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ST344: Professional Practice of Data Analysis

Lecturer(s): Professor David Firth, Dr Elke Thönnes

This module runs in Term 1, with a final coursework deadline towards the end of Term 2.

Restrictions: Available only to third-year undergraduate students in Mathematics and Statistics, MORSE and Data Science. Participation is strictly limited to 50 students: Pre-registration is essential and will open at 12 noon on 15th May 2019 for the 2019/20 academic year.

Due to the groupwork component of the module, de-registrations are not permitted after Week 3 of Term 1.

Prerequisite(s):

ST115 Introduction to Probability, ST218 Mathematical Statistics A, ST219 Mathematical Statistics B.

In addition, although not a formal pre-requisite, ST221 Linear Statistical Modelling is recommended as background for this module. From 2020/21 onwards prioritisation will be given to students who have taken ST221.

Commitment:

15 CATS. This includes, in addition to time for independent study and writing:

  • 10 hours of lectures (weeks 1-5)
  • 6 hours of supervised computer practicals (weeks 2-7)
  • 2 hours of supervised small-group contact time (weeks 6-9)
  • 16 hours of online material and activities (weeks 1-7)
  • a presentation to be given in Week 10 of Term 1.

A substantial proportion of every student's work will be done in small, collaborative teams.

Aims:

The module will introduce students to statistical problem solving and the statistical investigative cycle from problem formulation to the communication of conclusions. Students will be trained in teamwork, leadership and communication/presentation skills.

Broadly speaking, the intention of this module is to complement the more specialized and/or technical modules that our students take, by emphasising the skills needed to translate technical knowhow into professional practice.

Outline syllabus:

  • The statistical investigative cycle
  • Data collection and quality
  • Exploratory analysis of data
  • Statistical modelling and visualisations using R
  • Oral presentations and academic writing skills
  • Writing for a non-specialist audience
  • Teamwork, leadership and (multinational) communication

Principal learning outcomes:

At the end of the module, students will

  • understand the elements of a statistical investigative cycle;
  • be able to perform a simple statistical investigation;
  • have gained experience in communicating the results of the investigation in reports and oral presentations;
  • have gained understanding of and experience in working collaboratively in a team.

In addition, since R will be the principal software used throughout the module, a successful student in this module will become skilled in the use of R for data analysis and reporting.

Assessment:

  • 50% group-based coursework (group project plan and report; group oral presentation)
  • 50% individual coursework (online portfolio, reflective writing and lab reports)

All assessment materials to be presented to a high professional standard (with reports typeset; computer code and outputs properly annotated; etc.)

Assessment deadlines:

Term 1:

  • Thursday of Week 2: Lab report 1 (2%)
  • Thursday of Week 3: Lab report 2 (2%)
  • Thursday of Week 4: Lab report 3 (2%)
  • Monday of Week 5: Group project plan (5%)
  • Thursday of Week 5: Lab report 4 (2%)
  • Thursday of Week 6: Lab report 5 (2%)
  • Thursday of Week 9: Group project report (35%)
  • Week 10, Wednesday 10 am - 12 noon and Thursday 2 pm - 4 pm: Group oral presentation (10%)

Term 2:

  • Thursday of Week 3: Reflective writing on teamwork experience (10%)
  • Thursday of Week 10; Online portfolio (30%)

Feedback to students will be within 20 working days after the submission deadline. For the first six assessment items listed above, feedback will normally be within 5 working days.

Illustrative bibliography: