CSMAD21NU-Applied Data Science with Python

Module Provider: Computer Science
Number of credits: 20 [10 ECTS credits]
Level:7
Semesters in which taught: Semester 1 module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2023/4

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

NUIST Module Lead: Yunzhi Huang
Email: huang_yunzhi@nuist.edu.cn

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.

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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:

The course consists of a thorough introduction to the Python programming language with specific practical activities of increasing complexity. It then covers the important aspects of the Python support for data science application with practical projects.

• Introduction to Data Science and Python


• Variables and Data Types


• Flow Control


• Functional Programming


• Object-Oriented Programming


• Data Manipulation and Preprocessing


• Exploratory Data Analysis


• Time Series Analysis


• Data Visualization


• Data Science 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

Contact hours:

Ìý	Semester 1	Semester 2
Lectures	20
Practicals classes and workshops	10
Guided independent study:	Ìý	Ìý
Ìý Ìý Wider reading (independent)	20
Ìý Ìý Wider reading (directed)	20
Ìý Ìý Advance preparation for classes	30
Ìý Ìý Preparation for tutorials	30
Ìý Ìý Preparation of practical report	30
Ìý Ìý Essay preparation	30
Ìý Ìý Reflection	10
Ìý	Ìý	Ìý
Total hours by term	200	0
Ìý	Ìý	Ìý
Total hours for module	200

Summative Assessment Methods:

Method	Percentage
Set exercise	100

Summative assessment- Examinations:

None

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: /cqsd/-/media/project/functions/cqsd/documents/cqsd-old-site-documents/penaltiesforlatesubmissionpgflexible.pdf
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 50% overall.

Reassessment arrangements:

One 3-hour examination paper in August.

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

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Last updated: 17 April 2023