Data Science Foundations (ODL) STATS5095

  • Academic Session: 2023-24
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Taught Wholly by Distance Learning: Yes

Short Description

This course introduces students to data analytics and data science as well as different approaches to learning from data and provides an introduction to statistical model-based inference.


The course mostly consists of asynchronous teaching material.

Requirements of Entry

The course is only available to online-distance learning students on the PGCert/PGDip/MSc in Data Analytics for Government.

Excluded Courses

Inference 3

Statistics 3I: Inference

Statistical Inference (Level M)

Learning from Data - Data Science Foundations (ODL)




100% Continuous Assessment

The continuous assessment will typically be made up of one class test, a report, and three homework exercises, including online quizzes. Full details are provided in the programme handbook..

Main Assessment In: April/May

Course Aims

The aims of this course are:

■ to introduce students to different types of data and different approaches to learning from data;

■ to introduce students to data visualisation;

■ to present the fundamental principles of likelihood-based inference, interval estimation and hypothesis testing;

■ to introduce Bayesian inference;

■ to show students how to implement these statistical methods using R.

Intended Learning Outcomes of Course

By the end of this course students will be able to:


■ explain different types of data and data structures and discuss advantages and challenges of using data of different types in a given context;

■ describe different ways of collecting data and discuss advantages and challenges of using data obtained from different sources in a given context;

■ describe and visualise structured and unstructured data of different types using suitable summaries and plots;

■ explain different approaches to learning from data and discuss their advantages and disadvantages in a given context;

■ define and contrast population and sample, parameter and estimate

■ write down and justify criteria required of 'good' point estimators, and check whether or not a proposed estimator within a stated statistical model satisfies these criteria;

■ apply the principle of maximum likelihood to obtain point and interval estimates of parameters in statistical models, making appropriate use of numerical methods for optimisation;

■ formulate and carry out hypothesis tests in Normal models, as well as general likelihood-based models, correctly using the terms null hypothesis, alternative hypothesis, test statistic, rejection region, significance level, power, p-value;

■ describe the rules for updating prior distributions in the presence of data, and for calculating posterior predictive distributions;

■ implement these statistical methods using the R computer package;

frame statistical conclusions from interval estimates and hypothesis tests clearly.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.