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: 2022/3

Module Convenor: Dr Lily Sun
Email: lily.sun@reading.ac.uk

NUIST Module Lead:
Email:

Type of module:

Summary module description:

The module introduces students to the Python programming language and the Python data science library ecosystem, including programming fundamentals, 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 master the basics of programming and 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 example applications.Ìý

The Python language will be covered in depth, including:

• Basic flow control, dynamic typing


• Functional programming


• Working with matrices and arrays using NumPy


• Using data frames to organise and manipulate data with PandasÌý


• Analysing data using scikit-learn


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

Example applications to data science:

• Network analysis


• Regression


• Classification

Brief description of teaching and learning methods:

The module consists of lectures, weekly practical classes, and one piece of assessment in the form of a set of programming exercise.

Contact hours:

Ìý	Semester 1	Semester 2
Lectures		20
Practicals classes and workshops		20
Guided independent study:	Ìý	Ìý
Ìý Ìý Wider reading (independent)		10
Ìý Ìý Wider reading (directed)		10
Ìý Ìý Peer assisted learning		10
Ìý Ìý Preparation of practical report		10
Ìý Ìý Completion of formative assessment tasks		15
Ìý Ìý Reflection		5
Ìý	Ìý	Ìý
Total hours by term	0	100
Ìý	Ìý	Ìý
Total hours for module	100

Summative Assessment Methods:

Method	Percentage
Set exercise	100

Summative assessment- Examinations:

N/A

Summative assessment- Coursework and in-class tests:

One piece of coursework consists of a set of programming exercise and one in-class test on key aspects of Python.

Formative assessment methods:

The weekly practical sessions are used for conducting the formative assessment where feedback is provided to help develop understanding and enhance programming skills throughout the term.

Penalties for late submission:

The Support Centres will apply the following penalties for work submitted late:

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Assessment requirements for a pass:

A mark of 40% overall.

Reassessment arrangements:

One 2-hour examination paper in August/September.

Additional Costs (specified where applicable):

1) Required text books:Ìý

2) Specialist equipment or materials:Ìý

3) Specialist clothing, footwear or headgear:Ìý

4) Printing and binding:Ìý

5) Computers and devices with a particular specification:Ìý

6) Travel, accommodation and subsistence:Ìý

Last updated: 16 January 2023