Statistics and Mathematics for Business Analytics
This module will cover a range of mathematical methods that are used in business analytics, including key principles in statistics, econometrics, probability and algebra. These will form the foundation of analytical methods that you will explore and apply in later modules.
Full module specification
|Module title:||Statistics and Mathematics for Business Analytics|
This module is closed to MSc Business Analytics only
|Duration of module:||
Duration (weeks) - term 1: |
The module aims to enhance your ability to understand the math and statistics behind analysing a business problem. The student will be able to observe and interpret mathematical concepts in business and economics literature as well as to prepare a business/consulting report with the appropriate mathematical and statistical techniques.
ILO: Module-specific skills
- 1. P1: Demonstrate knowledge and understanding of fundamental, and domain-specific, analytics methods and tools.
- 2. P5: Create, manage, interrogate, interpret and visualise data from a wide range of different sources, types and including structured and unstructured forms.
ILO: Discipline-specific skills
- 3. P6: Critically analyse the use of data within a business context, identifying strengths and limitations.
- 4. P7: Critically analyse and interpret relevant academic, technical and industry literature.
ILO: Personal and key skills
- 5. P14: Technological and digital literacy: Our graduates are able to use technologies to source, process and communicate information.
Learning activities and teaching methods (given in hours of study time)
|Scheduled Learning and Teaching Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Scheduled learning and teaching activities||36||Lectures and Workshops|
|Guided Independent Study||40||Preparatory reading prior to workshops and lectures|
|Guided Independent Study||44||Practice use of software and concepts from additional exercises and examples|
|Guided Independent Study||30||Individual reading and study time for development of individual report.|
|Form of assessment||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|In-class exercises||During class hours||1-3||Verbally|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Coursework exercise||30||2 hours duration||1-5||Electronic, written comments|
|Individual report||70||3,000 words||1-5||Electronic, written comments|
Details of re-assessment (where required by referral or deferral)
|Original form of assessment||Form of re-assessment||ILOs re-assessed||Timescale for re-assessment|
|Coursework exercise||Coursework exercise (30%)||1-5||Summer reassessment period|
|Individual report||Individual report (70%)||1-5||Summer reassessment period|
Re-assessment will be in nature to the original assessment, but the topic, data, and materials must be new.
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.
The following content will be covered during the course:
- Visualize set operations using Venn diagrams.
- Understand how counting is used to compute probabilities.
- Be able to define and identify the roles of prior probability, likelihood (Bayes term), posterior probability, data and hypothesis in the application of Bayes’ Theorem. Students will be exposed to how fallacies and incoherent decision-making process can be avoided by applying Bayes theorem.
- Apply Principal Component Analysis (PCA) for high dimensional data.
- Linear and Logistic Regression – establish how data fits an underlying pattern and its real-world applications.
- Time-series forecasting – ability to provide basic forecasts for time series data.
Test of differences between populations. Students will be able to apply appropriate techniques to various situations when investigating differences across groups and attributes.
Indicative learning resources - Basic reading
The following content may be useful references for this course:
- Stinerock, R. (2018). Statistics with R: A beginner's guide. Sage.
- Weiers, R. M. (2010). Introduction to business statistics (7th ed.). Mason, OH: South-Western Cengage Learning.
- Shmueli, G., & Lichtendahl Jr, K. C. (2018). Practical time series forecasting with R: A hands-on guide (2nd ed.). Axelrod Schnall Publishers.
Module has an active ELE page?
Indicative learning resources - Web based and electronic resources
The following websites may also be useful:
- Official page for R software: http://www.r-project.org
- Download page: http://www.cran.r-project.org
- Official page for R studio: http://rstudio.com
- Download page for RStudio Desktop: https://rstudio.com/products/rstudio/download/#download
Last revision date