CSMADNU: Applied Data Science with Python

Module code: CSMADNU

Module provider: Computer Science; School of Mathematical, Physical and Computational Sciences

Credits: 20

Level: Postgraduate Masters

When you'll be taught: Semester 1

Module convenor: Dr Carmen Lam, email: carmen.lam@reading.ac.uk

NUIST module lead: Yunzhi Huang, email: huang_yunzhi@nuist.edu.cn

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: No

Last updated: 21 May 2024

Overview

Module aims and purpose

The module aims to enable students to work proficiently with Python tools for data science. It contains a number of topics and practical work, including simple programming tasks through to fully-formed data science applications in Python, with which students can gain a significant hands-on experience. This module also encourages students to develop a set of transferable and professional skills such as problem solving, critical thinking, technical report writing, self-reflection and effective use of data science technologies.Ìý

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Acquire and be able to apply statistical, programming, and machine learning techniques in Python for data science tasks;
2. Evaluate, select and use state-of-the-art Python tools and platforms for solving data science problems;
3. Design, implement, and execute solutions in Python for data science problems; and
4. Evaluate data science solutions in Python, including their outcomes, efficacy, constraints, and uncertainty.

Module content

This module will cover the following topics:

• Introduction to Data Science, Python
• Python Basics: Variables and Data Types
• Python Programming: Flow Control
• Python Functional Programming
• Data Science Concepts and Data Manipulation
• Exploratory Data Analysis and Data Pre-processing
• Data Visualisation
• Python Object-Oriented Programming and Exceptions
• Time Series Analysis
• Data Science Applications: Classification
• Data Science Applications: Clustering
• Data Science Applications: Regression
• Data Science Applications: Network Analysis

Structure

Teaching and learning methods

This module will take a problem-based learning approach. The lectures will introduce students the Python tools and machine learning methods specified in the indicative content. Students will be supervised in the practical sessions to apply the programming techniques to a given problem context and develop a technical solution.ÌýÌýÌý

The lectures and practical sessions will enable students to develop innovative solutions, and critically apply the Python tools and machine learning methods to real-world datasets.ÌýÌý

There will also be learning materials in digital forms when they are required to support learning.ÌýÌýÌý

Study hours

At least 48 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 50% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

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

Assessments with numerical marks

• 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 three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/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.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Weekly practical exercises (some may be in the form of groupwork) will be used as formative assessment. Feedback on weekly practical exercises will be given to students which will act as feedforward for the coursework assessments.Ìý

Reassessment

Additional costs