Study information

Introduction to Data Science - 2025 entry

MODULE TITLEIntroduction to Data Science CREDIT VALUE15
MODULE CODEECMM443 MODULE CONVENERDr Xiaoyang Wang (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated) 30
DESCRIPTION - summary of the module content

Data science is driving innovation across industries by enabling data-driven decision-making. This module provides the skills and foundations of data science, covering key steps in the data science workflow, including data wrangling and visualization. You will learn essential statistical techniques such as hypothesis testing and regression analysis, plus examples such as working with spatial, time-series, network, or text data. The module also introduces data storage and management. Ethical considerations and regulations, including GDPR compliance, will be explored to promote responsible data use and data protection. Practical exercises and individual study will consolidate your learning and provide the foundations for later study.

AIMS - intentions of the module

The module aims to develop a strong foundation in data science by equipping students with the knowledge and skills needed to prepare, analyse, and manage data effectively. It provides an understanding of statistical methods, database management, and various data types while emphasizing responsible data science. Through engaging with real-world datasets, students will learn to formulate data science problems and construct pipelines to begin to solve them technically.

Lectures will be accompanied by hands-on programming exercises, providing practical experience in data science tools and techniques.

 

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. Discuss the roles and impact of data science in industry and society.

  2. Demonstrate competence in handling, exploring and visualising complex datasets.

  3. Describe some of the main topics and techniques used in data science.

Discipline Specific Skills and Knowledge

  1. Identify some ethical issues associated with data science in society and business.

  2. Use Python to explore data.

  3. With some guidance employ basic data science techniques to explore data.

  4. With some guidance use basic techniques in sub-disciplines of data science, such as machine learning, statistics, network analysis, machine vision and high-performance computing.

Personal and Key Transferable / Employment Skills and Knowledge

  1. Communicate ideas and techniques fluently using written means in a manner appropriate to the intended audience.

  2. Communicate ideas effectively in oral presentations.

  3. Work effectively as part of a team.

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

Example topics (with associated exercises and discussions):

Introduction to data science and applications

Data wrangling

Data visualization

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 36 Guided Independent Study 114 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category

Hours of study time

Description

Scheduled Learning and Teaching

36

Lectures, Practicals, Seminars

Guided Independent Study

51

Coursework

Guided Independent Study

63

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
Feedback on practical work 18 hours All Oral
Feedback in seminar discussions 6 hours All Oral

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 0 Written Exams 100 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

40 hours

All

Written

Continuous Assessment 2

70

40 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 (40 hours, 30%)

All

Referral/deferral period

Continuous Assessment 2

Coursework 2 (40 hours, 70%)

All

Referral/deferral period

 

RE-ASSESSMENT NOTES

Reassessment will be by coursework and/or test in the failed or deferred element only. For referred candidates, the module mark will be capped at 50%. 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

ELE: http://ele.exeter.ac.uk

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Downey, A.B. Think Stats. O'Reilly Media. 2014
Set Mayer-Schonberger V. & Cukier K. Big data: a revolution that will transform how we live work and John Murray 2013
Set Marr, B. Big Data in Practice Wiley 2016
Set Downey, A.B. Think Python Green Tea Press/O'Reilly 2015
Set Schutt, R. and O’Neill, C. Doing Data Science: Straight Talk from the Frontline O'Reilly 2014
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Thursday 14th March 2024 LAST REVISION DATE Thursday 24th April 2025
KEY WORDS SEARCH data science, machine learning, statistics, data governance, data visualisation, data exploration, social networks, text analysis, time-series, database

Please note that all modules are subject to change, please get in touch if you have any questions about this module.