CS3PP19-Programming in Python for Data Science

Module Provider: Computer Science
Number of credits: 10 [5 ECTS credits]
Level:6
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites: Undergraduates must have taken (CS1MA16 or MA1LA) and CS1PR16 and CS2JA16 and postgraduates must be studying for a postgraduate degree in the Department of Computer Science
Co-requisites:
Modules excluded:
Current from: 2021/2

Module Convenor: Mr Miguel Sanchez Razo
Email: m.sanchezrazo@reading.ac.uk

Type of module:

Summary module description:

The module introduces students to the Python programming language and the Python data science module ecosystem, including data processing and machine learning libraries. Data manipulation and statistical data science methods are covered.

Aims:

The aim of the module is to introduce students to the Python programming language and enable them to work with current tools used in data science.

This module also encourages students to develop a set of professional skills, such as problem solving; critical analysis of published literature; creativity; technical report writing for technical and non-technical audiences; self-reflection; effective use of commercial software; organisation and time management; numeracy; hypothesis generation and testing.

Assessable learning outcomes:

Students should be able to implement common computer science algorithms in the Python programming language, apply functional programming paradigms in Python, to read and manipulate data to extract specific features and to apply statistical methods appropriately to analyse data.

Additional outcomes:

Students will have an appreciation of the wider Python ecosystem and tools.

Outline content:

The course consists of an introduction to the Python programming language followed by the Python data science library ecosystem, and finally example applications. The Python language will be covered in depth, including:Ìý

• Basic flow control, dynamic typing.Ìý


• Functional programming.Ìý

Handling data with widely used open-source libraries in Python will be covered:Ìý

Working with matrices and arrays using Numpy.Ìý

• Using data frames to organise and manipulate data with Pandas.Ìý


• Analysing data using scikit-learn.

Example applications to data science:Ìý

• Network analysis.Ìý


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• Classification.

Brief description of teaching and learning methods:

The module consists of lectures and practical classes throughout the term and an assessed assignment. The assignment will put material learnt in the lectures into practice.