Post on 31-Mar-2020
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Diploma in Business Analytics
Module 01: Business Mathematics & Statistics
Module Overview
The module forms an introduction to the mathematical and statistical
skills needed for the Business 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 Business 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
1) Knowledge and Understanding:
Having successfully completed this module, you will be able to
demonstrate knowledge and understanding of:
• The basic mathematical techniques of algebra
• The calculus and an understanding of the methods of differentiation
and integration when applied to a range of functions
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2) Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
• To analyse a problem and to choose the most suitable method for its
solution
• To work well under examination conditions
3) Transferable and Generic Skills
Having successfully completed this module you will be able to:
• To absorb and retain concepts
• 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
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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
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Diploma in Business Analytics
Module 02: Business Accounting & Finance
Module Overview
The module conceptualises financial statements through the
introduction of double entry & accounting equation and trial
balance. It covers adjustments like accruals, prepayments & bad
debt. It explains the assets, inventory, depreciation and revaluation.
The module talks about the sources of finance & capital structure
and interpretation of accounts & the business model. In the second
part some essential topics in management accounting will be given
which include the main functions of management accounting
systems, the roles of management accountants in the context of for-
profit-organisations and the key traditional management accounting
techniques.
Module Aims
To give students a good understanding of the way that financial
accounts are prepared and to introduce management accounting
and the calculative techniques for analysing costs. Most importantly
it gives you the tools to understand how a business works.
Learning Outcomes
1) Knowledge and Understanding:
Having successfully completed this module, you will be able to
demonstrate knowledge and understanding of:
• The main users of financial statements and their needs;
• Methods of recording transactions, as a basis of financial statements, and
for control within the organisation;
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• Basic principles and accounting concepts underlying the preparation of
financial statements;
• The basic management functions of planning, decision making & control
and how these are related within a business activity;
• The role and limitations of management accounting practices in the context
of other information and control systems.
2) Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to;
• Prepare simple financial statements from structured and
unstructured information;
• Develop intellectual skills associated with analysing, recording,
communicating and evaluating financial information, using both
qualitative and quantitative techniques, for stewardship and
decision making;
• Apply the main schemes of cost classification, costing methods,
contribution analysis and simple capital investment appraisal.
• Evaluate the operation of a budgetary control process, and
perform basic calculative analyses;
• Analyse the problem-solving and short-term decision-making
aspects of management accounting using cost- volume-profit.
3) Transferable and Generic Skills
Having successfully completed this module you will be able to:
• Demonstrate learning, numeracy, problem solving and written
communication skills;
• Show developed self-management skills.
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Diploma in Business Analytics
Syllabus
The topics covered in this module will include:
• Objectives and users of financial statements
• Accounting for control, the double entry model and control of data
• The Balance Sheet equation, the Profit and Loss Account, Cash Flow
Statement, the Balance Sheet and underlying concepts
• Preparation of accounts from records of transactions for sole
traders, partnerships and companies
• Valuation and accounting for assets and liabilities including fixed
assets and depreciation, stocks, debtors, and accruals and
prepayments
• Sources of finance & regulation of financial reporting, strengths
and limitations of historical cost accounting and the interpretation of
accounts
• The nature and functions of management accounting, classification
of costs, accounting for materials, labour, and overhead
•Cost accumulation systems: Job costing , cost reporting under
absorption and marginal costing, standard costing and variance
analysis (Direct material, direct labour and fixed overheads),
contribution and short-term decisions: CVP analysis , investment
appraisal methods and budgeting, budgetary control & cash
budgets.
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Diploma in Business Analytics
Learning & Teaching Methods
Teaching methods include:
• Lectures
• Problem-solving activities
• Directed reading
• Case study/problem solving activities
• Private/guided study
Learning activities include:
• Introductory lectures
• Case study/problem solving activities
• Private study
• Use of video and online materials
Resources & Reading list:
Weetman, P (2010). Financial and Management Accounting: An
Introduction
Wood and Sangster: Business Accounting Vol 1 and Vol 2.
Seal, W., Garrison, R.H., Noreen, E.W. (2015): Management
Accounting
Weetman, P (2016): Financial and Management Accounting: an
introduction
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Diploma in Business Analytics
Module 03: Business Analytics
Module Overview
Business analytics is closely related to management science and
operational research. It refers to the use of statistical methods and
models as well as empirical data to support the process of making
business decisions. This module provides general knowledge about
business analytics, illustrated with case studies and examples from
various industries. In order to use the above mentioned methods and
models effectively, one needs to understand the underlying
probability theory and statistics. Thus, the module also provides a
basic knowledge of statistics and probability. It introduces such
concepts as random variables and probability distributions, and it
covers the basics of statistical analysis and inference.
Module Aims
To introduce students to business analytics and to provide them with
the tools to solve simple business analytics problems. These tools are
derived from probability theory and statistics, and include, among
other things, some statistical tests and linear regression.
Learning Outcomes
1) Knowledge and Understanding:
Having successfully completed this module, you will be able to
demonstrate knowledge and understanding of:
• The role of business analytics in generating value from data
• The scope and nature of different types of business analytics
techniques
• The role of probability theory in modelling uncertainty
• Basic concepts of statistical analysis and inference models
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Diploma in Business Analytics
2) Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
• Apply basic business analytics techniques to business problems
• Use probability distributions to model uncertainty in real life
problems
• Apply basic statistical analysis and inference models to business
problems
3) Transferable and Generic Skills
Having successfully completed this module you will be able to:
• Learn the basics of mathematical arguments
• Communicate mathematical ideas effectively both in oral and written
form
• Use a variety of visual models for representing the results of your
analysis
Syllabus
The topics covered in this module will include:
• The role of business analytics in generating value from data based
on case studies from industry;
• Various types of business analytics techniques, i.e. descriptive,
predictive, and prescriptive, along with relevant examples and case
studies;
• Introduction to the concept of modelling;
• Important concepts of probability theory, including random
variables, expectation, and probability distributions;
• Statistical inference and relevant models;
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• An introduction to clustering;
• Applications of selected modelling approaches.
Learning & Teaching Methods
Teaching methods include:
• Lectures
• Interactive case studies
• Problem-solving activities
• Directed reading
• Private/guided study
Learning activities include:
• Introductory lectures
• Case study/problem solving activities
• In class debate and discussion
• Private study
• Use of video and online materials
Resources & Reading list:
• Moore, D.S., McCabe, G.P. and Craig, B. (2014). Introduction to
the Practice of Statistics.
• Evans, J.R (2013). Business Analytics: Methods, Models and
Decisions.
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Diploma in Business Analytics
Module 04: Business Forecasting
Module Overview
Forecasting is the process of making statements about events whose
actual outcomes (typically) have not yet been observed. A
commonplace example might be estimation of some variable of
interest at some specified future date. This module gives you a
thorough understanding of various statistical methods for
forecasting, in particular time-series methods that have wide
applications in business.
Risk and uncertainty are central to forecasting and prediction; it is
generally considered good practice to indicate the degree of
uncertainty attaching to forecasts, and sometimes it is necessary to
provide distributional rather than point forecasts. As such, an
introduction to methods for distributional forecasting will also be
provided.
As forecasting often requires huge amount of data, both for training
and testing the models, and the required formulae and equations are
often complicated, it is essential to implement forecasting methods
using a proper statistical package. As such training will be provided
on using SAS package for implementing forecasting methods.
Module Aims
To introduce the student to time series models and associated
forecasting methods, and show them how such models and methods
can be implemented in SAS package.
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Diploma in Business Analytics
Learning Outcomes
1) Knowledge and Understanding:
Having successfully completed this module, you will be able to
demonstrate knowledge and understanding of:
• Different fields of application of time series analysis and forecasting;
• The capabilities as well as limitations of quantitative-based
forecasting methods;
• The importance of incorporating uncertainty in forecasting.
2) Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
• Formulate time series models including exponential smoothing
methods, ARIMA methods, and innovations state space models;
• Use SAS to fit and analyse such models to data;
• Choose the most appropriate forecasting method using various types
of information criterion.
3) Transferable and Generic Skills
Having successfully completed this module you will be able to:
• Self-manage the development of learning and study skills;
• Plan and control effectively for successful completion of a personal
workload;
• Communicate effectively, in both oral and written form, using and
justifying argument within reports and presentations.
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Diploma in Business Analytics
Syllabus
The topics covered in this module will include:
• Introduction to Forecasting: quantitative and qualitative methods;
• Time series models: decomposition, analysis and removal of trend,
seasonality, and cycle;
• Exponential Smoothing Methods: Single Exponential, Holt and
Holt-Winters Methods;
• Box-Jenkins Methods for ARIMA models;
• Simple and Multiple Regression Techniques;
• Introduction to Innovations State Space models.
Learning & Teaching Methods
Teaching methods include:
• Lectures
• Interactive case studies
• Problem-solving activities
• Computer Labs
• Directed reading
• Private/guided study
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Diploma in Business Analytics
Learning activities include:
• Introductory lectures
• An assignment (individual written coursework)
• Case study/problem solving activities
• In class debate and discussion
• Private study
• Use of video and online materials
Resources & Reading list:
• SAS Base Software. This module will require the weekly use of a
computer lab equipped with the latest version of SAS Base
Software.
• Hyndman R.J., Koehler, A.B., Keith Ord, J. and Snyder, R.
D (2008). Forecasting with Exponential Smoothing: The State
Space Approach.
• Hyndman, R.J. and Athanasopoulos, G (2013). Forecasting:
Principles and Practice.
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Diploma in Business Analytics
Module 05: Business Simulation
Module Overview
An experimental technique, simulation is one the most widely used
modelling techniques. This is because, unlike optimising techniques
such as queuing theory, it requires few assumptions. As a result,
analysts use it to solve a wide variety of complex real-life problems. It
is very effective. For example, a quick look at the clients of the Simul8
corporation (http://www.simul8.com/) reveals a long and impressive
list of organisations who apply simulation. Students who successfully
complete this module acquire the practical skills needed to conduct
a successful simulation project from scratch, and have a theoretical
understanding that is essential for the effective use of this powerful
decision-aiding tool. Specifically, students will acquire theoretical
understanding of and develop practical modelling skills in using
three types of simulation:
(i) Monte Carlo simulation using the @risk program in MS
Excel spreadsheets to model complex but static problems
for which changes over time are not important such as
inventory control, forecasting and decision analysis;
(ii) Discrete Event Simulation using the Simul8 application to
model the operational behaviour of systems with complex
queues such as hospitals, airports and supermarkets; and
(iii) (iii) System dynamics using the Stella application to model
long-term, strategic problems such as the long term
effects of government policy decisions on the health care
system.
Module Aims
Simulation is arguably the most widely used Management Science
technique and has a vast range of applications. This module provides
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Diploma in Business Analytics
you with a basic understanding of what is meant by simulation and
of three key approaches:
• Monte Carlo simulation using spreadsheets;
• Discrete event simulation;
• System dynamics.
Learning Outcomes
1) Knowledge and Understanding:
Having successfully completed this module, you will be able to
demonstrate knowledge and understanding of:
• the reasons for using the different types of simulation and have
insight into the domains in which it can usefully be applied;
• how different simulation approaches relate to each other and to the
broader concept of modelling and problem solving in Management
Science.
2) Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
• solve Monte Carlo and discrete event simulation problems using
@Risk and Simul8, respectively;
• formulate system dynamics problems to solve qualitatively or
quantitatively to understand how they are used and how they
behave;
• experiment using the three different simulation approaches.
3) Transferable and Generic Skills
Having successfully completed this module you will be able to:
• use your analytic skills in problem solving;
• communicate technical ideas to non-specialist managers.
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Diploma in Business Analytics
4) Subject Specific Practical Skills
Having successfully completed this module you will be able to:
• use three different types of commercial simulation software: @Risk,
Simul8 and Stella.
Syllabus
The topics covered in this module will include:
• Monte Carlo Simulation.
• Why simulation is so widely used. Dealing with risk, variability and
uncertainty. Random numbers and sampling. Interpreting the
results.
• Discrete Event Simulation (DES).
• Introduction to DES. Approaches to modelling. Developing
simulation models using commercial software. Visual interactive
modelling.
• System Dynamics.
• Deterministic simulation approach used for modelling systems
with feedback. Examples include the flow of information in an
organisation.
*The computer packages @risk, simul8 and Stella are used in the
course of the module.
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Diploma in Business Analytics
Learning & Teaching Methods
Teaching methods include:
• Lectures
• Interactive case studies
• Problem-solving activities
• Computer Labs
• Directed reading
• Private/guided study
Learning activities include:
• Introductory lectures
• An assignment (individual written coursework)
• Case study/problem solving activities
• In class debate and discussion
• Private study
• Use of video and online materials
Resources & Reading list:
• Oakshott, L. (1997). Business Modelling and Simulation.
• Stella. Software
• Pidd, M. (2004). Computer Simulation in Management
Science.
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Diploma in Business Analytics
• Robinson, S. (2003). Simulation: The Practice of Model
Development and Use.
• @risk. Software
• Simul8. Software
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Diploma in Business Analytics
Module 06: Business Analytics Programming
Module Overview
Programming is a structured way of giving a computer unambiguous
instructions to perform specific tasks. Knowledge and experience of
programming not only improves your employability but it also
teaches you analytical skills such as breaking down a problem into
smaller parts and recognising and reusing previously solved
problems.
The purpose of this module is to equip you with the knowledge and
skills for writing structured computer programs. Although these
fundamentals can be achieved using any high level programming
language, e.g. Java and Python, the module introduces Visual Basic
for Application (VBA) as the introductory language.
VBA is a very versatile, event-driven programming language.
Programmers predominantly use VBA algorithms to build
customized applications and solutions for Microsoft office
applications such as MS-Excel, MS-Word and MS-Access in order to
enhance the capabilities of those applications. For example, you can
build a VBA algorithm to automate the repetitive task of forecasting
future demand for a product upon updating current sales data in
Excel.
Module Aims
To provide you with the fundamental knowledge and skills for
writing structured computer programs. Although these fundamentals
are applicable using any high level programming language, this
module will introduce the concepts using Visual Basic for
Applications (VBA), a versatile, event driven language.
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Learning Outcomes
1) Knowledge and Understanding:
Having successfully completed this module, you will be able to
demonstrate knowledge and understanding of:
• The software development techniques that constitute good
programming practice
• Object-oriented programming
• The importance of correctness, usability and readability in
programming
2) Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
• Design and implement an algorithm to conduct technical
calculations, manipulate data and create graphical user interfaces
• Be able to handle files using a programming language and
integration with other packages such as Excel
• Use techniques for debugging an algorithm
3) Transferable and Generic Skills
Having successfully completed this module you will be able to:
• Self-manage the development of learning and study skills
• Plan and control effectively for successful completion of a personal
workload
• Use your analytic skills in problem solving
• Communicate effectively, in both oral and written form, using and
justifying argument within reports and presentations
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Diploma in Business Analytics
Syllabus
The topics covered in this module will include:
1. Overview of Computer Programming: Purpose and nature;
the VBA Environment
2. Key Components of Programs:
• 1Variables, Constants and Data Types;
• Formatting and Identifiers;
• Commenting;
• Arrays
• Conditional Structures and Loops
• Routines, Procedures and Functions
3. Debugging
4. Manipulating Excel using VBA
5. Object-Oriented Programming
Learning & Teaching Methods
Teaching methods include:
• Lectures
• Interactive case studies
• Computer Labs
• Directed reading
• Private/guided study
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Learning activities include:
• Introductory lectures
• An assignment (individual written coursework)
• In class debate and discussion
• Private study
• Use of video and online materials
Resources & Reading list:
• Albright, S. C. (2013). VBA for Modelers: Developing Decision
Support Systems.
• Knuth, D. E. (1998). The art of computer programming: sorting
and searching, Pearson Education.