CSMAD21-Applied Data Science with Python

Module Provider: Computer Science
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2021/2

Module Convenor: Dr Carmen Lam
Email: carmen.lam@reading.ac.uk

Type of module:

Summary module description:

The module introduces the Python programming language in detail, its syntax and programming paradigm. The module also covers specific Python tools and libraries for data science, including data processing and manipulation, data visualisation, statistical methods, and machine learning libraries. The module covers techniques for data integration, manipulation, and visualisation for the effective analysis of data. It also covers methods for statistical analysis, data mining and machine learning in Python.

Aims:

The module aims to bring students up to an advanced level as regards the use of Python as programming language and to work proficiently with Python tools for data science. It contains a number of topics and practical work from programming tasks to data science applications in Python on which students can gain a significant hands-on experience.

Assessable learning outcomes:

Students will be able to

• understand and use appropriate Python syntax and ecosystem;


• understand statisticalÌýand machine learning methods for data analytics and mining in Python;


• applyÌýappropriate statisticalÌýand machine learning techniques for data science tasks.

Additional outcomes:

• In general, students will develop and enhance their programming skills.


• In particular, students will develop specific programming skills and experience for data science tasks, including data integration, manipulation, mining and visualisation.

Outline content:

• Introduction to the Python language


• Basic flow control, dynamic typing


• Functional programming


• Handling and analysing data with Python libraries (Numpy, Pandas, scikit-learn)


• Data integration methods and technologies


• Analysis of multidimensional datasets


• Data visalisation methods, design techniques and effective presentation


• Data ScienceÌýreal-world applications

Brief description of teaching and learning methods:

The module comprisesÌýlectures introducing the topics with appropriate tutorial support for learning the material. Practical time is provided where students can practice and further develop their understanding of the material covered.Ìý

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

One piece of coursework consists of a set of problem-solving/programming exercise.

Formative assessment methods:

Using examples and hands-on practicals to aid learning.

Penalties for late submission:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy 􀀓Penalties for late submission for Postgraduate Flexible programmes􀀔, which can be found here: