 Homepage
 Key Information
 Students
 Taught programmes (UG / PGT)
 Student Services and Procedures
 Student Support
 Events and Colloquia
 International Students
 Students as Change Agents (SACA)
 Student Staff Liaison Committees (SSLC)
 The Exeter Award
 Peer Support
 Skills Development
 Equality and Diversity
 Athena SWAN
 Outreach
 Living Systems Institute Webpage
 Alumni
 Info points and hubs
 Inbound Exchange Students
 Staff
 PGR
 Health and Safety
 Computer Support
 National Student Survey (NSS)
 Intranet Help
 College Website
ECMM456  Fundamentals of Data Science (Professional) (2023)
MODULE TITLE  Fundamentals of Data Science (Professional)  CREDIT VALUE  15 

MODULE CODE  ECMM456  MODULE CONVENER  Dr Alberto Moraglio (Coordinator) 
DURATION: TERM  1  2  3 

DURATION: WEEKS  11  0  0 
Number of Students Taking Module (anticipated) 

*** This module is a “professional” version of the similar module ECMM444. It is intended to be taught in a shortfat format based around 3day teaching blocks, as part of the MSc Data Science (Professional) programme. ***
Data science depends on a solid grounding in mathematics and programming. In this module, you will learn essential mathematical techniques and programming skills specific to data analysis, including how to apply the mathematical techniques you have learned as part of computational data analysis procedures. Other computational methods with direct relevance to data science and processing of large datasets will also be included, such as data analysis packages for Python. Overall this module will ensure you have the core skills and background knowledge that underpin many central topics in data science, including machine learning, statistical modelling, network analysis and computer vision.
Prerequisite modules: ECMM455 Introduction to Data Science.
Corequisite modules: None
This module is a core module for MSc Data Science students
Topics will be chosen depending on the background and experience of the student cohort, but are likely to include:
 Aspects of linear algebra (e.g.): Vectors, Matrices, Linear transformations, Eigenvalues and eigenvectors, Positive definite matrices, Singular value decompositions; Principal Component Analysis; Linear Discriminant Analysis
 Programming for data science in Python (e.g): Tools for handling data (Python: numpy, scipy, matplotlib, pandas), Linear algebra in code, Notebooks/markdown;
 Other topics may be included as appropriate to the skills and background of the student cohort.
Scheduled Learning & Teaching Activities  36.00  Guided Independent Study  40.00  Placement / Study Abroad  0.00 

Category 
Hours of study time 
Description 
Scheduled Learning and Teaching 
16 
Lectures 
Scheduled Learning and Teaching 
20 
Practicals and exercises 
Guided Independent Study 
40 
Coursework and associated preparation 

Form of Assessment 
Size of Assessment (e.g. duration/length) 
ILOs Assessed 
Feedback Method 
Practical Exercises 
20 hours 
All 
Oral 
Coursework  100  Written Exams  0  Practical Exams  0 

Form of Assessment 
% of Credit 
Size of Assessment (e.g. duration/length) 
ILOs Assessed 
Feedback Method 
Coursework exercises 
100 
1000 words 
All 
Written 

Original Form of Assessment 
Form of Reassessment 
ILOs Reassessed 
Time Scale for Reassessment 
Coursework exercises 
Coursework exercises 
All 
Within 8 weeks 

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to retake some or all parts of the assessment, as decided by the Module Convener. The final mark given for a module where reassessment was taken as a result of referral will be capped at 50%.
information that you are expected to consult. Further guidance will be provided by the Module Convener
Basic reading:
ELE: http://vle.exeter.ac.uk/
Web based and Electronic Resources:
Other Resources:
Reading list for this module:
Type  Author  Title  Edition  Publisher  Year  ISBN  Search 

Set  Strang, G.  Introduction to Linear Algebra  4th  Wellesley Cambridge  2005  [Library]  
Set  Grus, J.  Data Science From Scratch: First Principles With Python  O'Reilly  2015  [Library]  
Set  McKinney, W.  Python for Data Analysis: Data Wrangling with Pandas, Numpy and iPython  1st  O'Reilly Media  2012  9781449319793  [Library] 
Set  Teetor, P.  R Cookbook  O'Reilly  2011  [Library] 
CREDIT VALUE  15  ECTS VALUE  7.5 

PREREQUISITE MODULES  ECMM455 

COREQUISITE MODULES 
NQF LEVEL (FHEQ)  15  AVAILABLE AS DISTANCE LEARNING  No 

ORIGIN DATE  Monday 05 August 2019  LAST REVISION DATE  Wednesday 18 January 2023 
KEY WORDS SEARCH  Machine Learning; Linear Algebra; Python. 
