module 01: mathematics & statistics for data analyticsmodule 01: mathematics & statistics...

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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.



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



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



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


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.



Syllabus


Management Function

Basic Business Simulation

Excel & Spreadsheets

Decision Making

Resource Allocation

Probability Ideas

Market Research



• Lectures


• Interactive case studies








• Private study



▪ Lawrence, J.A. Jnr and Pasternak, B. A., Applied Management

Science, John WIley and Sons.



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


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.



Syllabus


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



• Lectures









• Private study



▪ 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



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


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.



Syllabus


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



• Lectures









• Private study



▪ 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



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


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.



Syllabus


. 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



• Lectures









• Private study



▪ WEISS, M.A. , Data Structures and Algorithm Analysis in Java

(Pearson, 2007 , 2 edition)



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


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.



Syllabus


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



• Lectures







• Private study





• 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

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