Big Data: Part One - Introduction

By Brielle Huang

Opportunities to leverage  big data are almost as immeasurable as the amount of big data. As more and more success stories of innovative users become available, corporations, institutions, and governments are starting to realize the need to understand big data. The following series of blog posts are meant to educate the reader on the concept of big data, the key players in big data (i.e., demand and supply side of big data, as well as mediators), and the diffusion of big data.

Technological changes in the last 20 plus years have changed our notion of what data is. For example, in the past accountants have associated data with transactions (e.g. sales and cash receipts). With the introduction of the Internet, this concept began to change. The new tech companies (e.g., Amazon, Google, and eBay) brought with them new sources and types of data. The sales transaction was transformed. What used to be, for most of transactions, a single data point became a cluster of data including among others the customer’s name, credit card, and address, as well as the customer’s browsing pattern (web traffic and click through rate). Later in the early 2000s, as social network firms like Facebook and Twitter came into existence, the ability to capture information related to a user’s online activities created a fundamental change in the type of information companies sought to capture. Instead of only providing binary information like whether the user was online at a certain time, Web 2.0 gave companies the ability to capture valuable information about preferences and human interactions. With the continued progression of technology, currently we are in the process of supplementing networks of humans with networks of machines - a phenomenon known as the Internet of Things (IoT). The IoT could provide us with more data about the product that customers buy.

The problem with keeping data is that it costs money to store it. Luckily, as technology such as cloud computing has improved, storage capacities have increased and the cost of storing has become cheaper. Paraphrasing Parkinson’s First Law in his 1980 speech  I.A. Tjomsland quipped that as storage became cheaper, the ability to retain large amounts of data which could become useful to the future increased. This points to the importance of making sure that captured/stored data are leveraged to create value.

In order to proceed with the analysis of the life of the big data phenomenon, it is necessary to first come up with a reasonable definition of big data. This first blog post will aim to do just that. Throughout the years, many experts have come up with their own definition of big data. We will attempt to look at the similarities between them in order to come up with our own definition.

Cox and Elseworth (1997):

“Visualization provides an interesting challenge for computer systems: data sets are generally quite large, taxing the capacities of main memory, local disk, and even remote disk. We call this the problem of big data.”

SAS:

“Big data is a term that describes the large volume of data - both structured and unstructured - that inundates a business on a day-to-day basis.”

In simple terms both of the above definitions can be understood by referencing tools such as MS Excel and MS Access. Both of these tools have been used extensively in business settings. Therefore, big data could be visualized as a data set that could force the limits of these tools.  For example, Microsoft Access only has a capacity of 2GB and Excel has a maximum of about a million lines.

Gartner:

“Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.”

High volume means that the data sets are much larger than the data sets we are used to - for example, there are almost 2 billion users on Facebook and the data sets that contain their profile and information would be enormous. This data would be impossible to organize in an a database management system like MS Access. High velocity means that the data is being captured at an incredibly high speed - for example, throughout a day one Facebook user could be liking upwards of a dozen photos, posts, and news articles. High variety means that the data is not structured and so cannot be easily processed - for example, you would be hard-pressed to capture the information from Tweets on an Excel sheet. In general very large data sets that combine structured and unstructured data are likely to exceed the storage and analyzing capability of traditional software applications like MS Access and Excel.

From the above definitions, several themes emerge:

1. Big data indicates a high volume of data
2. Big data is a continual flow of data, which means it must be processed in a timely manner or the business’ systems will become inundated
3. The data collected can be structured or unstructured

While big data is one of the emerging technologies with far reaching implications for  businesses, we would like to make sure that our readers realize that it differs from several other technologies. In fact, big data is not necessarily linked to a particular supplier/vendor or a patented technology. Unlike systems like Enterprise Resource Planning (ERP) or Customer Relationship management (CRM), big data is not as simple as a system that someone can patent. This is mainly due to the third theme we distilled above: structured and unstructured data must first be organized and processed before it can be analyzed and/or stored. Due to the high volume of complexity of the data, these steps would probably need to be conducted by different players who specialize in each step. The significance of this “unpatentable” phenomenon as well as the three themes discussed above will be utilized in our analysis of big data in our next blog post, which will be about the major players in big data - namely the demand, supply, and mediation sides.

Brielle Huang is a third year Accounting and Financial Management Student minoring in Legal Studies at the University of Waterloo. She is working as a Research Assistant under Professor Stratopoulos and researching emerging technologies, with a focus in Big Data. Brielle has completed her first coop term in Assurance at PwC. Her other interests include creative writing and travelling.

Sources:

- Cox, M., & Ellsworth, D. (1997). Application-controlled demand paging for out-of-core visualization. In Proceedings of the 8th IEEE Visualization ’97 Conference (p. 235-). IEEE Computer Society Press. Retrieved from http://dl.acm.org/citation.cfm?id=266989.267068

- Gartner: http://www.gartner.com/it-glossary/big-data/

- Press, G. (2013, May 9). A Very Short History Of Big Data. Retrieved October 31, 2016, from http://www.forbes.com/sites/gilpress/2013/05/09/a-very-short-history-of-big-data/

- SAS: http://www.sas.com/en_us/insights/big-data/what-is-big-data.html

-  TDWI - Moving Beyond Spreadsheets: http://www.sas.com/content/dam/SAS/en_us/doc/whitepaper2/tdwi-moving-beyond-spreadsheets-107310.pdf

Blockchain Technology Conference

I will be presenting my work on blockchain technology adoption at the UWCISA - Blockchain conference.

Blockchain Technology Conference 

for finance, accounting and auditing professionals

September 30, 2016 -  8:30 am-5:30 pm

St. Andrew’s Conference Centre, 

150 King St. W, 27th Floor, 

Toronto, ON M5H 1J9 Canada

You more details about the conference/program visit the following URL: 

http://jebcl.com/symposium/register-blockchain

For a preview of my presentation please visit the following URL:  

Blockchain Adoption

School of Accounting and Finance at 2016 Accounting IS Big Data Conference

The following work

Audit Data Analytics Survey: 

Current State and Future Directions

by

Clark Hampton  & Theo Stratopoulos

was presented at the 2016 Accounting IS Big Data Conference.

A copy of the presentation is available from the following link:
Audit Data Analytics Survey: Current State and Future Directions

The work was sponsored by CPA Canada and the Audit Data Analytics task force. For information about the work of the task force visit the following link:
Audit Data Analytics Committee

For more details about the conference program visit the following link:
2016 Accounting IS Big Data Conference

Week 13 - Epilogue

In the last two weeks our focus has been on the value of IT investments and how to ensure that we maximize the value from these investments. Another topic that deserves attention is that of IT budgets and IT portfolio management. Investment like the one undertaken by WHR constitute a substantial request for financial and human resources. Therefore, one of the  final course objectives will be to review corporate budgets (pros and cons), the IT budgeting process & factors affecting levels of IT budgets, and IT portfolio management.

We will close the course with a look at some of the basic principles behind IT governance.

Topics and Readings for Week 13

Theory: Chapter 9 (IT budgets and IT portfolio management) and Chapter 10 (IT Governance).

Seminar: There is no seminar this week.

Assignments for Week 13

There is no online quiz. I will post a practice quiz based on material from Chapters 9 and 10.

Week 12 - Risk Analysis and Monitoring of IT Investments

The NPV (expected payoffs) of a technology investment depends on the assumptions made by managers involved in the adoption of the new technology. These assumptions reflect their expectations regarding implementation (budget, time, and functionality), expectations regarding users’ ability to extract value from the new technology (increase sales or contain cost), as well as expectations regarding reactions of competitors and trading partners.

In previous chapters, we focused on factors that may affect these assumptions, such as business and IT strategy, IT capability, stage of technology adoption, and industry structure. This week, we shift our focus to implications of failure to meet these assumptions (risk analysis) on NPV. We will use this risk analysis to make recommendation on  how to monitor the progress during implementation and value extraction. The aim of the monitoring process is to introduce an element of accountability and ensure that proper action would be taken to ensure the maximization of the value of the technology investment.

Topics and Readings for Week 12

Theory: We will focus on Chapter 8 (monitoring IT investments). The primary focus of this chapter is on the idea of causality (cause and effect) as the foundation for the creation of a balanced scorecard for technology implementation and use.

Seminar: We will use the Whirlpool case study to review the NPV analysis and  perform a risk analysis (sensitivity analysis). We will use data based on different scenarios to generate predictions regarding the expected effect of these scenarios on the NPV of the project.

Assignments for Week 12

The online quiz will be a based on chapters 8  and material covered in the seminar. The quiz will be available on Friday at 12:30 pm.

The team project is due on Monday at 8:30 am. Please upload your project file as well as your data and R-script in the appropriate Learn dropbox.

The peer evaluation for the Team Project will become available on Tuesday - July 19 and remain open till July 26th. Please read the syllabus and make sure you understand the implications of the peer evaluation on the grade of your peers before you submit your evaluation. The submission of peer evaluation is a course requirement.

Prof. Stratopoulos

Week 11 - Evaluation of IT Investments

We started our course with the discussion about a firm (WHR) that wants to perform capital budgeting analysis of a major information technology investment. An investment in an enterprise system. Our objective has been on how to approach this evaluation from a strategic standpoint. We started by looking at technology innovation adoption and how the position that a firm takes will affect the expected payoffs (chapter 1). We combined Rogers’ innovation adoption theory with Gartner’s Hype cycle to estimate expected payoffs and duration of competitive advantage (chapter 2).

We learned that technological innovations have the potential to disrupt the competitive landscape and it is important for a firm to consider its technology related investments in the context of its strategic priorities (chapter 3). We used information generated in financial reports to make sense of business strategy and industry structure, and looked at how technology adoption (such as data analytics) can shape the competitive position of adopting firms (chapter 2, 3, and 4).

For firms to leverage data analytics they must have access to data, and this justified the need to have at a minimum a basic understanding of database theory (chapter 5). We used the examples of database schemas capturing business processes of different firms as a way of envisioning the foundation of an enterprise system (chapter 6). This brought the realization that even though enterprise systems constitute a mature technology today, they are critical for any firm that wants to leverage data analytics, because they are the source of all internal data (chapter 6).

Understanding the importance of implementing or upgrading a firm’s enterprise systems, brought us back to the place we started, i.e., perform the capital budgeting analysis for WHR (chapter 7), which is the topic of Week 11.

Topics and Readings for Week 11

Theory: We will focus on Chapter 7 (evaluation of IT investments). The primary focus of this chapter is on the expected benefits from IT investments and use of Real Options in assessing the value of multistage projects.

Seminar: We will use the mini-case of BlueBikes in order to understand the foundation of the capital budgeting setting used in the WHR case. Read carefully the part of the chapter that will help you understand the BlueBikes case so you can replicate the WHR case. While we will spend most of our time doing the capital budgeting analysis, I will reserve some of the seminar time to show you how to leverage some of the advanced functionality in R (e.g., neural networks) to forecast sales based on Compustat data.

Assignments for Week 11

The online quiz will be a based on chapters 7, the WHR case,  and material covered in the seminar. The quiz will be available on Friday at 12:30 pm.

Week 10 - Intro to Enterprise Systems

We continued working on database theory (reading database schemas and basic normalization principles). We used these knowledge to generate queries that related to operational needs of a company. We emphasized the need to be able to translate an English question to SQL and vice versa and its importance in the auditing process.

In week 10, we will focus on the database schema of one of the cases (GRT) and use it to understand the role and functionality of ERP systems with a company.

Topics and Readings for Week 10

Theory: Read chapter 6 (enterprise systems).

Seminar: In our seminar we will continue working with sql queries. We will some applications of SQL in the context of auditing (fraud detection).

Assignments for Week 10

The online quiz will be a based on chapters  6,  and material from the seminar. The quiz will be available on Friday at 12:30 pm.

Week 9 - Database, the heart of an Enterprise System

This week we are going to look at several examples (mini-cases) of business processes and their corresponding  database schemas. Our first objective is to familiarize ourselves with relevant database theory and more specifically database normalization theory. The second objective is to learn how to visualize the basic database structure and information flow behind a business process.

Looking at these examples will provide us the foundation for transition to enterprise systems (Chapter 6). The heart of an enterprise system is an integrated database. We will focus on the role that these systems play within the company, expected benefits, risks as well as critical success factors associated with their implementation and adoption.

Topics and Readings for Week 9

Theory: Read chapter 5 (database theory) and chapter 6 (enterprise systems).

Seminar: In our seminar we will continue working with SQL queries.  While last week we learned how to work with one table, this week we will combine multiple tables. One of the exercises that we will work on is motivated by the experience of Harrah’s. We will develop and use a query to identify a firm’s most valuable customers (observe the Pareto 80:20 principle in terms of sales and customers).

Assignments for Week 9

The online quiz will be a based on chapters  5,  and material from the seminar. The quiz will be available on Friday at 12:30 pm and stay open till Monday 8 am.

Week 8 - Data the fuel of analytics

In the second half of the course our focus will shift to information technology related to tactical and operational issues (we will use analytics to support decision making at the tactical and operational level). The topic for this week will be on database theory. We will learn about database theory, because data is the fuel of analytics.

More specifically our focus for this week will be on the following topics: Reading entity-relationship diagrams. An entity-relationship (E-R) diagram is the blueprint of a database. It provides us with a concise way of seeing the  entities (tables) and how these entities are related. This is critical if we want to design queries for data extraction.

Understanding the process for designing a normalized database. We will look at several examples based on mini cases and see how we can design a normalized database.

In our seminar, we will use the sqldf package in R in order to write sql queries for data extraction. We will start with queries based on data from a single table and make our way to more advanced queries based on multiple tables.

Topics and Readings for Week 8

Reading: Read chapter 5

Assignments for Week 8

The online quiz will be a based on chapters  5,  and material from the seminar. The quiz will be available on Friday at 12:30 pm.

Week 7 - Review and Midterm

We have completed the first half of the course. The focus has been on IT and technology adoption from a strategic data analytics standpoint. During the lecture this week and in preparation for the midterm, we will use the case of Harrah’s as a foundation for reviewing material we have covered since the beginning of the term.

There is no seminar in Week 7. In lieu of the seminar, I will hold extra office hours.

Topics and Readings for Midterm

Theory: chapter 1-4, the Whirlpool case study, material posted on Learn, and topics presented in lectures

Seminar: Appendices to chapters 1-4, material posted on Learn, and topics discussed in seminars.

Assignments for Week 7

The midterm has been scheduled for Friday 2016-06-17 at 18:00 to 19:30 (6 to 7:30PM). Please visit https://odyssey.uwaterloo.ca/teaching/schedule for details.

Week 6 - IT Strategy & Competing with Analytics

During week 5, we reviewed theory related to IT strategy and payoffs from IT investments. This week we will finish the discussion on IT strategy (chapter 3) and move to the discussion on competing with analytics (Chapter 4,  which is an extension of the discussion on IT strategy). We will leverage the text mining capability of R (word cloud) to get an idea about the alignment between IT and business strategy of Whirlpool and learn how to synthesize material from the first three chapters to generate risk profiles and competitive payoffs for IT investments.

In the seminar, we previewed the Whirlpool case study payoffs from their technology investment. This week we are going to look more carefully at  financial performance metrics that could best capture the expected payoffs from the firm’s IT investment. The results, especially those related to the firm’s days of inventory, are quite interesting. We will revisit the data from the wireless industry and relate this to hypercompetition.

Topics and Readings for Week 6

Theory: Re-read chapter 3 and this time focus on payoffs. While we have been using analytics from the beginning of the term, Chapter 4 provides a formal introduction to the topic, as well as historical presentation of what is perceived one of the most celebrated success stories of competing with data analytics, i.e., Harrah’s entertainment (the firm is now known as Caesars Entertainment Corporation).

Readings: Chapters 3 and 4 (no need to memorize details about Harrah’s from chapter, just make sure you get the big picture).

Seminar: We will review the script and discuss the results of the analysis of the payoffs from tech adoption in the case of Whirlpool. See script:  WHR_TechAdoptionPayoffs

Hypercompetition is a term that has been introduced since the late nineties to reflect an increasing level of competition and to account for the growing percentage of firms reporting losses. Compare the US versus Canadian side of the market (percentage of firms reporting losses in the wireless market). See script: WirelessMarket_5010_Losses

Analyzing the Whirlpool case, we explored considered one performance metric that could capture the expected payoffs from their ERP investment. If you were to repeat this exercise with Harrah’s which variable would you select and why? Feel free to consider/suggest a new variable, i.e., a variable not listed in the Appendix.

Assignments for Week 6

The online quiz will be a based on chapters 3 and 4,  and material from the seminar. The quiz will be available on Friday at 12:30 pm.

Week 5 - IT Strategy - Payoffs from Tech Adoption

IN the seminar for Week 4, we learned how to use financial data to make sense of business strategy and industry structure. These are the two main topics in Chapter 2. In addition to this, some in a couple of the quiz questions, we revisited tech adoption in conjunction with the resource based view to generate  expected duration of competitive advantage. More specifically we related this to the tech adoption in the context of the Whirlpool case. This week we are going to move to chapter 3 which deals with IT strategy. Our two primary objectives would be to understand the role that adoption of technological innovations can play on industry structure and different strategies related to IT strategy.

Topics and Readings for Week 5

Theory: We will look at IT strategy, alignment between IT and business strategy, and payoffs from IT investments. Read Chapter 3 and revisit (read carefully) the Whirlpool case

Seminar: Read the notes that I have posted on Learn on how we generate proxies for industry structure and run the accompanying script (script week 5). We use this as a foundation to perform an analysis of the payoffs from tech adoption in the case of Whirlpool.

Assignments for Week 5

The online quiz will be a based on Chapter 3  and material from the seminar. The quiz will be available on Friday at 12:30 pm.

Week 4 - Strategic Analytics (Using Accounting Data)

During our 3rd week, we considered the implications of technology adoption from a strategic standpoint (material in chapter 2) and started used analytics to make sense of topics covered in Chapter 2. We have seen how we can leverage confidence interval analysis and ROA decomposition to visualize/compare two firms following different business strategy.

Topics and Readings for Week 4

There is no lecture this week (Victoria Day).

Seminar: We will continue leveraging accounting data to understand business strategy, industry structure, and implications for competitive position. I have prepared some notes that will help you understand the logic/functions used for the ROA decomposition. The notes are available on Learn Week 4. 

We will leverage accounting data to generate proxies for industry structure and use this to establish the effect of industry structure on profitability.

Assignments for Week 4

The fourth online quiz will be a based on Chapter 2  and material from the seminar. The quiz will be available on Friday at 12:30 pm.

Week 3 - Introduction to Business Strategy

In last week’s lecture I asked the following question: If you were to identify competitors of Blackberry in the beginning of 200s, could you have included Apple and Google in the list? Chances are the answer would have been no. However, a few year later the competitive landscape had changed. In 2005 Google acquired Android, which by 2013 had become the most installed operating system (wikipedia). Apple introduced its highly successful iPhone in the summer of 2007. If you want to see the implications run the R script named (R_Blackberry.R) which is available on Learn. A more recent story is coming from Bloomberg (May 13) Apple’s $1 Billion China Deal Accentuates Ambitions for Cars. As you browse through this article try to think what it means in terms of the definition of competition and industry structure (topics covered in Chapter 2).

Topics and Readings for Week 3

Lecture: In order to understand the choices made by firms regarding technology adoption, we need to evaluate their choices in terms of their internal environment (resources and capabilities that the firm has been investing on and its strategic priorities) as well as the firm’s external environment (industry structure). These topics are covered in Chapter 2. (Read Chapter 2)

Seminar: We will review some of the theory that you have learned in your statistics class about confidence intervals and use it to generate confidence intervals for IT spending. (Review the R script posted on Learn CI_Analysis.R). We will use this as foundation to generate CI based on industry level IT spending data and industry level sales data from Compustat.  You will need to start reviewing the financial ratios (Read Appendix 2A) and ROA decomposition (Read Appendix 2B).

Assignments/Quizzes.

The online quiz will be a based on Chapter 2  and material from the seminar. The quiz will be available on Friday at 12:30 pm.

Prof. Stratopoulos

Week 2 - Patterns in IT Spending & Introduction to Business Strategy

We used the Amazon story about cloud computing to introduce the course. The objective was to show the implications of emerging technology for firms that develop/supply, as well as for firms that adopt the technology. Firms that supply the new technology hope that the technology will be adopted by a large percentage of the targeted population. We leveraged analytics and big data to explore adoption rate of cloud computing as well as the market reaction (investors and analysts implicit assumptions) regarding the expected adoption of cloud computing by the market. 

The special report on cloud computing let us see the same story from the adopting firms’ standpoint. Adoption of emerging technologies has important implications for performance and competitive position of adopting firms. Firms have to consider which technologies to invest on, when to adopt the technology, as well as monitor and evaluate the expected and realized benefits from these technology related investments. During the course we will be using the Whirlpool case study to explore the multifaceted implications of technology adoption and its competitive implications.

Topics and Readings for Week 2

Lecture: Adoption of emerging technologies implies that firms will allocate resources in their IT budget. In Chapter 1 we will explore some patterns related to IT spending over time and across industries. When firms invest in IT, they expect to achieve certain objectives. The justification, possible manifestations, and expected payoffs of IT spending are the remaining topics that we will discuss in the rest of chapter 1. (Read Chapter 1).

Firms invest in IT hoping to increase sales, reduce costs, or both. While the argument sounds reasonable, early empirical evidence showed that this expected positive association between IT spending and firm performance should not be taken for granted. Maximizing the payoffs from IT investment requires planning and the development of a vision regarding the role of IT in the organization (IT strategy) that is aligned with the firm's business strategy. We will start exploring strategic management related topics and business strategy in Chapter 2 (Read pp. 80-96)

Seminar: We will continue our practice problems related to technology adoption (Read Appendix 1B) and explore IT spending patterns (Read Appendix 1C-1D).  In exploring IT spending we will combine industry level IT spending data from IW500 with financial data from Compustat.

Assignments/Quizzes.

The second online quiz will be a based on Chapter 1  and the Whirlpool case, as well as material we cover in lecture/seminar. The quiz will be available on Friday at 12:30 pm.

All the best.

Prof. Stratopoulos

Week 1 - A Business Analytic Approach to the Value of Information Technology

Welcome to AFM241

Every week, I will provide you with a class update, which will summarize what we have done in the previous week, what we are planning to do in the following week, as well as a reminder of assignments/quizzes which are due in the following week.

Data analytics, big data, cloud computing, internet of things, and blockchain are just a few of the recent technological innovations. Over 200 emerging technologies were identified by Gartner Inc., in the last ten years (Stratopoulos 2016).  Course focus will be on the following questions:  1) Why firms invest in information technology (IT), i.e., what are the expected benefits, costs, and risks. 2) What are some of the most common information technology investments that firms undertake. 3) How do companies justify, monitor, and control IT spending, and how they evaluate the expected payoffs from these investments.

By the end the course, students should be able to achieve the following objectives: 1) Understand appropriate accounting, business and IT strategy concepts in order to explain why firms invest in IT, what investments they make, and how firms justify, monitor, and evaluate IT investments. 2) Leverage and apply appropriate structured/unstructured data and business analytics tools in order to answer questions related to why, what, and how of IT investments. 3) Integrate appropriate accounting, business and IT strategy concepts with facts based evidence from business analytics in order to make recommendations on how to justify, monitor, and evaluate IT investments.

Please start by reviewing the syllabus, which you access from the following link: Syllabus 2016. Course delivery will be based on a combination of lectures (theory) and hands on seminars (leverage business analytics to understand the theory).

Topics and Readings for Week 1

During the first week’s lecture, we will use the Whirlpool case study as a way of exploring the main questions examined in this course (i.e., why firms invest in information technology, what investments they make, and how they justify/evaluate these investments). In chapter one, we will explore emerging technologies and technology adoption (sections 1.3 and 1.4, pp. 12-27).

During the first week’s seminar, we will start working with R (a very powerful and versatile open source software) on business analytics problems. We will explore the RStudio interface and work with stock market data (Appendix 1.A, pp. 39-50), as well as the use of R and Google Trends to understand and predict emerging technology adoption (Appendix 1.B, pp. 51-56).

Please read the Whirlpool case and assigned pages from text before you come to class.

1. Stratopoulos, T. C. 2016. Business Value of Information Technology: A Business Analytics Approach. University of Waterloo, ON. The text is available from the University of Waterloo bookstore. Please note that that the price charged by the bookstore is just the printing cost.
2. Ruback, R. S., Balachandran, S., and Aldo Sesia 2001. Whirlpool Europe. Case Study, Boston: Harvard Business School. You can purchase/download the case from the following URL: http://hbr.org/product/whirlpool-europe/an/202017-PDF-ENG?Ntt=%2520whirlpool%2520europe

Assignments/Quizzes for week 1

There is going to be a quiz on Friday based on the Whirlpool case, assigned pages from the text, and material covered in the lecture/seminar. The quiz is open books,  you will take it online (via Learn), and you will have to take it during a specific time (12:30 pm).

Again welcome to AFM241 and best wishes for a healthy and productive term.

Prof. Theo Stratopoulos