Probability, Statistics and Data - 2025 entry
MODULE TITLE | Probability, Statistics and Data | CREDIT VALUE | 30 |
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MODULE CODE | MTH1004 | MODULE CONVENER | Dr Chaitra H. Nagaraja (Coordinator) |
DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS | 11 | 11 | 0 |
Number of Students Taking Module (anticipated) | 275 |
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Our ability to collect and analyse data is increasingly driving our world. Statistics is concerned with both the practice of analysing data to learn about the world, and the theory that underpins the methods and models used for data collection and analysis. This theory is itself based on probability, the mathematics of chance and uncertainty. In this module, you will learn about the mathematics of combinatorics and probability, and the key ideas of statistical modelling and inference, in which probability is used to quantify uncertainty. You will also gain experience of employing these ideas to analyse data using statistical software such as the R programming environment. The module develops key ideas and techniques that form the foundation of modules such as MTH2006 Statistical Modelling and Inference.
The aim of this module is to introduce you to basic topics in probability, statistics and data analysis. This module provides the foundation for the second-year stream in Statistical Modelling and Inference, and subsequent modules in statistics in years 3 and 4.
On successful completion of this module you should be able to:
Module Specific Skills and Knowledge:
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demonstrate a sound understanding of selected essential topics in probability theory, including the ability to apply those concepts in tackling an appropriate range of problems;
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demonstrate a knowledge of the basic ideas of statistical inference, including probability distributions, point and interval estimation and hypothesis tests;
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use the statistical programming environment R to manipulate, visualise and analyse data.
Discipline Specific Skills and Knowledge:
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show sufficient knowledge of fundamental mathematical and statistical concepts, manipulations and results.
Personal and Key Transferable/ Employment Skills and Knowledge:
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reason using abstract ideas, formulate and solve problems and communicate reasoning and solutions effectively in writing;
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use learning resources appropriately;
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exhibit self-management and time-management skills.
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data wrangling (including merging data sets)
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data science framework,
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ethics for data collection, analysis, and dissemination
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data quality
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fundamentals of experimental design and survey sampling
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interpreting statistical analyses
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data visualisation
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probability theory and applications;
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random variables and moments;
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discrete and continuous distributions;
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bivariate and multivariate distributions;
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parametric statistical models;
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prediction and simulation;
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applications of models;
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combinations of random variables;
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transformation of random variables;
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point estimation;
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interval estimation;
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hypothesis testing.
Scheduled Learning & Teaching Activities | 88 | Guided Independent Study | 212 | Placement / Study Abroad |
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Category |
Hours of study time |
Description |
Scheduled learning and teaching activities |
66 |
Lectures |
Scheduled learning and teaching activities |
11 |
Practical classes in a computer lab |
Scheduled learning and teaching activities |
11 |
Tutorials |
Guided independent study |
212 |
Guided independent study |
Form of Assessment |
Size of Assessment (e.g. duration/length) |
ILOs Assessed |
Feedback Method |
---|---|---|---|
Weekly theoretical and practical exercises |
1 hour each week |
All |
Class feedback |
Report 1 practice |
4 hours |
All |
Class feedback |
Report 2 practice |
4 hours |
All |
Class feedback |
Coursework | 30 | Written Exams | 70 | Practical Exams |
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Form of Assessment |
% of Credit |
Size of Assessment (e.g. duration/length) |
ILOs Assessed |
Feedback Method |
---|---|---|---|---|
Written exam – closed book |
60 |
2 hours (Summer) |
1, 2, 4-7 |
Via SRS |
Report 1 |
15 |
Short report, about two pages |
All |
Feedback sheet |
Report 2 |
15 |
Short report, about four pages |
All |
Feedback sheet |
Mid-term Test 1 |
5 |
40 minutes |
All |
Via SRS |
Mid-term Test 2 |
5 |
40 minutes |
All |
Via SRS |
Original Form of Assessment |
Form of Re-assessment |
ILOs Re-assessed |
Time Scale for Re-reassessment |
---|---|---|---|
Written Exam |
*Written exam (2 hours) |
1,2,4-7 |
Referral/deferral period |
Report 1* |
Report 1 |
All |
Referral/deferral period |
Report 2* |
Report 2 |
All |
Referral/deferral period |
Mid-term test 1* |
Mid term test 1 |
All |
Referral/deferral period |
Mid-term test 2* |
Mid term test 2 |
All |
Referral/deferral period |
*Please refer to reassessment notes for details on deferral vs. Referral reassessment
Deferrals: Reassessment will be by coursework and/or exam in the failed or deferred element only. For deferred candidates, the module mark will be uncapped.
Referrals: Reassessment will be by a single written exam worth 100% of the module only. As it is a referral, the mark will be capped at 40%.
information that you are expected to consult. Further guidance will be provided by the Module Convener
- ELE
Reading list for this module:
Type | Author | Title | Edition | Publisher | Year | ISBN |
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Set | McColl, J. | Probability | Arnold | 1995 | 0000340614269 | |
Set | Grolemund, G. and Wickham, H. | R for Data Science | O'Reilly Media | 2016 | 978-1491910399 | |
Set | Rice, J A | Mathematical Statistics and Data Analysis | 3rd | Brooks Cole | 2007 | 978-0495118688 |
CREDIT VALUE | 30 | ECTS VALUE | 15 |
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PRE-REQUISITE MODULES | None |
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CO-REQUISITE MODULES | None |
NQF LEVEL (FHEQ) | 4 | AVAILABLE AS DISTANCE LEARNING | No |
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ORIGIN DATE | Tuesday 12th March 2024 | LAST REVISION DATE | Monday 31st March 2025 |
KEY WORDS SEARCH | Probability; probability distributions; random variables; statistics; inference; estimation; prediction; simulation; data analysis; data visualisation; R. |
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Please note that all modules are subject to change, please get in touch if you have any questions about this module.