Study information

# Introduction to Data Science and Statistical Modelling - 2023 entry

MODULE TITLE CREDIT VALUE Introduction to Data Science and Statistical Modelling 15 MTHM502 Dr Dorottya Fekete (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 5 (October start) / 0 (January start) 0 (October start) / 5 (January start) 0
 Number of Students Taking Module (anticipated) 50
DESCRIPTION - summary of the module content

In this module you will learn the basics of statistical inference, including probability, sampling variability, confidence intervals 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 of 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 statistical inference, 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.

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:

1. Understand random sampling and statistical distributions
2. Understand the methodology, and practical use, of regression modelling
3. 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:

1. Statistical analysis skills;
2. Use R/RStudio and other software to implement statistical and data science methods
3. Use learning resources effectively
4. 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;
• Point estimation and confidence intervals
• Linear regression;
• Model selection;
• Non-parametric statistics.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
 Scheduled Learning & Teaching Activities Guided Independent Study Placement / Study Abroad 30 120 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
 Category Hours of study time Description Scheduled Learning and Teaching Activities 20 Lectures Scheduled Learning and Teaching Activities 10 Hands-on practical sessions Guided Independent Study 56 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
Online quizzes 4 x 1 hour All Electronic

SUMMATIVE ASSESSMENT (% of credit)
 Coursework Written Exams Practical Exams 100 0 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method

Examination (closed book) 100 2 hours All Electronic

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
Examination (closed book) Class test All August Ref/Def Period

RE-ASSESSMENT NOTES

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

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

A First Course in Probability. Sheldon Ross. Pearson

Schaum's outline of probability and statistics. Spiegel, Murray R., John Schiller, Alu Srinivasan. McGraw-Hill

Introduction to Probability with Statistical Applications. Geza Schay. Birkhauser