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COMM415DA  Fundamentals of Data Science (Professional) (2023)
MODULE TITLE  Fundamentals of Data Science (Professional)  CREDIT VALUE  15 

MODULE CODE  COMM415DA  MODULE CONVENER  Dr Alberto Moraglio (Coordinator) 
DURATION: TERM  1  2  3 

DURATION: WEEKS  0  0  11 
Number of Students Taking Module (anticipated)  90 

DESCRIPTION  summary of the module content
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: COMM414DA Introduction to Data Science. Corequisite modules: None
This module is a part of the dualqualification MSc Data Science (Professional) / Level 7 Research Scientist Apprenticeship programme. It cannot be taken as an elective by students on other programmes.
The apprenticeship standard and other documentation relating to the Level 7 Research Scientist Apprenticeship can be found here: https://www.instituteforapprenticeships.org/apprenticeshipstandards/researchscientistv10.
AIMS  intentions of the module
The aim of this module is to equip you with the core mathematical and computational skills essential for further study of data science. Topics will be tailored to the cohort, to address diverse backgrounds and previous experience. At the end of the module, you should possess a solid grounding in aspects of linear algebra and computational methods that are common to many areas of data science.
Most taught content will be delivered as lectures and practical work. Lectures will be accompanied by data analysis exercises and practical sessions. The module will be completed through individual study and coursework, supported by the module staff. Assessment may include assessed practical exercises and coursework.
In addition to its academic aims as part of the programme, this module has specific aims as part of the Level 7 Research Scientist Apprenticeship. The full list of Knowledge, Skills and Behaviours that must be demonstrated to complete the Apprenticeship can be found here: https://www.instituteforapprenticeships.org/apprenticeshipstandards/researchscientistv10.
This module will deliver content that may be used to evidence the Knowledge, Skills and Behaviours set out below. Primarily: K1 and K5. Secondarily: S1, S2 and S6.
Knowledge (K), Skill (S) or Behaviour (B)
K1: Subject specific knowledge: A deep and systemic understanding of a named / recognised scientific subject as found in an industrial setting, such as biology, chemistry or physics, found in the nuclear, food manufacture, pharmacology or energy production sectors, at a level that allows strategic and scientific decision making, while taking account of inter relationships with other relevant business areas / disciplines.
K5: Data analysis and evaluation: Statistical analysis techniques, numerical modelling techniques and how they are applied in context. How to interpret and categorise data to make informed and objective decisions against the goals and targets of the project. How to evaluate and interpret the data and associated analysis against company objectives.
S1: Scientific Knowledge: Apply a range of advanced, new and emerging practical and experimental skills appropriate to the role (e.g. chemical synthesis, bio analysis, computational modeling).
S2: Data Collection and Reporting: Capture and evaluate data critically drawing a logical conclusion, e.g. Case Report Forms, Data Management Plans, Data Review Plans, edit checks and User Acceptance Testing Plans.
S6: Critical Thinking: Conceptualise, evaluate and analyse information to solve problems.
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. Demonstrate competence in relevant aspects of linear algebra relevant to data science;
2. Utilise a variety of computational methods relevant to data science.
Discipline Specific Skills and Knowledge:
3. Use linear algebra as part of data analysis procedures;
4. Understand the underpinning mathematical principles commonly used in machine learning and statistical modelling;
5. Carry out linear algebra operations using Python.
Personal and Key Transferable / Employment Skills and Knowledge:
6. Explain the relationship between mathematical principles and core techniques in data science;
7. Understand mathematical notation and use mathematical notation effectively to communicate to a specialist.
SYLLABUS PLAN  summary of the structure and academic content of the module
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.
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities  40.00  Guided Independent Study  110.00  Placement / Study Abroad  0.00 

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category  Hours of study time  Description 
Scheduled Learning and Teaching  40  Lectures, practicals and exercises 
Guided Independent Study  110  Reading, coursework and associated preparation 
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 
Practical Exercises 
20 hours 
All 
Oral 
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 

Technical report  60  e.g. 1000 words  All  Written 
Technical report  40  e.g. 1000 words  All  Written 
DETAILS OF REASSESSMENT (where required by referral or deferral)
Original Form of Assessment 
Form of Reassessment 
ILOs Reassessed 
Time Scale for Reassessment 
Coursework exercises 
Coursework exercises 
All 
Within 8 weeks 

REASSESSMENT NOTES
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%.
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
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  COMM414DA 

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

ORIGIN DATE  Monday 05 August 2019  LAST REVISION DATE  Tuesday 24 January 2023 
KEY WORDS SEARCH  Machine Learning; Linear Algebra; Python. 
