UNIVERSITY COLLEGE DUBLIN
Bachelor of Business Studies (Singapore)
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Data Analysis for Decision Makers (MIS3008S)
STUDY GUIDE
BBS 35 FT / Singapore
Copyright October 2021
Author: Seán McGarraghy (2021)
This guide was prepared for University College Dublin as a comprehensive support for students completing the above-mentioned Degree programme.
© This publication may not be reproduced, in whole or in part without permission from University College Dublin.
Module Co-ordinator: Dr. Seán McGarraghy
Office: +353 1 716 4734
Local Lecturer
TABLE OF CONTENTS
WELCOME MESSAGE
1. INTRODUCTION 7
a. Background Details
b. Module Aims
c. Programme Goals
2. MODULE OUTLINE 12
a. Module Learning Outcomes
b. Themes and Topics
c. Learning Materials
3. MODULE DELIVERY SCHEDULE 16
a. Session Arrangements
b. Student Engagement
c. Office Hours
4. ASSESSMENT DETAILS 20
a. Assignments
b. Module Assessment Components
i. Team Project
ii. Examination
5. GRADING 24
a. University Grading Policy
b. Grade descriptors for assessment components
6. CONCLUDING COMMENTS 31
WELCOME MESSAGE
Dear Student,
As co-ordinator of the Data Analysis for Decision Makers module, I wish to welcome you to the module.
In recent years, especially with the advent of high-performance computing, analytical and mathematical approaches have become increasingly important in addressing business and other problems; notably problems where a decision must be made with reference to useful information in a large dataset. Business Analytics is the field of study concerned with quantitative methods of analysis in the context of business. Business Analytics means using data, mathematical modelling, statistics and computational techniques to achieve these goals in the context of business: understanding, prediction, and evidence-based decision-making. Data Analysis (including Business Statistics) is a crucial part of this.
The objective of this course is to develop an understanding of the application of quantitative techniques for data analysis to aid decision making in business and management. You will learn how to conceptualise complex business problems and apply mathematical and statistical approaches to these problems. While the word mathematical is used to describe the approaches, the required level of mathematics is not high. This module demonstrates the power and versatility of simple statistical models and measures.
The Study Guide is designed to support your learning. There is no set textbook for this course, as the comprehensive lecture notes will suffice; however, we give some useful texts for background and extended reading. You will be introduced to the use of computer packages (Excel) to implement and solve models for sample problems and assignments, and to compute summary statistics. The principles of Active Learning guide the face-to-face contact sessions with students engaging in hands-on mathematical modelling exercises.
I encourage you to be active learners: ask questions and make relevant points. Although class debate is less feasible in a technical course such as this one, we will still be open to it where possible. I hope you find this module challenging and rewarding. Should you require clarification on any matter pertaining to the module, please do not hesitate to contact the lecturer.
· Seán McGarraghy
INTRODUCTION
“I think a basic understanding of data analytics is incredibly important for this next generation of young people. That’s the world you’re going into. By data analytics, I mean a basic knowledge of how statistics works, a basic knowledge of how people make conclusions over big data.” — , executive chairman of Google’s parent company Alphabet, quoted at https://www.cnbc.com/2017/03/31/google-execs-agree-on-the-skill-employers-will-look-for-in-the-future.html.
To operate effectively within the Global Business World, it is essential to understand the theory and practice of Data Analysis and Business Analytics in the context of Management Decision Making. In the “Big Data” era, there is a challenge to turn data into insight. Data Analysis is the science by which raw data is processed and analysed so as to provide managers with useful information to aid their decision making. This knowledge and skill will define the future economies of many nations like Singapore (http://www.straitstimes.com/politics/pm-lee-zooms-in-on-three-areas-for-singapore-to-prepare-for-the-future). In today’s era of “Big Data”, huge data sets are ubiquitous and cheap. In contrast, as pointed out by Google’s Chief Economist and Prime Minister of Singapore Mr Loong, the analytic ability to utilise this data is a scarce and highly prized skill that will be the focus of human capital development in Singapore for the next decade. Thus, an individual capable of extracting and communicating valuable information from data will be highly sought after by any employer. This module will equip students with knowledge of the theoretical underpinning of data analysis and hands-on experience of its implementation. Given that data analysis is the basis for all decision making at every business level, such knowledge will be necessary in order to avoid the pitfalls of uninformed analysis and oversimplified decision-making processes. As the science of “good” decision making in the face of uncertainty, Data Analysis is used in a wide array of disciplines, such as financial analysis, econometrics, auditing, risk assessment, production, operations research, insurance underwriting, and marketing research, to list just a few. On the successful completion of this module, students will have the necessary knowledge and skills to use and critically interpret a range of statistical techniques, which are employed in a multitude of business applications
In this module, we will consider issues around the collection and display of data, use descriptive statistical techniques to summarise and characterise collected data, and employ inferential statistical methods to inform whatever decisions are taken on the basis of the data. Given the widespread use of statistical methods, managers must be well informed about how statistical methods can be used in business, as well as have some introduction to programs that use statistical methods.
This module will equip you with an understanding of the theoretical basis of these statistical methods, as well as providing an opportunity to gain practical experience of analysing data via the Microsoft Excel software package. It is designed to deepen your interest and expertise in the area of Data Analysis, prepare you for a deeper study of Business Analytics and provide you with the skills and knowledge that will enable you to analyse data to assist management with decision making. While much of the focus is on knowledge acquisition, attention is also given to enhancing and developing your professional and personal skills and competencies. To successfully complete this module, several learning activities are to be completed, which should provide enjoyment and fun, and help the attainment of the module learning outcomes.
We concentrate on Data Analysis in Management Decision Making and the role that data, statistical models and computation can play in enabling management to make good and informed decisions. Information systems are adept at processing vast quantities of data to give information. The skill of the Analytics professional is to derive knowledge and insight from these data. The next question is “how does management use this to make informed decisions?” We argue that (part of) the answer lies in models incorporating mathematical, statistical and computer approaches. The input to the models is data, and the output is informed decisions.
This Study Guide is designed to provide you with the essential information for this module: the learning outcomes; and delivery and assessment arrangements. It may change slightly over the duration of the course: if this happens, an updated version will be uploaded to Brightspace. The Study Guide consists of 6 parts.
Part 1 provides background details to the subject area and sets out the broad aims of the module and how it fits within the programme goals.
Part 2 consists of the module outline. In this part we explain: (a) the module learning outcomes, (b) the themes and topics to be explored and (c) the learning supports to be used.
Part 3 gives details of the module delivery arrangements. It sets out the session contents and the expectations for your prior preparation and student engagement. The times and dates of the sessions will be provided in an appendix.
Part 4 provides details of the assessment techniques used in this module explaining the assessment components and their rationale.
Part 5 explains the UCD grading policy, and grade descriptors drawing on the university document are given for the Examination (online, open book).
Part 6 presents the concluding comments.
The Appendices discuss managing groupwork, UCD regulations, example exam questions and the session timetable.
Accessing Brightspace Live Zoom Classes
This module will be wholly delivered via UCD’s integrated Zoom classroom.
Kindly logging into Brightspace, go to “MIS3008S-Data Analysis for Decis Makers-2021/22 Summer”, click “My Class”, “Zoom”.
Please always login using your UCD email address and your name. Your name should be visible to the lecturer and other students to facilitate collaboration.
Please join your online session no later than five minutes before the advised time of your session.
Engagement tools on Collaborate
Throughout the online sessions for this module, you will be frequently asked to engage with both your lecturer, and with your fellow students.
The lecturer may send you into breakout groups and you discuss some class content in smaller groups before your findings are discussed with the whole class. You may use the “Share Screen” function (if enabled) to show some summary points of the breakout group discussions.
If you select “Chat”, a chat window will open and you can communicate with the whole class or with your lecturer. If you would like to send a private message to your lecturer, please select your lecturer’s name instead of everyone.
By clicking on “Reactions”, another menu will open. This menu allows you to raise your hand if you have a question or would like to comment. If you see a hand icon in the left upper corner of your screen, your hand is currently raised. You can lower your hand by clicking on this icon a second time. The lecturer can also lower your hand.
When you join a Zoom session, you will be muted, and your camera is turned off. But for better engagement in the class, it is advised to keep your camera turned on. Please only unmute yourself if you would like to speak to avoid background noises. You can change your audio and video setting by clicking the small arrow beside the “Unmute” or “Start Video” icon.
Background Details
Statistical and Probabilistic analysis, and other mathematical modelling techniques, have long been used in business, from Fermat and Pascal’s probabilistic estimates of the value of a game of chance in the 1600’s, to Bachelier’s introduction of rigour to finance in 1900, to Pearson’s development of summary statistics in the early 20th century, to subsequent advanced work in statistical and machine learning. In recent years, especially with the advent of high-performance computing, mathematical and statistical approaches have become increasingly important in addressing business and other problems. In the last decade or so, the discipline of Analytics (incorporating Management Science and Operations Research) has arisen, where mathematical, statistical and computational techniques are used to aid decision making by extracting the crucial information from large datasets. Recent surveys by Accenture, IBM and others have shown:
· 83 percent of respondents identified business analytics as a top priority and a way to enhance competitiveness
· 72 percent are working to increase their company’s analytics usage;
· only half believe they are spending enough on analytics
· over a third said they face a shortage of analytics and data science talent.
We regard Business Analytics as Evidence-based Decision Making, or, in a little more detail:
how an organisation gathers, searches, models, analyses and interprets data so as to aid decision-making and improve/optimise business processes.
It ranges over a spectrum from the softer topics such as decision science and multi-criteria decision making to quantitative approaches such as data mining and exact search and optimisation algorithms. It uses quantitative and computational approaches to solve problems and aid decision-making and improve or optimise business processes.
An important part of how Business Analytics aids decision making is via Data Analysis. Data Analysis is the application of statistical techniques to describe and explore a set of data with the aim of highlighting useful information and so supporting evidence-based decision making.
Our objective in this course is to develop an understanding of Data Analysis techniques and their application to problem solving in business and management.
You need some mathematical background for this course, which you should have from your school courses.
This module is delivered through the Brightspace eLearning environment, using blended learning. All topics are dealt with from a theoretical and applied point of view. Lecture notes, examples and case studies are used to further develop the applied nature of the subject.
We employ the principles of active learning and require students to engage fully with all class and assigned exercises. Before each class, the lecture notes and other learning resources will be uploaded to Brightspace: you should have read over these before class, to have an idea of what will be covered, and to identify questions you may have for the class. Participants will engage in active learning exercises during online contact time.
I encourage you to ask questions (about points that are unclear or on which you hold a different opinion) and make relevant points. You can do this by first pressing the “Raise Hand” button on Zoom and then using either the Chat facility or your microphone. Engage fully with all class exercises and assignments. Take responsibility for your own learning. Raise issues as they arise. Please ensure that you promote a constructive learning environment. In particular, please turn off your microphone and camera unless asking a question.
Our course will have a practical element, so we will solve problems both on paper and by using computer packages. Some experience with computers is helpful but not required: you will be shown how to use any computer applications required. In particular, we will make extensive use of Microsoft Excel in class. You will need to install the Excel software and bring your laptop to class. Make sure to install the Analysis ToolPak: to do this, go to the menu item
Tools -> Excel Addins…
then click on the Analysis ToolPak box and hit OK.
Students are required to work together in small groups to discuss, analyse and propose solutions to problems posed in-class and in assigned project work.
You must abide by the provisions of the UCD Student Code. You are expected to attend all class sessions, unless you have a pressing reason.
All deliverables, whether individual or group, must comply with UCD policies on Academic Integrity and Plagiarism. Please see Appendix 3 for further information on Plagiarism and the policy on the Late Submission of Coursework. Emails should comply with the official UCD policies on email.
Module Aims
This module is a foundation in data analysis for all business students and aims to serve the needs of subsequent courses in areas such as marketing, finance, accounting and business analytics. The three main areas introduced in this course are:
1. Quantitative Analysis and Descriptive Statistics: how to gather, present and interpret large volumes of data in order to describe the information in concise and useful ways.
For example, what is the average spend of a sample of customers in a coffee shop?
2. Probability and Distributions: discrete and continuous distributions with examples from the real world;
3. Inferential Statistics: how to infer population parameters from sample statistics.
For example, estimate how much is likely to be spent in the coffee shop in total.
Thus, the aim of this module is to provide students with an overview of the theory and practice of a range of Quantitative Statistical approaches in Management Decision Problems. This includes
· Describing the main concepts and application areas of Data Analysis;
· Describing the main principles of a suite of key analytical and statistical approaches, including descriptive and inferential statistics;
· Application of these principles to improve the quality of analysis and decision-making;
· Discussion of a portfolio of important business and other applications of these principles;
· Understanding the use of mathematical computer packages as an aid in data analysis and evidence-based decision making.
· Interpreting and communicating the meaning of statistical results in a managerial context.
These are fleshed out in the themes below. The assessment tasks for this module have been designed with this in mind as detailed later in the study guide.
Programme Goals: Bachelor of Business Studies (Singapore)
Pathway: MIS
Programme Goals
Programme Learning Outcomes
On successful completion of the programme students should be able to:
Data Analysis for Decision Makers
Module Components
Programme Goal 1:
Informed Thinkers: Our graduates will be knowledgeable on management theory and will be able to apply this theory to business problems (Knowledge).
Programme Learning Outcome 1a:
Explain current theoretical underpinnings of business and the management of organisations.
Understand foundations of quantitative approaches to business problems:
Assignment, Examination
Programme Learning Outcome 1b:
Apply appropriate methods, tools and techniques for identifying, analysing and resolving business problems within functional and across functional business areas.
Quantitative analysis, solution and interpretation using statistical techniques and spreadsheet tools
Examination
Programme Goal 2:
Communication, Analytical and Critical Thinking Skills: Our graduates will have well developed skills of communication, analysis and critical thinking (Skills and Competencies).
Programme Learning Outcome 2a
Prepare a short business presentation (written and/or oral) on a current business issue.
Programme Learning Outcome 2b:
Analyse specific business case studies or problems and formulate a report detailing the issues and recommended actions.
Analyse and report on a business problem using data analysis
Assignment, Examination
Programme Learning Outcome 2c:
Conduct secondary research on management-related issues and report on the findings and draw appropriate conclusions.
Provide executive summary of analysis work
Assignment
Programme Goal 3:
Personal and Professional Development: Our graduates will demonstrate a commitment to personal and professional excellence and development (Skills, Competencies and Attitudes).
Programme Learning Outcome 3a:
Develop collaborative learning and team-work skills by engaging in module-related team activities.
Programme Learning Outcome 3b:
Demonstrate capacity for problem solving collaboratively and individually.
Apply appropriate methods to quantitative business problems to solve and interpret result
Assignment, Examination
Programme Goal 4:
Ethical Awareness: Our graduates will demonstrate an awareness of ethical issues in business and
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