Data Visualisation Analytics module (CS51007)

Explore how to extract valuable information from large datasets.

Credits
20
Module code
CS51007
Level
5
Semester
Semester 1
School
School of Science and Engineering
Discipline
Computing

In today's data-driven world, the ability to effectively analyse and visualise data is becoming increasingly important across a wide range of industries, such as healthcare, finance, and digital marketing.

This module provides you with the skills and knowledge needed to make sense of complex datasets and communicate insights effectively.

By studying statistical methods, exploring data visualisation techniques, and learning how to mine data from vast datasets, you will be able to tackle real-world problems and make data-driven decisions. 
 

What you will learn

In this module, you will:

  • study basic frequentist statistics
  • explore how to visualise data and information effectively
  • learn about the methodologies of the Cross-Industry Standard Process for Data Mining (CRISP-DM)
  • explore various data mining algorithms and their use cases
  • work with industry standards and techniques for data visualisation and analytics

By the end of this module, you will be able to:

  • understand and explain the importance of statistics in today's world
  • select appropriate statistical techniques and data visualisations
  • discuss various data mining techniques
  • select appropriate data mining techniques for real-world problems

Assignments / assessment

  •  Data Visualization (30%)
  • Final Project (70%)

This module does not have a final exam.

Teaching methods / timetable

You will learn by taking a hands-on approach. Lectures will introduce the main concepts to you. You will explore these concepts further during practical lab sessions. Coursework assignments will be used to consolidate and demonstrate your learning.

Learning material is provided through lecture materials, lab-based exercises, and machine learning walkthroughs.

Courses

This module is available on the following courses: