Further Mathematical Methods for Data Science
| Code | School | Level | Credits | Semesters |
| DATA1005 | Computer Science | 1 | N/A | Spring UK |
- Code
- DATA1005
- School
- Computer Science
- Level
- 1
- Credits
- N/A
- Semesters
- Spring UK
Summary
This teaching block consolidates core mathematical topics in calculus, linear algebra and discrete mathematical methods in the application of data science. An emphasis in this block is to develop general skills and confidence in applying the methods and developing techniques and ideas that are widely applicable and used in subsequent modules. Apprentices will use a computer package and basic programming to plot graphs and implement basic algorithms. Major topics include:
- Logic
- Differential calculus of several variables
- Matrix algebra
- Basic graph theory and optimisation.
Target Students
Only available to those studying towards the Data Scientist Degree apprenticeship programme.
Classes
49 hours of weekly distance learning mathematical exercises with supporting video material and drop-in remote support sessions. Two x 3-hour block release days.
Assessment
- 100% Assessment: Completion of the teaching block
Assessed by end of designated period
Educational Aims
To develop familiarity and advanced use of differential and integral calculus.To give a firm foundation in linear mathematics and associatedtechniques.To provide confidence in core mathematical techniques for use in subsequent modules.Learning Outcomes
- Understand and apply the concepts of propositional logic.
- Understand and apply the basic concepts of single and multivariate calculus.
- Apply manipulation of matrices to solve systems of linear equations and eigenvalue equations.
- Solve optimisation problems.
- Apply knowledge and techniques of graph theory to solve problems.