CS2PP22NU-Programming in Python for Data Science

Module Provider: School of Mathematical, Physical and Computational Sciences
Number of credits: 10 [5 ECTS credits]
Level:5
Semesters in which taught: Semester 2 module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2023/4

Module Convenor: Dr Todd Jones
Email: t.r.jones@reading.ac.uk

NUIST Module Lead: Wenwen Liu
Email: w.liu@nuist.edu.cn

Type of module:

Summary module description:

The module introduces students to the Python programming language and the Python data science library ecosystem through application of programming fundamentals, data processing, and machine learning techniques.Ìý Data manipulation and statistical data science methods are also covered.

Aims:

The aim of the module is to introduce students to the Python programming language and enable them to master the basics of programming while working with current tools used in data science and general program design and development.

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

Assessable learning outcomes:

On completion of this module, students will be able to:Ìý

• Implement common computer science algorithms in the Python programming language;Ìý


• Demonstrate an understanding of the use of functionalÌýand object-orientedÌýprogramming paradigms in Python;Ìý


• Read and manipulate data in several formats to extract specific features;Ìý


• Assemble, implement, and select appropriate data science methodologies in Python;


• Employ third-party Python libraries appropriately toÌýdesign and create well-structured programsÌýfor practical applications.Ìý

Additional outcomes:

Students will gain generally improved programming skills and a deeper understanding of the wider Python ecosystem and tools.

Outline content:

The course begins with an introduction to the Python programming language and the Python library ecosystem.Ìý Students will perform a series of practical exercises designed to develop skill in Python scripting and wider program development. These will incorporate aspects of data analysis and professional and scientific research techniques.

The Python language will be covered in depth, including:

• Data types, operators, and flow control


• Functional and object-oriented programming


• Using DataFrames to organise and manipulate data with Pandas


• Working with matrices and arrays using NumPy


• Data visualisation with Matplotlib


• Analysing data using scikit-learn


• Handling data with widely used, open-source Python libraries

Example application to data science:

• Regression


• Clustering


• Classification


• Network (graph) analysis

Brief description of teaching and learning methods:

The module consists of weekly lectures and practical sessions, where students will be encouraged to collaborate with their peers to develop solutions to a series of problems.Ìý Skills gained in the lectures and practical sessions will be applied two pieces of assessment in the form of set programming exercises and related technical reporting of analysis results.