Data Analysis STATS4052

  • Academic Session: 2018-19
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

This course gives students further experience of analysing data in a wide variety of contexts, using the R computer package, and develops written communication skills.

Timetable

10, 2-hour practical sessions (weekly)

Requirements of Entry

The normal requirement is that students should have been admitted to an Honours- or Master's-level programme in Statistics.

Excluded Courses

Statistics 3A: Data Analysis

Data Analysis (Level M) [STATS5018]

Assessment

Laboratory Work (100%)

Are reassessment opportunities available for all summative assessments? Not applicable

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

Course Aims

This course aims:

■ to provide further experience of analysing data using the R package;

■ to develop general expertise in formulating and implementing statistical approaches to practical problems in a wide variety of contexts;

■ to develop written communication skills.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ implement the statistical techniques covered in other Honours courses in R.

■ interpret the results from statistical procedures and draw appropriate conclusions.

■ develop and implement an appropriate modeling strategy to answer questions of interest about a given data set.

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.