CS266-15 Data Analytics

Academic year
24/25
Department
Computer Science
Level
Undergraduate Level 2
Module leader
Weiren Yu
Credit value
15
Module duration
10 weeks
Assessment
Multiple
Study location
University of Warwick main campus, Coventry
Introductory description

This module provides a gateway for students to understand the fundamental principles, techniques, and tools that underpin the practice of data analytics, empowering them to make decisions based on analytical insights. It also serves as a valuable resource for students undertaking their 3rd-year projects, particularly those delving into the realms of machine learning and data mining.

Module aims

This module aims to provide students, regardless of their prior experience, with the necessary skills and knowledge to navigate the data analytics landscape. Students will learn how to gain insights from large and complicated data using statistical and machine learning methods, optimisation techniques, and predictive models. These acquired skills will be directly applicable to solving real-world data-driven problems, enhancing their comprehension of data analytics concepts, and preparing them effectively for their 3rd-year projects.

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.

Learning outcomes

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

Subject specific skills
  1. Employ statistical and machine learning methods to analyse data, interpret results, and draw meaningful conclusions.
  2. Build and evaluate predictive models using data-driven techniques to forecast outcomes based on historical data.
  3. Devise optimization methods to enhance decision-making processes and improve the efficiency of data-driven solutions.
  4. Apply theoretical knowledge to solve real-world data analytic problems through hands-on case studies and practical scenarios.
Transferable skills
  1. Critical Thinking: Developing the ability to analyse complex data-driven problems, evaluate data, and make informed decisions based on data-driven insights.
  2. Problem-Solving: Applying analytical techniques to address real-world challenges and optimize decision-making processes.
  3. Adaptability: Cultivating adaptability in dealing with diverse datasets, understanding how data analytics integrates into various domains, and contributing to interdisciplinary projects.

Study time

Type Required
Lectures 20 sessions of 1 hour (13%)
Practical classes 10 sessions of 1 hour (7%)
Private study 120 hours (79%)
Assessment 2 hours (1%)
Total 152 hours
Private study description

Private study, background reading and revision

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 D
Weighting Study time
Individual Coursework 20%

This assessment is eligible for self-certification (extension).

Final Exam 80% 2 hours
  • Answerbook Pink (12 page)
  • Students may use a calculator
Assessment group R
Weighting Study time
Resit Exam 100%
  • Answerbook Pink (12 page)
  • Students may use a calculator
Feedback on assessment

Individual written feedback on coursework

Past exam papers for CS266

Courses

This module is Optional for:

  • Year 2 of UCSA-I1N1 Undergraduate Computer Science with Business Studies

This module is Option list A for:

  • UCSA-G500 Undergraduate Computer Science
    • Year 2 of G500 Computer Science
    • Year 2 of G500 Computer Science
    • Year 2 of G500 Computer Science
  • UCSA-G503 Undergraduate Computer Science MEng
    • Year 2 of G500 Computer Science
    • Year 2 of G503 Computer Science MEng
    • Year 2 of G503 Computer Science MEng

This module is Option list B for:

  • UCSA-G4G1 Undergraduate Discrete Mathematics
    • Year 2 of G4G1 Discrete Mathematics
    • Year 2 of G4G1 Discrete Mathematics
  • Year 2 of UCSA-G4G3 Undergraduate Discrete Mathematics