COM2015 | 2025/6 | University of Exeter

MODULE TITLE	Introduction to Data Science	CREDIT VALUE	15
MODULE CODE	COM2015	MODULE CONVENER	Dr Xiaoyang Wang (Coordinator)

DURATION: TERM	1	2	3
DURATION: WEEKS	11

Number of Students Taking Module (anticipated)

DESCRIPTION - summary of the module content

In this module, you will learn about the broad and fast-moving field of data science. You will be introduced to the core competencies and application areas associated with data science, including data handling and visualisation, machine learning, statistical modelling, social network analysis, text mining. You will also explore the ways in which data science is transforming business and society, and learn about ethical and governance aspects of data science. Practical exercises, individual study and group work will consolidate your learning and provide the foundations for later study.

This module cannot be taken with BEM1025 Programming for Business Analytics.

AIMS - intentions of the module

This module will cover the breadth of data science to equip students with the context and vocabulary to support more detailed study in future modules. Topics will evolve to reflect current issues in data science, providing student with the tools to formulate data science problems and construct pipelines to begin to solve them technically.

Lectures will be accompanied by data analysis exercises and seminar discussions. A series of guided practical exercises will develop skills in programming (in Python), data handling and visualisation.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Design a data science pipeline for a given problem in a chosen domain.
2 Investigate a dataset using mathematical and visualisation tools.

Discipline Specific Skills and Knowledge

3. Apply principles of statistical pattern recognition to a given problem.
4. Articulate a decision problem to be solved with data science affecting business or society.

Personal and Key Transferable / Employment Skills and Knowledge

5. Critically read and report on research papers.
6. Present the results of a piece of data science work in the form of a report.

SYLLABUS PLAN - summary of the structure and academic content of the module

Example topics (with associated exercises and seminar discussions)

Introduction to data science and applications

Data wrangling

Data visualization

Data manipulation and analysis with pandas

Statistics and analysis tools for data science

Data storage and management

Ethics, regulation and data protection

Working with different types of data (e.g., spatial, time-series, network, text)

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	33	Guided Independent Study	117	Placement / Study Abroad	0

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category	Hours of study time	Description
Scheduled learning and teaching activities	33	Lectures, Practicals, Seminars
Guided independent study	50	Assessment
Guided independent study	67	Self-study and background reading

ASSESSMENT

FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

Form of Assessment	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Workshop exercises	2 hours per week	All	Model answers and verbal feedback

SUMMATIVE ASSESSMENT (% of credit)

Coursework	100	Written Exams	0	Practical Exams	0

DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment	% of Credit	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Continuous Assessment 1	30	15 hours	ALL	Written
Continuous Assessment 2	70	35 hours	ALL	Written

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)

Original Form of Assessment	Form of Re-assessment	ILOs Re-assessed	Time Scale for Re-assessment
Continuous Assessment 1	Coursework 1 (15 hours, 30%)	ALL	Completed over the summer with a deadline in August
Continuous Assessment 2	Coursework 1 (35 hours, 70%)	ALL	Completed over the summer with a deadline in August

RE-ASSESSMENT NOTES

Reassessment will be by coursework in the failed or deferred element only. For referred candidates, the module mark will be capped at 40%. For deferred candidates, the module mark will be uncapped.

RESOURCES

INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Downey, A.B., Think Stats, O'Reilly Media, 2014.
Mayer-Schonberger V. & Cukier K., Big data: a revolution that will transform how we live work and think, John Murray, 2013.
Marr, B., Big Data in Practice, Wiley, 2016.
Downey, A.B., Think Python, Green Tea Press/O’Reilly, 2015.
Schutt, R. and O’Neill, C., Doing Data Science: Straight Talk from the Frontline, O’Reilly, 2014.

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	None
CO-REQUISITE MODULES	None

NQF LEVEL (FHEQ)	5	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Wednesday 13th March 2024	LAST REVISION DATE	Wednesday 3rd September 2025

KEY WORDS SEARCH	Database; design; modelling

Introduction to Data Science - 2025 entry