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Durham University

Postgraduate Module Handbook 2021/2022

Archive Module Description

This page is for the academic year 2021-22. The current handbook year is 2022-23

Department: Mathematical Sciences

MATH42515: Data Exploration, Visualization, and Unsupervised Learning

Type Tied Level 4 Credits 15 Availability Available in 2021/22
Tied to G5K823 Data Science
Tied to G5K923 Data Science (Digital Humanities)

Prerequisites

  • None

Corequisites

  • None

Excluded Combination of Modules

  • None

Aims

  • To introduce the concepts and methods of exploratory data analysis, data visualization, and unsupervised learning

Content

  • Advanced exploratory data analysis
  • Density estimation and data visualization
  • Unsupervised learning and clustering
  • Principal component analysis (PCA) and dimension reduction • Data visualization and statistical computing with R
  • Methods for non-numerical data: e.g. categorical, spatial and temporal, text, images, networks, graphs.
  • Further topics: e.g. anomaly detection, treatment of missing values, association rules.

Learning Outcomes

Subject-specific Knowledge:
  • By the end of the module students will have a knowledge and understanding of statistics concepts in the following areas:
  • Advanced exploratory data analysis
  • Density estimation and data visualization
  • Unsupervised learning and clustering
  • PCA and dimension reduction
  • Data visualization and statistical computing with R
Subject-specific Skills:
  • Students will have basic statistical skills in the following areas: data analysis, data visualisation, statistical computing.
Key Skills:
  • Students will have skills in the following areas: exploratory data analysis, critical statistical thinking, statistical computer skills

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • This module will be delivered by the Department of Mathematical Sciences.
  • Workshops describe theory and its application to concrete examples, concretize understanding via the application of calculational and computational methods to more complex problems, as well as providing feedback and encouraging active engagement via discussion and groupwork.
  • Online resources support learning and normally include: video content, directed reading, reflection through activities, opportunities for self-assessment, and peer-to-peer learning within a tutor-facilitated discussion board
  • Surgeries give students the chance to pose personalized questions on both theory and practice.
  • Summative assignments are designed to test the acquisition and articulation of knowledge and critical understanding, and skills of implementation and interpretation of calculational and computational methods as applied to both synthetic and real problems.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Workshops (a combination of live lectures, computer practicals, problem classes, and tutorials) 12 3 times per week (Term 2, weeks 16-19) 2 hours 24
Lectures 8 2 times per week (Term 2, weeks 16-19) 1 hour 8
Surgeries 12 3 times per week (Term 2, weeks 16-19) 1 hour 12
Preparation, exercises, and reading 106
Total 150

Summative Assessment

Component: Coursework Component Weighting: 25%
Element Length / duration Element Weighting Resit Opportunity
Quizzes (e-assessments) 100%
Component: Assignment 1 Component Weighting: 25%
Element Length / duration Element Weighting Resit Opportunity
Assignment 100%
Component: Assignment 2 Component Weighting: 50%
Element Length / duration Element Weighting Resit Opportunity
Assignment 100%

Formative Assessment:

None


Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University