Module 01: Business Mathematics & Statistics...Module 01: Business Mathematics & Statistics Module Overview The module forms an introduction to the mathematical and statistical skills

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Professional Diploma in Business Intelligence & 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



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


• 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



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: 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




• 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;



• 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.


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.



• Demonstrate learning, numeracy, problem solving and written

communication skills;

• Show developed self-management skills.



Syllabus


• 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.





• Lectures








• Private study



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



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




• 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





• 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



• 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 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;



• An introduction to clustering;

• Applications of selected modelling approaches.



• Lectures

• Interactive case studies







• In class debate and discussion

• Private study



• 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.



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.



Learning Outcomes




• 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.



• 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.



• 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.



Syllabus


• 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.



• Lectures



• Computer Labs







• An assignment (individual written coursework)



• Private study



• 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.



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

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




• 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.



• 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.





• use your analytic skills in problem solving;

• communicate technical ideas to non-specialist managers.

4) Subject Specific Practical Skills


• use three different types of commercial simulation software: @Risk,

Simul8 and Stella.

Syllabus


• 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.





• Lectures



• Computer Labs








• Private study



• Oakshott, L. (1997). Business Modelling and Simulation.

• Stella. Software

• Pidd, M. (2004). Computer Simulation in Management

Science.



• Robinson, S. (2003). Simulation: The Practice of Model

Development and Use.

• @risk. Software

• Simul8. Software



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.



Learning Outcomes




• The software development techniques that constitute good

programming practice

• Object-oriented programming

• The importance of correctness, usability and readability in

programming



• 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



• Self-manage the development of learning and study skills


workload

• Use your analytic skills in problem solving


justifying argument within reports and presentations



Syllabus


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



• Lectures


• Computer Labs









• Private study



• 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.



Module 07: Data Management & Visualization

Module Overview

This module introduces the fundamental concepts and technologies

that are used by modern international businesses to store, fuse,

manipulate and visualise mass datasets.

Module Aims

To develop the skills required to manage, manipulate and visualize

(noisy) data at scale, as collected in business environments. The

module will transfer practical knowledge about key issues and

technologies, taking participants on a journey through cloud

technologies, key database languages (e.g. SQL) through to

visualization techniques (e.g. Tableau, CartoDB) . In addition key data

concepts in modern business analytics will also be covered such as

Geospatial Data, Open Data and Web visualization (e.g D3).

Learning Outcomes




• Concepts and technologies that are used by modern international

businesses for data management

• Visualization of mass data sets

• The technologies to store, fuse, manipulate mass data



• Manage, manipulate and visualize mass data



• Be able to handle the key data base languages and visualization

techniques

• Use key data concepts in modern business analytics



• Self-manage the development of practical knowledge

• Use your analytical and technical skills

Syllabus


1. Core database and Cloud technologies;

2. Data acquisition and cleansing;

3. How to manipulate mass datasets (focussing on SQL, Hadoop);

4. Effective solutions to common data challenges (e.g. missing data);

5. Handling geospatial and Open data;

6. Visualisation technologies (Tableau, PowerMap, QGIS, CartoDB);

7. Web Visualization (HTML5)





• Lectures


• Computer Labs







• Private study



• Data Management: Foundations of Data Analytics by Richard

Watson.

• Modern Data Base Management; 12th Edition by Jeffrey A.

Hoffer.

https://www.amazon.com/s/ref=dp_byline_sr_ebooks_1?ie=UTF8&text=Richard+Watson&search-alias=digital-text&field-author=Richard+Watson&sort=relevancerank

https://www.amazon.com/s/ref=dp_byline_sr_ebooks_1?ie=UTF8&text=Richard+Watson&search-alias=digital-text&field-author=Richard+Watson&sort=relevancerank



Module 08: Machine Learning & Predictive Analytics

Module Overview

This module covers core predictive models and their motivation

within business use cases. Introduces the key techniques that

underpin this increasingly relevant field along with their evaluation

(e.g. measures of success, cross validation, A-B testing). Students will

establish knowledge of state-of-the-art prediction techniques.

Practical exercises will be set against a range of real world datasets

and time series data. This module is jointly taught using SPSS

modeller and Python, giving students practical experience in

industrial workflow data modelling environments where applicable.

Module Aims

To develop an understanding of the use cases and utility of

predictive analytics and the key role of machine learning in modern

business analysis. To develop a practical ability to apply such

algorithms in a business environment.

Learning Outcomes




• The use cases and utility of predictive analytics

• The role of machine learning



• Apply the core predictive models



• Apply prediction techniques and evaluation



• Work efficiently in small groups to analyse data


workload;

Syllabus


1. Classification models (e.g. decision trees/forests, SVMs)

2. Nearest Neighbour models

3. Bayesian methods

4. Ensembles

5. Deep Learning



• Lectures


• Computer Labs









• Private study



• Fundamentals of Machine Learning for Predictive Data

Analytics: Algorithms, Worked Examples, and Case Studies

(MIT Press) 1st Edition by John D. Kelleher.



Module 09: Analytics Specializations & Applications

Module Overview

This module gives an in-depth look at specialised analytical

techniques which present significant opportunities within business

environments to extract actionable insights.

Module Aims

To develop an understanding of the applicability and practicalities in

business environments of the use of a range specialized analytical

techniques. The aim is to teach participants how these advanced

skills can bring competitive advantage in real-world business

applications.

Learning Outcomes




• The applicability and practicalities of analytical techniques

• Advanced skills for business applications



• Specialize analytical techniques for business

• Use different Analytical Applications





• Self-manage the development of learning and study skills

• Use your analytic skills in problem solving

Syllabus


• Recommender Systems (e.g. collaborative filtering in business)

• Text Analytics (linguistic processing, social media analysis)

• Spatial/Temporal analytics (e.g. financial time series)

• Network analytics (e.g. social graph analysis)

• High dimensional analytics.



• Lectures


• Computer Labs







• Private study





• Bayesian Variable Selection for High Dimensional Data

Analysis by Yang Arjun.

• Business Analytics: An Application Focus by Halady P.

https://www.amazon.in/s/ref=dp_byline_sr_book_1?ie=UTF8&field-author=Halady+P&search-alias=stripbooks



Module 10: Big Data Business Projects

Module Overview

This module explicitly focuses on technologies, planning and

managerial issues associated with big data projects in business.

Module Aims

An understanding of how to avoid the pitfalls of big data projects;

Using common technologies such as Hadoop; Knowledge of how to

lead a team of business analysts; How to handle complexity; How to

present results.

Learning Outcomes




• The technologies for big data projects in business

• How to avoid pitfalls of big data projects



• Use technologies to lead a team of business analysts

• To plan and manage issues associated with big data projects



• To handle complexity and to present results



• Work efficiently in groups for projects

Syllabus


• Understanding requirements; coordination of a business

analytics team

• The data lifecycle; using noSQL solutions (e.g. Hadoop, Pig,

Hive, MongoDB)

• Linked data; taking solutions from evaluation to

implementation (e.g. A/B Testing);

• Presenting persuasive results (e.g. d3, advanced python and

HTML5)

• Extending to real-time/stream business analytics



• Lectures


• Computer Labs









• Private study



• Complete Analytical Project before Data Science

interview by TheDataMonk .

• Big Data for Executives: A Project Management Approach to

Big Data by David Macfie .

https://www.amazon.in/s/ref=dp_byline_sr_ebooks_1?ie=UTF8&field-author=TheDataMonk&search-alias=digital-text&text=TheDataMonk&sort=relevancerank

https://www.amazon.in/David-Macfie/e/B01B7YCWG2/ref=dp_byline_cont_ebooks_1



Module 11: Advanced Operations Analysis

Module Overview

This module builds further on the fundamental planning and control

concepts, tools and techniques, introduced in Supply Chain Planning

and Management. The module is more quantitative in nature.

Module Aims

To provide a thorough understanding of the tools and techniques

that can be used to analyse, plan and control operations in

contemporary businesses.

Learning Outcomes




• The tools and techniques for supply chain planning & management

• The planning and control concepts in business



• Use the forecasting techniques

• Analyze the operations and logistics systems



• Use your analytic competency in problem solving




justifying argument within reports and presentations

Syllabus


• Advanced forecasting techniques (including more advanced

time series and causal models);

• Inventory modelling (quantity discount models; joint

replenishment; reorder point –lot size systems; periodic review

models; news vendor model; (S-1, S) model; multi-warehouse

situations);

• Shop floor control; Little's Law; buffer management in serial

production lines;

• Operations scheduling and sequencing;

• Simulation as a tool to analyse operations & logistics systems



• Lectures


• Computer Labs









• Private study



• Applying Analytics: A Practical Introduction by E. S. Levine.

• Advanced Business Analytics by Fausto Pedro García Márquez.

https://www.amazon.in/s/ref=dp_byline_sr_book_1?ie=UTF8&field-author=E.+S.+Levine&search-alias=stripbooks

https://www.amazon.in/s/ref=dp_byline_sr_book_1?ie=UTF8&field-author=Fausto+Pedro+Garc%C3%ADa+M%C3%A1rquez&search-alias=stripbooks



Module 12: Advanced Business Analytics Programming

Module Overview

This module will cover the fundamentals of SAS base programming.

On successful completion of this module, you will be able to use SAS

software to read data, access, manage and manipulate ‘big’ data,

solve problems, save programs and datasets, use conditional logic,

combine files, produce tables and listings of data, and use data

analysis techniques.

Module Aims

To provide you with basic training in using the SAS software for

manipulating, analysing and visualising data.

Learning Outcomes




• Key computer programming concepts for managing and analysing

‘big data’;

• The SAS Base language and how it is used to manage, analyse and

report data.



• Read structured and unstructured data stored in popular formats such as

excel worksheets, text tables into SAS;

• Store data in SAS libraries;

• Manipulate, analyse, visualize and report data using SAS.





• Use your analytic skills in problem solving;

• Communicate technical ideas to non-specialist managers.

Syllabus


• Overview of SAS: Submitting SAS programs, Working with SAS

syntax and SAS libraries

• Introduction to reading data and adding attributes: Reading Excel

worksheets, Reading delimited raw data files

• Manipulating data in SAS: Combining SAS Datasets, Producing

Summary Reports, Enhancing Reports using Global statements and

using ODS, Outputting to multiple datasets or observations

• Reading Raw Data Files with input controls: Data Transformations-

manipulating character and numeric values, Debugging Techniques-

putlog and debug

• Processing Data Iteratively- Do loops and Arrays, Restructuring a

Data Set with rotate and transpose, Analysing relationships among

variables: proc corr, regression and logistic



• Lectures


• Computer Labs









• Private study



• Sas Institute (2011). SAS Certification Prep Guide: Base

Programming for SAS 9.

• Peng, J.C.Y. (2009). Data Analysis Using SAS.

Documents

Module 01: Business Mathematics & Statistics...Module 01: Business Mathematics & Statistics Module Overview The module forms an introduction to the mathematical and statistical skills