Data Analysis Skills (Level M) STATS5085
- Academic Session: 2021-22
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 2
- Available to Visiting Students: No
- Available to Erasmus Students: No
This course gives students the experience of analysing data in a wide variety of contexts, using the R computer package, develops written and verbal communication skills, and provides an opportunity for students to carry out a short data-sourcing and analysis project. The practical, lab-based course delivers experience in key skills needed by the professional statistician.
10 2-hour labs
14 additional hours of lectures and workshops
STATS4048 Professional Skills
STATS4052 Data Analysis
STATS3011 Statistics 3A: Data Analysis
Practical skills assessment (100%) of independent and group work on statistical analysis tasks, typically including quizzes (25%), in-class tests (50%), and a group project and presentation (25%).
This course aims to prepare students for their possible future role as practising statisticians, by
■ learning to work independently in statistical planning, implementation, and data analysis;
■ critically integrating the knowledge acquired in the other courses taught in this programme;
■ developing written and verbal skills of presentation and communication, through case studies, teamwork exercises and associated written reports and presentations;
■ introducing students to the social, ethical, legal, and professional issues arising in Statistical research.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ work independently, as well as in a team, on practical data analysis tasks;
■ perform the steps in completing a formal statistical analysis, including visualization, data wrangling, identifying relevant statistical methodology, its implementation and validation;
■ develop an analysis plan and implement an appropriate modelling strategy to answer questions of interest about a given data set;
■ implement the statistical techniques covered in other postgraduate courses in R;
■ use features of scientific word-processing and presentation software, including the creation of reproducible documents in R;
■ critically collate the results from statistical procedures, interpret them, draw appropriate conclusions and write up the results clearly as a report;
■ communicate conclusions from data analyses effectively in a presentation;
■ develop, present and critically reflect upon arguments on social, ethical, legal and professional issues in Statistics.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.