University of Windsor
Windsor, ON
Data Analytics and Decision Making by Ali AbdulHussein is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.
Data Analytics and Decision Making by Ali AbdulHussein is licensed under a Creative Commons Attribution 4.0 International License
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Data Analytics is a rapidly-evolving field applicable to many business, engineering, and computer science workplace contexts. Open educational resources are more likely to be better options than costly, traditional commercial textbooks that become outdated rapidly. The digital content in this new online course, Data Analytics and Decision Making, introduces learners to fundamental concepts of data analytics and their application in decision making. The emphasis is on the utilization of practical analytic tools in a complex engineering management environment. Topics covered include careers in data analytics; data types, formats, and repositories; big data and cloud computing; stages of data analytics; descriptive analytics; predictive analytics, including statistical learning and machine learning; and prescriptive analytics with decision-making examples.
At the end of the course, the successful student will know and be able to:
The course will be offered within the curriculum of graduate degree programs in the Faculty of Engineering at the University of Windsor such as: Master of Engineering, Master of Applied Science, and Master of Engineering Management. The modular design of the course will allow future alignments with other programs.
Today’s labor market, namely for engineering graduates, demands data analytics skills. Employers are increasingly requiring candidates to be able to deal with large amount of data and be able to interpret and utilize data in decision making. To further verify this market need, we have conducted a labor market analysis (https://www.services.labour.gov.on.ca/) and scanned skills required for jobs with Above Average job outlook for 2017- 2021 such as: computer network technicians, computer and info systems managers, info systems analytics, system consultants and database analysts. Data-related skills were demanded in these jobs.
The technical nature of the course lends it the strong potential to be delivered online with high quality. In Summer 2022, this course will be offered for the first time as a required course (GENG-8050) in the Master of Engineering Management program that attracts full-time working professionals. Hence, offering it in a virtual format (fully or partially) adds to programming flexibility.
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This project was developed with funding from the Government of Ontario and eCampusOntario Virtual Learning Strategy.
Data Analytics was developed by Prof. Ali AbdulHussein (Odette School of Business) in collaboration with Dr. Bahman Naderi (Faculty of Engineering) at the University of Windsor. Dr. Bahman contributed draft presentation slides, labs and tutorials, and assignments for the course.
Dr. Nobuko Fujita (Office of Open Learning) provided instructional design support, and Dr. Chris Teplovs recorded the lecture videos.
Dr. Beth Robertson contributed her Equity, Diversity, Decolonization, and Inclusion (EDDI) expertise in reviewing the course materials and providing advice.
Sakshi Arora, our student partner specializing in the Business Analytics stream. Odette Master of Management, contributed to editing data analytics equations for accessibility and multimedia production and post production.
Shreyas Tambe (Office of Open Learning) provided support for video production and post-production.
Patrick Carnevale (OOL Co-op Student; School of Computer Science) provided graphic design support on Dr. Naderi’s slides.
Current students in the Master of Management Program, Mayank Ghai, Hasib Imam, Muhammad Shahid, and Spencer Stinson, contributed to the creation of the course by providing feedback on the use of data analytics in their professional engineering contexts and experiences with online learning.
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While we attempt to make all elements of this resource conform with international accessibility guidelines, we must acknowledge a few accessibility issues:
This accessibility statement is adapted from Understanding Document Accessibility by The Chang School, Ryerson University is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License, except where otherwise noted.
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The suggested citation for this book in APA format is:
AbdulHussein, A. (2022). Data Analytics and Decision Making. University of Windsor. http://ecampusontario.pressbooks.pub/dataanalytics/
The suggested attribution for this textbook is
Data Analytics and Decision Making by Ali AbdulHussein is licensed under a Creative Commons Attribution License 4.0
If you adopt this book, as a core or supplemental source, please report your adoption in order for us to celebrate your support of students’ savings! Report your commitment at https://openlibrary.ecampusontario.ca/report-an-adoption/
We invite you to adapt this book further to meet your and your students’ needs. Please let us know if you do! If you would like to use Pressbooks, the platform used to make this book, contact eCampusOntario for an account using open@ecampusontario.ca.
If this text does not meet your needs, please check out the full library at www.openlibrary.ecampusontario.ca. If you still cannot find what you are looking for, connect with colleagues and eCampusOntario to explore creating your own open education resource (OER).
eCampusOntario is a not-for-profit corporation funded by the Government of Ontario. It serves as a centre of excellence in online and technology-enabled learning for all publicly funded colleges and universities in Ontario and has embarked on a bold mission to widen access to post-secondary education and training in Ontario. This textbook is part of eCampusOntario’s open textbook library, which provides free learning resources in a wide range of subject areas. These open textbooks can be assigned by instructors for their classes and can be downloaded by learners to electronic devices. These free and open educational resources are customizable to meet a wide range of learning needs, and we invite instructors to review and adopt the resources for use in their courses.
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One or more interactive elements has been excluded from this version of the text. You can view them online here: https://ecampusontario.pressbooks.pub/dataanalyticsvls1/?p=25
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Exercises
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Photo by Mika Baumeisteron Unsplash
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One or more interactive elements has been excluded from this version of the text. You can view them online here: https://ecampusontario.pressbooks.pub/dataanalyticsvls1/?p=41
Chapter 1. Descriptive Statistics and Frequency Distributions
Chapter 2. The Normal and t-Distributions (only the normal distribution)
This is a chapter from a free, open textbook that has been adapted to the Canadian context. When read online, it allows readers to learn the basic and most commonly-applied statistical techniques in business in an interactive way using Excel spreadsheets.
Introductory Business Statistics with Interactive Spreadsheets – 1st Canadian Edition by Mohammad Mahbobi & Thomas Tiemann is licensed under a Creative-Commons Attribution 4.0 License.
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One or more interactive elements has been excluded from this version of the text. You can view them online here: https://ecampusontario.pressbooks.pub/dataanalyticsvls1/?p=43
Lab 1
The instructor will go over the following file in class. Load the following file to practice creating data visualizations such as a boxplot and pivot table.
DataVisualization_Template.xls
Lab 2
Load the following file to practice creating data visualizations.
Data Source:
Amazon Top 50 Bestselling books 2009-2019,
https://www.kaggle.com/sootersaalu/amazon-top-50-bestselling-books-2009-2019
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In-Class Optional Exercises
The instructor will go over these examples in class. Load the following files to practice analyzing data summarization.
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Use the dataset, Case_Insurance.csv to answer the following questions:
Consider the fact that not everyone identifies within a binary of male/female or man/woman. For the purposes of this assignment, we are using the word “sex” to refer to the physiology of the person. A better word to use may be “gender.” This is because preconceived notions and biases associated with gender, rather than solely the physiology of the person, has been proven to affect health insurance rates and access to health services more generally.
Katherine Hay, M. A., et. al. (2019). “Disrupting Gender Norms in Health Systems: Making the Case for Change,” Lancet, 393 (10190), pp. 2535-2549. https://doi.org/10.1016/S0140-6736(19)30648-8
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Photo by PAUL SMITHon Unsplash
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One or more interactive elements has been excluded from this version of the text. You can view them online here: https://ecampusontario.pressbooks.pub/dataanalyticsvls1/?p=51
This is a chapter from a free, open textbook that has been adapted to the Canadian context. When read online, it allows readers to learn the basic and most commonly-applied statistical techniques in business in an interactive way using Excel spreadsheets.
Introductory Business Statistics with Interactive Spreadsheets – 1st Canadian Edition by Mohammad Mahbobi & Thomas Tiemann is licensed under a Creative-Commons Attribution 4.0 License.
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One or more interactive elements has been excluded from this version of the text. You can view them online here: https://ecampusontario.pressbooks.pub/dataanalyticsvls1/?p=53
This is a chapter from a free, open textbook that has been adapted to the Canadian context. When read online, it allows readers to learn the basic and most commonly-applied statistical techniques in business in an interactive way using Excel spreadsheets.
Introductory Business Statistics with Interactive Spreadsheets – 1st Canadian Edition by Mohammad Mahbobi & Thomas Tiemann is licensed under a Creative-Commons Attribution 4.0 License.
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One or more interactive elements has been excluded from this version of the text. You can view them online here: https://ecampusontario.pressbooks.pub/dataanalyticsvls1/?p=55
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Lab 5
Load the following file to practice multiple linear regression.
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Use the dataset, Logistic_Regression_Case_Template, to analyze whether an individual will default on their credit card payment based on their annual income monthly credit card balance, and student status for a subset of 10,000 individuals.
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Exercises
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Cross Validation Tutorial
Load the following file for a cross validation tutorial
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Lab 8
Load the following files to practice MCNFP
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Exercises
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Lab 10 – part 2 of 4
Load the following files to practice investment management
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Exercises
Lab 10 – part 3 of 4
Load the following file to practice stochastic decision tree analysis
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We recommend using an Ivey Case Study, Research and Development at ICI: Anthraquinone (1999, revised 2010) by Peter C. Bell. This case is available for purchase, per person for a low cost (CAD 9.00) and has an accompanying Microsoft Excel model available.
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Exercises
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A hotel with 100 rooms considers entering into the booking market. There are two demands:
Early customers first book and pay $10 which is non-refundable. They can cancel the booking anytime before one week to the date. In the case of finalizing, they pay another $50. Late customers pay $80 which is non-refundable.
The hotel currently plans for booking policy. Past data shows that at least 80% of early customers finalize the booking. If overbooking is realized, the hotel pays a penalty of $150 to each booked customer.
Use the dataset, Assignment Hotel.xlsx, to analyze how the hotel can maximize profit.
Maximize Profit (X) = Booking fee + Early demand profit + Late demand profit – Overbooked cost