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Department of Statistics

ST340 Programming for Data Science

Please note that all lectures for Statistics modules taught in the 2022-23 academic year will be delivered on campus, and that the information below relates only to the hybrid teaching methods utilised in 2021-22 as a response to Coronavirus. We will update the Additional Information (linked on the right side of this page) prior to the start of the 2022/23 academic year.

Throughout the 2021-22 academic year, we will be adapting the way we teach and assess your modules in line with government guidance on social distancing and other protective measures in response to Coronavirus. Teaching will vary between online and on-campus delivery through the year, and you should read the additional information linked on the right hand side of this page for details of how this will work for this module. The contact hours shown in the module information below are superseded by the additional information. You can find out more about the University’s overall response to Coronavirus at: https://warwick.ac.uk/coronavirus.

All dates for assessments for Statistics modules, including coursework and examinations, can be found in the Statistics Assessment Handbook at http://go.warwick.ac.uk/STassessmenthandbook

You must register for this module using the pre-registration form linked on the right hand side.

ST340-15 Programming for Data Science

Academic year
21/22
Department
Statistics
Level
Undergraduate Level 3
Module leader
Lyudmila Grigoryeva
Credit value
15
Module duration
10 weeks
Assessment
Multiple
Study location
University of Warwick main campus, Coventry

Download as PDF

Introductory description

This module runs in Term 2 and is available for students on a course where it is a listed option and as an Unusual Option to students who have completed the prerequisite module ST221 Linear Statistical Modelling.

There is a cap on student numbers for this module and pre-registration is essential. Information about prioritisation and the pre-registration form can be found at http://go.warwick.ac.uk/ST340

Module web page

Module aims

To introduce students to algorithms suitable to the analysis of large datasets. In the modern world it is very easy to generate very large amounts of data. Capturing and exploiting the important information contained within such datasets poses a number of statistical challenges. It may not even be clear how much useful information the data contains. The module will cover a variety of algorithms developed to tackle some of these challenges.

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

  1. Computational Complexity.
  2. Principal components analysis and singular value decomposition.
  3. Markov chains and PageRank.
  4. Clustering, EM algorithm.
  5. Bandit problems.
  6. Supervised learning, k-Nearest neighbours.
  7. Supervised and unsupervised learning. Penalised regression.
  8. Support vector machines.
  9. Artificial neural networks.
  10. Gaussian processes.
  11. Parallel and distributed algorithms.

Learning outcomes

By the end of the module, students should be able to:

  • Understand how to use a variety of practical algorithms when dealing with data analysis problems.
  • Use R to implement data analysis algorithms.
  • Interpret the output of various algorithms when applied to data sets.

Indicative reading list

View reading list on Talis Aspire

Subject specific skills

TBC

Transferable skills

TBC

Study time

Type Required Optional
Lectures 20 sessions of 1 hour (13%) 2 sessions of 1 hour
Practical classes 10 sessions of 1 hour (7%)
Private study 46 hours (31%)
Assessment 74 hours (49%)
Total 150 hours

Private study description

Weekly revision of lecture notes and materials, wider reading, practice exercises and preparing for examination.

Costs

No further costs have been identified for this module.

You do not need to pass all assessment components to pass the module.

Assessment group C2
Weighting Study time Eligible for self-certification
Assignment 3 17% 25 hours No

Due in Term 2 Week 10.
You will use R to implement and run algorithms on large datasets in response to a set of questions. You will present, discuss and evaluate the results. The number of words noted below refers to the amount of time in hours that a well-prepared student who has attended lectures and carried out an appropriate amount of independent study on the material could expect to spend on this assignment. 500 words is equivalent to one page of text, diagrams, formula or equations; your ST340 Assignment 3 should not exceed 25 pages in length.
Assignment 1 16% 24 hours No

Due in Term 2 Week 5.
You will analyse algorithms. You will use R to implement algorithms in response to a set of questions. You will present, discuss and evaluate the results. The number of words noted below refers to the amount of time in hours that a well-prepared student who has attended lectures and carried out an appropriate amount of independent study on the material could expect to spend on this assignment. 500 words is equivalent to one page of text, diagrams, formula or equations; your ST340 Assignment 1 should not exceed 24 pages in length.
Assignment 2 17% 25 hours No

Due in Term 2 Week 8.
You will use R to implement and run algorithms in response to a set of questions. You will present, discuss and evaluate the results. The number of words noted below refers to the amount of time in hours that a well-prepared student who has attended lectures and carried out an appropriate amount of independent study on the material could expect to spend on this assignment. 500 words is equivalent to one page of text, diagrams, formula or equations; your ST340 Assignment 2 should not exceed 25 pages in length.
On-campus Examination 50% No

The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade.

~Platforms - Moodle

  • Answerbook Pink (12 page)
Assessment group R1
Weighting Study time Eligible for self-certification
Assignment 50% No

You will be asked to complete this assignment if you failed the module and you failed the coursework component of the original assessment. The reassessment will be similar in nature to the original assignments. 500 words is equivalent to one page of text, diagrams, formula or equations; your Assignment should not exceed 25 pages in length.
In-person Examination - Resit 50% No

The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade.

~Platforms - Moodle

  • Answerbook Pink (12 page)
Feedback on assessment

Marked assignments will be available for viewing at the support office within 20 working days of the submission deadline. Cohort level feedback will be provided, and students will be given the opportunity to receive feedback via face-to-face meetings.

Cohort level feedback will be provided for the examination.

Past exam papers for ST340

Courses

This module is Optional for:

  • Year 3 of UCSA-G4G1 Undergraduate Discrete Mathematics
  • Year 3 of UCSA-G4G3 Undergraduate Discrete Mathematics
  • Year 4 of UCSA-G4G2 Undergraduate Discrete Mathematics with Intercalated Year
  • USTA-G300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics
    • Year 3 of G300 Mathematics, Operational Research, Statistics and Economics
    • Year 4 of G300 Mathematics, Operational Research, Statistics and Economics

This module is Option list A for:

  • Year 3 of USTA-G304 Undergraduate Data Science (MSci)
  • Year 4 of USTA-G303 Undergraduate Data Science (with Intercalated Year)
  • USTA-G1G3 Undergraduate Mathematics and Statistics (BSc MMathStat)
    • Year 3 of G1G3 Mathematics and Statistics (BSc MMathStat)
    • Year 4 of G1G3 Mathematics and Statistics (BSc MMathStat)
  • USTA-G1G4 Undergraduate Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)
    • Year 4 of G1G4 Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)
    • Year 5 of G1G4 Mathematics and Statistics (BSc MMathStat) (with Intercalated Year)
  • Year 3 of USTA-GG14 Undergraduate Mathematics and Statistics (BSc)
  • Year 4 of USTA-GG17 Undergraduate Mathematics and Statistics (with Intercalated Year)
  • Year 3 of USTA-Y602 Undergraduate Mathematics,Operational Research,Statistics and Economics
  • Year 4 of USTA-Y603 Undergraduate Mathematics,Operational Research,Statistics,Economics (with Intercalated Year)

This module is Option list B for:

  • Year 3 of USTA-G302 Undergraduate Data Science

This module is Option list D for:

  • Year 4 of USTA-G300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics
  • Year 5 of USTA-G301 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics (with Intercalated

This module is Option list E for:

  • Year 4 of USTA-G300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics
  • Year 5 of USTA-G301 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics (with Intercalated

This module is Option list F for:

  • Year 3 of USTA-G300 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics
  • USTA-G301 Undergraduate Master of Mathematics,Operational Research,Statistics and Economics (with Intercalated
    • Year 3 of G30H Master of Maths, Op.Res, Stats & Economics (Statistics with Mathematics Stream)
    • Year 4 of G30H Master of Maths, Op.Res, Stats & Economics (Statistics with Mathematics Stream)
Catalogue
Additional Information
Pre-registration
Resources
Feedback and Evaluation
Grade Distribution
Timetable