�e Future is Here.
Diploma in Data Analytics
Module 01: Mathematics & Statistics for Data Analytics
Module Overview
The module forms an introduction to the mathematical and statistical
skills needed for the Data Science and Analytics. It starts with basic
topics in mathematics before proceeding on to cover calculus,
further algebra and series. In the second part some essential topics in
statistics will be given which include statistical parameters, graphs
including histogram and some topics in probability. You develop
your ability to absorb and retain concepts; analyse a problem and
choose the most suitable method for its solution and demonstrate
your application of theory to problem. This module cements
mathematical statistical skills needed for Data Science and Analytics.
Module Aims
To ensure that students from a wide range of educational
backgrounds have a broad understanding of basic mathematical &
statistical skills and to equip them with the mathematical techniques
needed to solve problems and to clearly structure their solutions and
conclusions.
Learning Outcomes
On successful completion of the module a student is expected to be
able to demonstrate:
1. Knowledge of the basic mathematical techniques of algebra.
2. Knowledge of calculus and an understanding of the methods of
differentiation and integration when applied to a range of functions.
3. An ability to analyse a problem and to choose the most suitable
method for its solution.
�e Future is Here.
Diploma in Data Analytics
4. An ability to work well under examination conditions.
5. An ability to absorb and retain concepts.
6. An ability to clearly communicate knowledge without immediate
recourse to source material.
Syllabus
The topics covered in this module will include:
Numberwork
Algebra
Coordinate Geometry
Further Algebra
Calculus
Differentiation
Calculus integration
Series
Set theory
Probability and statistics
Learning & Teaching Methods
Teaching methods include:
• Lectures
• Problem-solving activities
• Directed reading
• Private/guided study
�e Future is Here.
Diploma in Data Analytics
Learning activities include:
• Introductory lectures
• Case study/problem solving activities
• Private study
• Use of video and online materials
Resources & Reading list:
▪ Stroud, K.A. and Booth, Dexter J. (2009) Foundation Mathematics.
London: Palgrave Macmillian
�e Future is Here.
Diploma in Data Analytics
Module 02: Fundamentals of Quantitative Management
Module Overview
This application-driven module teaches the manager how to
formulate and solve real-world problems concerned with decision
making in modern management. Simulation is the main solution
tool. The course demonstrates how to build simulation models, how
to run simulations using simple Excel spreadsheets, and, to evaluate
and interpret output results.
Module Aims
To introduce (and revise) basic mathematical notions essential to
quantitative management;
To understand simulation modelling and how it can be used to effect
in key areas of management such as inventory, queues and
maintenance;
To understand how to submit a model, and run a simulation using
spreadsheets and how to evaluate results from that model.
Learning Outcomes
On successful completion of the module a student is expected to be
able to:
1. Formulate decision problems arising in management;
2. Build simulation models of such problems;
3. Solve practical instances of such problems using spreadsheets;
4. Analyse and evaluate simulation results prior to their use in
decision making.
�e Future is Here.
Diploma in Data Analytics
Syllabus
The topics covered in this module will include:
Management Function
Basic Business Simulation
Excel & Spreadsheets
Decision Making
Resource Allocation
Probability Ideas
Market Research
Learning & Teaching Methods
Teaching methods include:
• Lectures
• Problem-solving activities
• Interactive case studies
• Directed reading
• Private/guided study
�e Future is Here.
Diploma in Data Analytics
Learning activities include:
• Introductory lectures
• Case study/problem solving activities
• Private study
• Use of video and online materials
Resources & Reading list:
▪ Lawrence, J.A. Jnr and Pasternak, B. A., Applied Management
Science, John WIley and Sons.
�e Future is Here.
Diploma in Data Analytics
Module 03: Fundamentals of Database
Module Overview
This module introduces the underlying principles of databases,
database design and database systems. It covers the fundamental
concepts of databases, and prepares the student for their use in
commerce , science and engineering.
Module Aims
The aim of this module is to provide an introduction to data
modelling and the design and implementation of relational
databases.
Learning Outcomes
On successful completion of the module a student is expected to be
able to:
1. Prepare a relational schema from a conceptual model developed
using the entity-relationship model.
2. Employ the notions of relation, key and normal forms in a
relational database design.
3. Create a relational database schema in SQL that incorporates key,
entity integrity, and referential integrity constraints.
4. Demonstrate data definition in SQL and retrieve information from
a database using the SQL SELECT statement.
5. Use a relational database management system to build a
database.
�e Future is Here.
Diploma in Data Analytics
Syllabus
The topics covered in this module will include:
Underlying principles
. The relational model of databases
Fundamentals of Relational Database Systems
. entity-relationship modelling
. mapping conceptual schema to a relational model
. relational algebra: operations developed for relational databases
Relational Database Design
. functional dependency
. normal forms and normalisation
Structured Query Language
. data definition
. keys
. entity integrity constraints and referential integrity constraints
. query formulation
Learning & Teaching Methods
Teaching methods include:
• Lectures
• Problem-solving activities
• Directed reading
• Private/guided study
�e Future is Here.
Diploma in Data Analytics
Learning activities include:
• Introductory lectures
• Case study/problem solving activities
• Private study
• Use of video and online materials
Resources & Reading list:
▪ Database Principles: Fundamentals of Design, Implementations
and Management, 2nd Edition Peter Rob, Carlos Coronel,
Keeley Crockett, Stephen Morris
▪ Database Systems: A Practical Approach to Design,
Implementation, and Management, 6th Edition Thomas
Connolly, Carolyn Begg
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Diploma in Data Analytics
Module 04: Database & Information Retrieval
Module Overview
This module build on the foundations of data and information
systems, learn how to design and manage fully structured data
repositories and explore the rather different principles and
techniques involved in representing, organising and displaying
unstructured information.
Module Aims
The aims of this module are to extend the principles of SQL database
modelling laid down in the previous modules, to describe the field of
Information Retrieval, to introduce the concept of NoSQL databases
and hence to compare the strengths and weaknesses of all three
approaches to information access.
Learning Outcomes
On successful completion of the module a student is expected to be
able to:
1. Understand SQL database modelling and normalisation;
2. Appreciate the principles of Information Retrieval;
3. Apply and evaluate IR in a practical context;
4. Discuss differences between models such as SQL, IR and NoSQL.
�e Future is Here.
Diploma in Data Analytics
Syllabus
The topics covered in this module will include:
SQL Database Design Principles Entity Relationship Modelling Normalisation Modelling in a Realistic Scenario Principles of Information Retrieval Term Weighting Models Word Frequency, Stemming and Stoplists Inverted Indexing, TF*IDF and OKAPI Implementation of Phrase and Wildcard Searches Performance Evaluation in a Practical Task Introduction to NoSQL Databases Comparison of SQL, IR and NoSQL paradigms
Learning & Teaching Methods
Teaching methods include:
• Lectures
• Problem-solving activities
• Directed reading
• Private/guided study
�e Future is Here.
Diploma in Data Analytics
Learning activities include:
• Introductory lectures
• Case study/problem solving activities
• Private study
• Use of video and online materials
Resources & Reading list:
▪ Connolly, T., Begg, C. (2014). Database Systems: A Practical
Approach to Design, Implementation and Management, Addison-
Wesley.
▪ Hills, T. (2017). NoSQL and SQL Data Modeling: Bringing Together
Data, Semantics, and Software, Technics Publications.
▪ Manning, C. D., Raghavan, P. and Schütze, H. (2008). Introduction
to Information Retrieval, Cambridge University Press
�e Future is Here.
Diploma in Data Analytics
Module 05: Data Structures & Algorithms
Module Overview
Data structures and algorithms lie at the heart of Computer Science
as they are the basis for the efficient solution of programming tasks.
In this module, students will study core algorithms and data
structures, as well as being given an introduction to algorithm
analysis and basic computability.
Module Aims
The module will give students core algorithmic skills required.
Learning Outcomes
On successful completion of the module a student is expected to be
able to:
1. Demonstrate an understanding of core data types such as stacks,
queues, trees, and graphs.
2. Implement core data types in Java and write programs that make
efficient use of them.
3. Reason about the time and space complexity of programs.
4. Demonstrate knowledge of commonly used algorithms.
5. Explain the main concepts of computability and how some
problems have no algorithmic solution.
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Diploma in Data Analytics
Syllabus
The topics covered in this module will include:
. Data types
Abstract data types
Lists, stacks, queues, trees, sets, graphs
. Algorithms
Divide and conquer
Sorting and searching
Algorithms: binary search trees, minimum cost spanning trees,
shortest paths, parse trees
Algorithm analysis: time and space complexity
. Basic computability, incomputable functions and the halting
problem
Learning & Teaching Methods
Teaching methods include:
• Lectures
• Problem-solving activities
• Directed reading
• Private/guided study
�e Future is Here.
Diploma in Data Analytics
Learning activities include:
• Introductory lectures
• Case study/problem solving activities
• Private study
• Use of video and online materials
Resources & Reading list:
▪ WEISS, M.A. , Data Structures and Algorithm Analysis in Java
(Pearson, 2007 , 2 edition)
�e Future is Here.
Diploma in Data Analytics
Module 06: Data Modelling
Module Overview
This module is concerned with the application of linear models to the
analysis of data. The underlying assumptions are discussed and
general results are obtained using matrices. The standard approach
to the analysis of normally distributed data using ANOVA is
introduced. Methods for the design and analysis of efficient
experiments are introduced. The general methodology is extended
to logistic regression.
Module Aims
The module aims to apply linear models to analyse data; discuss
underlying assumptions and standard approaches; understand
methods to design and analyse experiments.
Learning Outcomes
On successful completion of the module a student is expected to be
able to:
1) Calculate confidence intervals for parameters and prediction
intervals for future observations;
2) Understand how to represent a linear model in matrix form;
3) Check model assumptions and identify influential observations;
4) Identify simple designed experiments;
5) Construct factorial experiments in blocks;
6) Adapt linear models to fit growth curves;
7) Work efficiently in small groups to analyse data;
8) Analyse linear models using R.
�e Future is Here.
Diploma in Data Analytics
Syllabus
The topics covered in this module will include:
Relational data modelling
Operational database
Relational model concepts
ERD
Normalisation
Multidimensional data modelling
Data warehouse
Dimensional model concepts
Dimensional modelling process
Dimension Normalization
Linear Modelling
Learning & Teaching Methods
Teaching methods include:
• Lectures
• Problem-solving activities
• Directed reading
• Private/guided study
Learning activities include:
• Introductory lectures
• Case study/problem solving activities
• Private study
• Use of video and online materials
�e Future is Here.
Diploma in Data Analytics
Resources & Reading list:
• Faraway, J. J. (2004), Linear Models with R, Chapman & Hall
(UK)
• Montgomery, D.C. (2005) Design and Analysis of Experiments
6th edition), Wiley
• Dobson, A. J., Barnett, A.G. (2008), An Introduction to
Generalized Linear Models (3rd edition), Chapman & Hall