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CSC-10058 - Module Specification
School of Computer Science and Mathematics
Faculty of Natural Sciences

For academic year: 2024/25 Last Updated: 24 April 2024

CSC-10058 - Introduction to Data Science I
Coordinator: Peter Wootton Tel: +44 1782 7 33767
Lecture Time: See Timetable...
Level: Level 4
Credits: 15
Study Hours: 150
School Office: 01782 733075

Programme/Approved Electives for 2024/25

None

Available as a Free Standing Elective

No

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2024/25


Aims
This module introduces data science, its relationship with business analytics, statistics, machine learning and artificial intelligence. It outlines the key terms and skills required by a data scientist and provides a foundation in the tools that are required to apply such skills.

Intended Learning Outcomes

Collect, clean and organise sets of data from the real world: 1,2
Identify methodologies and tools for extracting information from data: 1,2
Demonstrate different visualisation techniques in order to present information from data: 1,2
Form hypotheses and inferences that relate to real-world problems: 2

Study hours

Lectures: 22h
Practicals: 22h
Independent study: 106h

School Rules

None

Description of Module Assessment

1: Exercise weighted 50%
Programming exercises
A set of 3 data science programming exercises spread across the semester. All exercises are provided in a notebook template with answers to be accompanied by appropriate commentary. Students complete the exercises outside of class in open-book conditions and collaboration is forbidden. In total students can expect to spend 4 hours completing the exercises.

2: Project weighted 50%
Data analysis project
2000-word report on a data science task where students will be given a set of raw data to operate on. Students will be expected to extract the right data from the given set (thus demonstrating what they would choose to collect in the first place), use appropriate methods to clean and organise the data and then apply basic data mining methodologies in order to extract information that would aid the solution of the given problem. Finally the students will choose appropriate visualisation techniques to present the information extracted. . Formatting guidelines will be provided.

Please note that for the 2022/23 Academic Year programmes may be adapted to operate in accordance with a flexible, digital approach. This means that there may be some changes made to how modules are delivered and assessed. The latest information will be provided to you by your School in module handbooks. We endeavour to give you as much notice as possible of any changes and details about the circumstance in which changes are made to programmes can be found in the Student Agreement, available at: https://www.keele.ac.uk/student-terms-and-conditions/

Last Updated: 24 April 2024