Unit information: Statistical Computing 1 in 2022/23

Unit name	Statistical Computing 1
Unit code	MATHM0039
Credit points	20
Level of study	M/7
Teaching block(s)	Teaching Block 1 (weeks 1 - 12)
Unit director	Professor. Anthony Lee
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 Mathematics
Faculty	Faculty of Science

Unit Information

Lecturers: Anthony Lee and Feng Yu

This unit introduces statistical computing. The basic concepts and typical workflows of statistical software development are exemplified using state-of-the art tools with R as the primary programming language. Students will be introduced to literate programming, package development, version control and testing. Basic scientific programming concepts will be covered, such as vectorization, functional and object-oriented programming. Some data science packages in the tidyverse will be introduced to enable data manipulation and visualization. The use of important statistical tools will be covered, such as sparse matrix algebra, numerical optimization and numerical integration.

Your learning on this unit

By the end of the unit students should be able to:

• Set up and work productively within an integrated development environment.
• Use collaborative tools for software development involving multiple people.
• Create and use R packages containing useful software for scientific research.
• Use some tidyverse functionality for data manipulation and visualization.
• Take advantage of matrix sparsity in scientific computations.
• Implement a solution to a data science problem using numerical optimization methods.
• Implement a solution to a data science problem using numerical integration methods.

How you will learn

The unit will be taught through a combination of

• synchronous online and, if subsequently possible, face-to-face lectures
• asynchronous online materials, including narrated presentations and worked examples
• guided asynchronous independent activities such as problem sheets and/or other exercises
• synchronous weekly group problem/example classes, workshops and/or tutorials
• synchronous weekly group tutorials
• synchronous weekly office hours

How you will be assessed

Formative: a homework each week

Summative:

• A personal portfolio of notes, code snippets, and vignettes, 30%.
• Assessed coursework, 2 at 20% each.
• A group project, 30%.

Resources

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. MATHM0039).

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 Faculty workload statement relating to this unit for more information.

Assessment
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. If you have self-certificated your absence from an assessment, you will normally be required to complete it the next time it runs (this is usually in the next assessment period).
The Board of Examiners will take into account any extenuating circumstances and operates within the Regulations and Code of Practice for Taught Programmes.