Mathematics

MTHM502 - Introduction to Data Science and Statistical Modelling (2019)

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MODULE TITLEIntroduction to Data Science and Statistical Modelling CREDIT VALUE15
MODULE CODEMTHM502 MODULE CONVENER Dorottya Fekete (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11 0 0
Number of Students Taking Module (anticipated) 15
DESCRIPTION - summary of the module content

In this module you will learn the basics of statistical inference, including probability, sampling variability, hypothesis testing and how to identify patterns in data and to represent them using statistical models. You will learn the essential mathematical techniques that are required for the implementation and interpretation and statistical and machine learning methods. You will learn how to fit statistical models to data, to evaluate whether models are appropriate given the context of the data and how they can be used to quantify relationships and for prediction.

Pre-requisites: None

AIMS - intentions of the module

The aim of this module is to equip students with the skills they will need to perform data science techniques and statistical analysis and to understand and interpret the outputs. Initially the focus will be on understanding essential concepts in probability and mathematics that underpin statistical analysis. Statistical distributions will be explored and used as the basis of hypothesis testing, with an emphasis on how data can inform decision making. Regression modelling will be introduced as a method of understanding relationships between variables and for prediction. Model diagnostics and methods for assessing model fit will be used to evaluate whether regression models are fit for purpose. An introduction to machine learning and clustering techniques will be given, together with examples using real-world datasets.

Activities will include data analysis, regression modelling, machine learning and report writing and presentation. Assessment will be based on examination and practical examples using real-world data examples.

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 Understand principles of probability and sampling;

2 Apply statistical regression models to data, choosing the appropriate form based on the form and origins of the data

3 Perform regression and machine learning in R/RStudio

Discipline Specific Skills and Knowledge:

4 Understand random sampling and statistical distributions  

5 Understand  the methodology, and practical use, of regression modelling

6 Assess whether a regression model is appropriate in a given setting (model checking and diagnostics) and whether it provides an accurate representation of relationships within data

Personal and Key Transferable/ Employment Skills and Knowledge:

7 Statistical analysis skills;

8 Use R/RStudio and other software to implement statistical and data science methods

9 Use learning resources effectively

10 Communicate the results of data analysis clearly and accurately,  both in writing and verbally

SYLLABUS PLAN - summary of the structure and academic content of the module

Topics will include:

• Data and variables;

• Initial data analysis;

• Probability;

• Sampling;

• Statistical distributions;

• Hypothesis testing;

• Linear regression;

• Model selection;

• Non-parametric statistics;

• Machine learning;

• Clustering.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 36.00 Guided Independent Study 114.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning and Teaching Activities 24 Lectures
Scheduled Learning and Teaching Activities 12 Hands-on practical sessions
Guided Independent Study 50 Self-study & background reading
Guided Independent Study 64 Assessed data analyses, report writing

 

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 unassessed data analyses examples (which will include report writing) 24 All Oral

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 0 Written Exams 60 Practical Exams 40
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit 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) 40 1.5 hours x 4 All Oral and Written
Examination (Closed Book) 60 2 hours 1, 2, 4-7 Oral (on request)

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
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 re-assessment 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 was 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/

 

Faraway, J.J., Linear Models with R, (2nd edition), Chapman & Hall

Dobson, A.J., Introduction to Statistical Modelling, Springer

Heumann, C., Schomaker, M., Shalabh, Introduction to Statistics and Data Analysis: With Exercises, Solutions and Applications in R, Springer

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Faraway, J.J. Linear Models with R Chapman and Hall/CRC (Texts in Statistical Science) 2004 978-1584884255 [Library]
Set Dobson, A.J. Introduction to Statistical Modelling 1st Springer 1983 978-0412248603 [Library]
Set Heumann, C., Schomaker, M., Shalabh Introduction to Statistics and Data Analysis: With Exercises, Solutions and Applications in R 1st Springer 2016 978-3319834566 [Library]
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 Monday 17 June 2019 LAST REVISION DATE Friday 13 September 2019
KEY WORDS SEARCH None Defined