# Mathematics

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

MODULE TITLE CREDIT VALUE Introduction to Data Science and Statistical Modelling 15 MTHM502 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 Guided Independent Study Placement / Study Abroad 36 114 0
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 Written Exams Practical Exams 0 60 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

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