submit: 8th November 2021
Each topic in the course has a set of exercises to be carried out during the
supervised practical session and in your own time. The results of these
exercises and short reflections upon them should be entered into a portfolio.
The portfolio should be kept succinct and not bulked up with printed copies
of data sets etc. Marks will be awarded for completion of exercises,
appropriate presentation of the results (including conciseness) and for the
perceptiveness of your reflections.
|Individual Data Analysis Project
(see project flowchart in Moodle)
CSV data file: 17th November 2021
Draft: 13th December 2021
Final: 5th January 2022
You will be provided with data giving patterns of COVID-19 deaths by local
authority area in England. You should analyse these data using a range of
social and economic variables of your choice from the NOMIS Data Portal4
or elsewhere on data.gov.uk. You will integrate the data through an SQLite
database and extract data from it for analysis in R (extracted data as CSV
file to be submitted for review). The data set should be assessed for
reliability, explored, hypotheses raised and appropriately tested, regression
models built. The report should provide a clear understanding of:
▪ which dependent and independent variables you chose to use,
▪ which techniques you used and in which order,
▪ why you chose to apply each of these techniques,
▪ what outcome resulted at each stage of the analysis and what it means.
State clearly your hypotheses/research questions that you develop from the
literature and through the data exploration. Use tables and visualisations as
appropriate to present your analysis. Show the key elements of your SQL
and R scripts, organised in an appendix. Where texts and other background
reading are cited, a list of references should be provided using Harvard style.
Your report should tell the ‘narrative’ of your analysis project and what
variables seem to best explain the COVID-19 deaths such as in a regression
model rather than just being a ‘catalogue’ of things done to data.
There will be no Turnitin dropbox for the Portfolio.
Each part of the assessment should be submitted as a single Word or CSV file only. The file name
must contain your student number in the form: u1234567_DS7006_CW1.docx (or .doc) for the
portfolio and: u1234567_DS7006_CW2.docx (or .doc) for the project.
Assignments must be submitted through the Moodle dropbox before midnight on the due date.
4 https://www.nomisweb.co.uk/ https://data.gov.uk/
|Module Code: DS7006
Dr Yang Li
Additional tutor: Dr Yang Li
|Pre-requisite: None||Pre-cursor: None|
|Co-requisite: None||Excluded combinations:
|Suitable for incoming study
|Location of delivery: UEL – Block delivery of face-to-face teaching and practical sessions with
on-line support for learning and project work.
|Summary of module for applicants:
This module aims to provide an understanding of how quantitative data are analysed in
social science research, to develop the necessary practical skills through project work
using key software including Excel, and open source software packages R and SQLite, and
confidence in handling large quantitative datasets.
|Main topics of study:
▪ Quantitative research processes; relationship with qualitative research; mixed mode
▪ Sources of data and official statistics; handling large data sets.
▪ Data quality (metadata), cleaning and outlier detection; data integration issues.
▪ Building a database; database query and exporting tables to other software.
▪ Exploration of univariate, bivariate and multivariate relationships.
▪ Creating data visualisations: tables, graphs and maps.
▪ Probability: normal, binomial, Poisson distributions; Bayesian probability.
▪ Formulating and testing hypotheses: parametric (incl. ANOVA) and non-parametric
▪ Deriving statistical models: factor analysis, clustering, regression, decision trees;
▪ Presentation and evaluation of quantitative analyses.
|This module will be able to demonstrate at least one of the following examples/ exposures
(please tick one or more of the appropriate boxes, evidence will need to be provided later in
Live, applied project ☒
Company/engagement visits ☐
Company/industry sector endorsement/badging/sponsorship/award ☒
|Learning Outcomes for the module
Please use the appropriate headings to group the Learning Outcomes. While it is expected that
a module will have LOs covering a range of knowledge and skills, it is not necessary that all
four headings are covered in every module. Please delete any headings that are not relevant.
You should number the LOs sequentially to enable mapping of assessment tasks.
Where a LO meets one of the UEL core competencies, please put a code next to the LO that
links to the competence.
• Digital Proficiency – Code = (DP)
• Industry Connections – Code = (IC)
• Emotional Intelligence Development – Code = (EID)
• Social Intelligence Development – Code = (SID)
• Physical Intelligence Development – Code = (PID)
• Cultural Intelligence Development – Code = (CID)
• Community Connections – Code = (CC)
• UEL Give-Back – Code = (UGB)
|At the end of this module, students will be able to:
1 Demonstrate a high level of understanding of the benefits and limitations of
quantitative methods for promoting understanding and knowledge production in the
social sciences and their relationship to other methodological approaches
2 Demonstrate a high level of understanding of the dual role of exploratory and
confirmatory approaches to data analysis
3 Demonstrate a high level of understanding of the assumptions underlying parametric
and non-parametric approaches to statistical testing
4 Develop a strategy for data analysis (DP, PID)
5 Interpret in the context of domain and method, the results of quantitative analyses
6 Evaluate in the context of domain and method, published analytical results (IC)
Subject-based practical skills
7 Be proficient in the use of open source R and SQL-based database (DP)
8 Access data sources, build a database, conduct queries and export tables to other
9 Develop quantitative graphics for inclusion in papers and thesis (DP)
Skills for life and work (general skills)
10 Approach quantitative research methods and data handling with confidence (EID, PID)
11 Present quantitative analyses to technical and non-technical audiences (SID, CID)
|Teaching/ learning methods/strategies used to enable the achievement of learning outcomes:
For on campus students:
Integrated lectures and practical workshops with live demonstration of techniques that
students follow on their own laptop. Extensive use is made of the University’s virtual
learning environment. Feedback is provided throughout the module in the form of both
formative and summative work.
|Assessment methods which enable students
to demonstrate the learning outcomes for
the module; please define as necessary:
Portfolio of laboratory exercise results (1000
An individual data analysis project report. 4000
words + graphics, charts, maps, scripts etc.
|Indicative reading and resources for the module:
Bryman, A. (2008) Social Research Methods. Oxford University Press, Oxford
Cramer, D. (2003) Advanced Quantitative Data Analysis. Open University Press,
Elston, R.C. & Johnson, W.D. (2008) Basic Biostatistics for Geneticists and Epidemiologists.
Gaubatz, K. (2015) A Survivor’s Guide to R: an introduction for the uninitiated and the
unnerved. Sage, Thousand Oaks, CA.
Guidici, P. (2003) Applied Data Mining: Statistical Methods for Business and Industry.
|Hartwig, F. & Dearing, B. (1979) Exploratory Data Analysis. Sage, Thousand Oaks, CA.
Hand, D.J. (2008) Statistics: a very short introduction. Oxford University Press, Oxford.
Kreibich, J.A. (2010) Using SQLite. O’Reilly, Sebastopol, CA. Available from:
McCallum, Q. (2012) Bad Data Handbook, O’Reilly, Sebastopol, CA. Available from:
McCandless, D. (2009) Information is Beautiful. Harper Collins, London.
Navarro, D. (2021) Learning Statistics with R. Available from:
Neuman, W. (2006) Social Research Methods. 6/e, Pearson International, Boston.
Raper, S. (2017) Why good science is good business. Significance 14(1): 38-41
Tolmie, A; Muijs, D. & McAteer, E (2011) Quantitative Methods in Educational and Social
Research. OUP, Maidenhead.
Tufte, E. (1983) The Visual Display of Quantitative Information. Graphics Press, CT.
Zhao, Y. (2021) R and Data Mining: Examples and Case Studies. Elsevier. Available from:
|Provide evidence of how this module will be able to demonstrate at least one of the following
Live, applied project Individual data analysis based around a current affairs topic
Company/industry sector endorsement/badging/sponsorship/award ESRC recognition for
Doctoral Training Partnership
|Indicative learning and
(10 hrs per credit):
|Activity and hours (Defined as lectures, seminars, tutorials, project
supervision, demonstrations, practical classes and workshops,
supervised time in studio/workshop, fieldwork, external visits, work
based learning (not placements), formative assessment):
Lecture/seminar/practicals: 36 hours
On-line discussion of formative feedback and direction: 4
|2. Student learning time:||Activity (e.g. seminar reading and preparation/assignment preparation/
background reading/ on-line activities/group work/portfolio/diary
preparation, unsupervised studio work etc.):
Individual project work: 120 hours
Work completing portfolio of lab exercises: 60 hours
Reading for the main topics of study: 80 hours
|Total hours (1 and 2):||300 hours|
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