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| Category | Assignment | Subject | Science |
|---|---|---|---|
| University | University of East London (UEL) | Module Title | DS7006 Quantitative Data Analysis |
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.
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.
Where and when Docklands Campus, Microsoft Teams
|
Date |
Time |
Room |
Time |
Room |
|
Wednesday 1st October |
09:00-12:002 |
WB.G.02 |
13:00-16:00 |
EB.2.43 |
|
Wednesday 8th October |
09:00-12:00 |
EB.2.44 |
13:00-16:00 |
EB.2.43 |
|
Wednesday 15th October |
09:00-12:00 |
WB.G.02 |
13:00-16:00 |
EB.2.43 |
|
Wednesday 22nd October |
09:00-12:00 |
EB.2.44 |
13:00-16:00 |
EB.2.43 |
|
Wednesday 29th October |
09:00-12:00 |
EB.2.44 |
13:00-16:00 |
EB.2.43 |
All rooms are available for student-centred activity from 09:00 and 15:00
|
session 1 |
session 2 |
session 3 |
session 4 |
session 5 |
session 6 |
session 7 |
session 8 |
session 9 |
session 10 |
|
01-Oct-25 |
01-Oct-25 |
08-Oct-25 |
08-Oct-25 |
15-Oct-25 |
15-Oct-25 |
22-Oct-25 |
22-Oct-25 |
29-Oct-25 |
29-Oct-25 |
|
09:00-12:003 |
13:00-16:00 |
09:00-12:00 |
13:00-16:00 |
09:00-12:00 |
13:00-16:00 |
09:00-12:00 |
13:00-16:00 |
09:00-12:00 |
13:00-16:00 |
|
Introduction |
Introduction |
Databases: |
Exploratory |
Measures of |
Hypothesis |
Sample Size |
More t-test |
Building |
Building |
|
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to R |
|
Data |
Central |
Formulation |
|
|
Models |
Models |
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Res. Design |
|
Building |
Analysis |
Tendency |
Significance |
Significance |
Building |
Factor |
Regression |
|
Trends: |
Installing |
Joining |
Correlation |
Probability |
Testing |
Testing |
Models |
Analysis |
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|
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R & SQLite |
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|
|
(nonparametric) |
(parametric) |
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Multiple |
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Standardising |
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Querying |
Making Maps |
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|
|
ANOVA |
Clustering |
regression |
|
Indexing |
|
|
|
|
|
|
|
|
|
|
WB.G.02 |
EB.2.43 |
EB.2.44 |
EB.2.43 |
WB.G.02 |
EB.2.43 |
EB.2.44 |
EB.2.43 |
EB.2.44 |
EB.2.43 |
|
Assessment methods which enable student to demonstrate the learning outcomes for the Unit: Portfolio of laboratory exercise results. (no word limit) An individual data analysis project report. 4000 words maximum (abstract to conclusion) + graphics, charts, maps, scripts etc. Marks will be deducted pro- rata for reports that exceed the word limit. |
Weighting: 40% 60% |
Submit: 10th November 2025
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.
Submit:
(see project flowchart in Moodle) CSV data file: 17th November 2025 Draft: 8th December 2025 Final: 5th January 2026
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:
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 for the portfolio and: u1234567_DS7006_CW2.docx for the project.
Assignments must be submitted through the Moodle dropbox before midnight on the due date.
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Request to Buy Answer|
Module Title: |
Module Code: DS7006 |
Module Leader: |
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Quantitative Data Analysis (QDA) |
Level: 7 Credit: 30 |
Dr Yang Li Additional tutor: Dr Yang Li |
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ECTS credit: |
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Pre-requisite: |
None |
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Pre-cursor: None |
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Co-requisite: |
None |
Excluded combinations: None |
Suitable for incoming study abroad? N |
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Location of delivery: UEL – Block delivery of face-to-face teaching and practical sessions with on-line support for learning and project work. |
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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. |
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Main topics of study: § Quantitative research processes; relationship with qualitative research; mixed mode approaches. § 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 techniques. § Deriving statistical models: factor analysis, clustering, regression, decision trees; multi-level models. § Presentation and evaluation of quantitative analyses. |
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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 this document) Live, applied project ☒ Company/engagement visits ☐ Company/industry sector endorsement/badging/sponsorship/award ☒ |
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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.
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At the end of this module, students will be able to: Knowledge 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 Thinking skills 4 Develop a strategy for data analysis (DP, PID) 5 Interpret in the context of domain and method, the results of quantitative analyses (EID) 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 software (DP) 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) |
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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. |
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Assessment methods which enable students to demonstrate the learning outcomes for the module; please define as necessary: Portfolio of laboratory exercise results (1000 words). An individual data analysis project report. 4000 words + graphics, charts, maps, scripts etc. |
Weighting: 40% 60% |
Learning Outcomes demonstrated: 2,3,4,5,6,7,9 1,4,5,6,7,8,9,10,11 |
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Provide evidence of how this module will be able to demonstrate at least one of the following examples/ exposures Live, applied project Individual data analysis based around a current affairs topic Company/engagement visits Company/industry sector endorsement/badging/sponsorship/award ESRC recognition for Doctoral Training Partnership |
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Indicative learning and teaching time (10 hrs per credit): |
Activity |
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1. Student/tutor interaction: |
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 hours |
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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 |
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Total hours (1 and 2): |
300 hours |
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