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Module Overview

Data Science 1

This module introduces students to the basic principles of data science, importing data, data transformation, cleaning and imputation, and visualisation for exploratory analysis.

Module Code

PHYS 1016

ECTS Credits

5

*Curricular information is subject to change

  • Importing and preparing data:

    • Data science life-cycle and the CRISP-DM process;

    • Developing data understanding and analysis pipelines;

    • Data file types and importation strategies;

    • Variable types, formatting, labelling;

  • Data imputation

    • Imputation of a mean;

    • Random imputation;

    • Imputation by a model;

    • Effect on modelling;

  • Data transformation, wrangling and joins

    • Data filtering and selection;

    • Rotations;

    • Relational data and data joins;

  • Data visualisation techniques

    • Visualisation theory;

    • Exploratory vs Explanatory visualisations; infographics, art;

    • Techniques for trends, comparisons, relationships

    • Use of encodings, scaling, annotations, labelling, colour;

A mixture of lectures, practical computing laboratory classes and tutorials. Programming will be taught in the computer laboratory, and with supplemental lectures. The module will use the computer laboratory throughout the syllabus to achieve as much as possible subject matter interaction.

Module Content & Assessment
Assessment Breakdown %
Other Assessment(s)100