Data Science Foundations (ODL) STATS5095
- Academic Session: 2021-22
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 2
- Available to Visiting Students: No
- Available to Erasmus Students: No
- Taught Wholly by Distance Learning: Yes
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.
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
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.