Computer Science

ECMM456 - Fundamentals of Data Science (Professional) (2019)

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MODULE TITLEFundamentals of Data Science (Professional) CREDIT VALUE15
MODULE CODEECMM456 MODULE CONVENERDr Fabrizio Costa (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11 0 0
Number of Students Taking Module (anticipated) 30
DESCRIPTION - summary of the module content

*** This module is a “professional” version of the similar module ECMM444. It is intended to be taught in a short-fat format based around 3-day 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, and optimisation techniques for speeding up large computations. 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.



Pre-requisite modules: ECMM455 Introduction to Data Science.

Co-requisite modules: None

This module is a core module for MSc Data Science students

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, probability, 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 will include assessed practical exercises and coursework.
 

 

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. Demonstrate competence in aspects of probability;

3. Utilise a variety of computational methods relevant to data science.



Discipline Specific Skills and Knowledge

:

4. Use linear algebra and probability theory as part of data analysis procedures;



5. Understand the underpinning mathematical principles commonly used in machine learning and statistical modelling;

6. Carry out linear algebra and probability theory operations using Python.



Personal and Key Transferable / Employment Skills and Knowledge

:

7. Explain the relationship between mathematical principles and core techniques in data science;

8. Understand mathematical notation and use mathematical notation effectively to communicate to a specialised audience.

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, Systems of linear equations, Linear transformations, Eigenvalues and eigenvectors, Symmetry, Positive definite matrices, Singular value decompositions;

- Aspects of probability (e.g.): Basic probability, Marginal, conditional and joint probability, Bayes theorem, Probability distributions (the Normal, Gamma, Binomial and Bernoulli distributions), Central limit theorem, Moments, Multivariate distributions;

- 

Programming for data science in Python (e.g): Tools for handling data (Python: numpy, scipy, matplotlib, pandas), Linear algebra in code, Probability distributions and random numbers, Notebooks/markdown;

- Aspects of optimisation (e.g.): Linear least-squares, Gradient descent, Convexity (local vs global extrema), Linear programming;

- 

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 36.00 Guided Independent Study 114.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category

Hours of study time

Description

Scheduled Learning and Teaching

16

Lectures

Scheduled Learning and Teaching

20

Practicals and exercises

Guided Independent Study

50

Coursework and associated preparation

Guided Independent Study

64

Exercises 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

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

Coursework exercises

40

1000 words

All

Written

Coursework report

60

1000 words

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

Coursework exercises

Coursework exercises

All

Within 8 weeks

Coursework report

Coursework report

All

Within 8 weeks

 

RE-ASSESSMENT 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 re-take some or all parts of the assessment, as decided by the Module Convener. The final mark given for a module where re-assessment 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

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 978-1449319793 [Library]
Set Teetor, P. R Cookbook O'Reilly 2011 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES ECMM455
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 15 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Monday 05 August 2019 LAST REVISION DATE Thursday 10 October 2019
KEY WORDS SEARCH Statistics; Machine Learning; Linear Algebra; Probability; R; Python.