MAS8407 : Practical Statistics for Exploratory Data Analytics
- Offered for Year: 2025/26
- Module Leader(s): Dr James Bentham
- Lecturer: Dr Aamir Khan
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
Statistical methods are of crucial importance for data science. This module aims to first introduce the fundamental statistical and mathematical concepts and techniques underpinning modern computational statistics and data analysis. Furthermore, this module aims to provide students with the basic skills needed for statistical modelling, data analysis and computing that ground these statistics concepts in a variety of business cases to make statistically sound conclusions and data driven decisions to solve live commercial problems.
Outline Of Syllabus
This course will introduce both classical and Bayesian approaches to statistical inference, and where appropriate contrast them with one another. Relevant notions of probability will be introduced where appropriate. Topics covered will include parametric families of models, likelihood, hypothesis testing, and p-values. We will introduce Bayes’ theorem (both continuous and discrete), as well as the practical specification of priors and computation of posteriors. Classes of common statistical models will be considered, such as linear and generalised linear models. Focus will be spent on the use of computational techniques to conduct statistical analysis (such as random sampling, Monte Carlo, Markov chain Monte Carlo, and related techniques).
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 5 | 1:00 | 5:00 | Revision Lectures |
| Scheduled Learning And Teaching Activities | Lecture | 20 | 2:00 | 40:00 | Formal Lectures |
| Guided Independent Study | Assessment preparation and completion | 2 | 10:00 | 20:00 | Formative Exercise |
| Scheduled Learning And Teaching Activities | Lecture | 10 | 1:00 | 10:00 | Problem classes |
| Guided Independent Study | Project work | 85 | 1:00 | 85:00 | Main Project |
| Guided Independent Study | Independent study | 20 | 2:00 | 40:00 | Preparation time for lectures and consolidation of materials afterwards |
| Total | 200:00 |
Jointly Taught With
| Code | Title |
|---|---|
| CSC8643 | Data Management and Exploratory Data Analysis |
| MAS8600 | Graduate Foundations of Statistics and Data Science |
| MAS8504 | Graduate Foundations of Statistics and Data Science (Theory & Methods) |
| MAS8505 | Graduate Foundations of Statistics and Data Science (Applications) |
Teaching Rationale And Relationship
Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Problem classes are used to help develop the students’ abilities at applying the theory to solving problems. Practical classes are used to help the students’ ability to apply the methods in practice.
The teaching methods are appropriate to allow students to develop a wide range of skills. From understanding basic concepts and facts to higher-order thinking.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Oral Presentation | 1 | M | 10 | Recorded 5 minute presentation describing main findings |
| Report | 1 | M | 90 | Project report |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Report | 1 | M | A short technical report allowing learners to receive feedback on analytical techniques and report writing (max. 10 x A4 pages). |
| Report | 1 | M | A short technical report allowing learners to receive feedback on analytical techniques and report writing (max. 10 x A4 pages). |
Assessment Rationale And Relationship
An extended technical report allows learners to fully explore the potential applications of the statistical techniques learned in the module, as well as providing them with the opportunity to generate useful insights from the data they are analysing. The video presentation showcases their communication skills as well as their ability to identify the main useful/interesting aspects of their analysis to discuss. Formative assignments allow learners to receive feedback on their analytical report writing and analysis technique before the full summative assessment.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- MAS8407's Timetable