Data Analysis Skills (Level M) STATS5085
- Academic Session: 2019-20
- 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
Requirements of Entry
Some optional courses may be constrained by space and entry to these is not guaranteed unless you are in a programme for which this is a compulsory course.
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.