| Unit name | Statistical Computing and Empirical Methods for Business |
|---|---|
| Unit code | SEMTM0038 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. D'Andrea |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Engineering |
Why is this unit important?
The aim of this unit is to provide you with a broad introduction to the principles of statistical computing and empirical methods using the R programming language. We will cover topics such as data wrangling and data exploration, statistical significance testing, parameter estimation, experimental design, and regression analysis.
These foundational skills are required so that you can understand, implement, and apply data science and statistical methods across the other units in their degree and so that you are well-prepared for working on projects that involve data. By getting you to work extensively in the R programming language, this unit will also serve to reinforce and broaden your existing skills with programming and software engineering.
How does this unit fit into your programme of study
Statistical Computing and Empirical Methods is a core unit in the Digital Health for you who come from a background in Computer Science (CS) or Engineering. This unit will equip you with the essential knowledge of data analysis and applied statistics that is commonly taught in STEM subjects such as physics, life sciences, psychology, or engineering mathematics, but are very rarely covered in any depth in CS or Engineering degrees. This unit is a foundational unit that provides essential knowledge and skills that will be used throughout the rest of the degree, especially in projects that involve working with data.
The curriculum for Statistical Computing and Empirical Methods was originally developed for the Data Science MSc and related degrees. This version of the unit is the specialised form for you taking the Data Science for Business MSc. The teaching will mostly be the same as the teaching for you on the Data Science MSc, but the examples used in classes and the exercises used in assessments may focus on applications in business.
An overview of content
Topics covered in this unit will include:
• Data wrangling and data visualisation
• R programming
• Sample statistics and exploratory data analysis
• Fundamental concepts in probability theory including probability spaces, random variables, probability density function, expectation
• Conditional probability and Bayes’ theorem
• Limit laws in probability
• Statistical hypothesis testing concepts including test statistics, p-value, test size, and statistical power
• Hypothesis testing for paired data and unpaired data
• Interval estimation and its relationship to hypothesis testing
• Foundations of statistical estimation including bias, variance, and consistency
• Parameter estimation including maximum likelihood estimation
• Challenges for experimental design including selection bias, confounding variables, and measurement error
• Experimental design with randomised intervention
• Fundamental concepts in supervised learning
• Linear and nonlinear classification models and the maximum likelihood principle for estimating parameters of these models.
• Linear and nonlinear regression methods
• Regularisation and hyperparameters
How will students, personally, be different as a result of the unit
This unit will equip you with the necessary skills in statistical computing to deal with data. You will understand key concepts and principles in different statistical techniques and apply them to draw valid scientific conclusions from data. You will also have a better understanding of the probabilistic models that underpin the statistical methods.
Learning outcomes
On successful completion of the unit, you will be able to:
1. Select and successfully apply appropriate statistical significance tests to evaluate a research hypothesis and demonstrate the ability to conduct simulation studies to investigate significance tests.
2. Select and employ appropriate tools to perform a variety of data wrangling tasks including the gathering and cleaning of tabular data sets.
3. Critically appraise scientific conclusions drawn from data with reference to concepts from the theory of experimental design and explain the relative merits of designed experiments compared with observational studies.
4. Describe the maximum likelihood approach to estimating the parameters of a statistical model and apply these concepts to basic supervised learning approaches.
5. Correctly use terminology and concepts from probability and describe how they relate to basic statistical techniques used in Data Science.
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities and self-directed exercises.
Tasks which help you learn and prepare you for summative tasks (formative):
You will complete weekly assignments and computer labs that will support you to gain a better understanding of the concepts and techniques as well as experience dealing with data. You will receive feedback from the teaching team through lab activities.
Tasks which count towards your unit mark (summative):
Coursework (100%) that takes the form of a data science report completed individually within Rmarkdown. This report will enable you to demonstrate their data wrangling and statistical skills and will assess all ILOs.
When assessment does not go to plan:
Re-assessment takes the same form as the original summative assessment.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. SEMTM0038).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
