Course Instructor: Name: Email: Office Hours:.com By appointment only School of Accounting and Finance AFM 244:

 Analytic Methods for Business 3 Spring 2022 Course Syllabus Zoom class meeting information: 001 002 003 004 https://us06web.zoom.us/j/9726420131?pwd=ejVpTHdOWnc0eWk3WnBaeStIME9JZz09 Section number Lecture time Mondays and Wednesdays 4:00PM – 5:20PM Mondays and Wednesdays 5:30PM – 6:50PM Mondays and Wednesdays 7:00PM – 8:20PM Mondays and Wednesdays 8:30PM – 9:50PM Meeting ID: 972 642 0131 Passcode: AFM244 One tap mobile +13017158592,,9726420131#,,,,*430500# US (Washington DC) +13126266799,,9726420131#,,,,*430500# US (Chicago) Dial by your location +1 301 715 8592 US (Washington DC) +1 312 626 6799 US (Chicago) +1 346 248 7799 US (Houston) +1 669 900 6833 US (San Jose) +1 253 215 8782 US (Tacoma) Meeting ID: 972 642 0131 Passcode: 430500 Find your local number: +1 929 205 6099 US (New York) https://us06web.zoom.us/u/kcc3C1iU6R Course Description: Why Accounting Analytics Matter: Data analytics is the discovery of patterns/knowledge from data. However, accounting students are not here just to learn about data analytics, they are here to learn data analytics in order to make better accounting and business decisions. Hence, the intent of this course is to provide an intuitive and practical introduction to data analytics tools/concepts using problems/applications in financial and managerial accounting, auditing, taxation, and accounting information systems. The primary tool used will be R, Excel and Tableau. Applications of data analytics in accounting include topics such as: ● Financial Accounting: Compare competing strategies (product differentiation and cost leadership) through ROA decomposition, establish a company’s relative position (competitive advantage, parity, disadvantage) versus its peers. ● Managerial Accounting: Understand how we translate data into the information needed to monitor the performance of a business. For example, work with a retail business to analyze their sales and develop an interactive business dashboard. ● Auditing: Audit client records to identify fraud and assess inventory valuation. ● Taxation: Analyze client data for compliance with IRS rules. ● Accounting Information Systems: Evaluate payoffs from technology investments. Understand emerging technologies (e.g., cloud computing, blockchain) and predict rate of adoption. BAFM Program Level Learning Outcomes Course Learning Outcomes Programs delivered by the School of Accounting and Finance (SAF) are designed to provide students with the competencies, professionalism and practical experience that they need to excel in their chosen careers. By the end of the course, the students should be able to achieve the following objectives: 1. Business Understanding: Identify business applications where we can use data analytics concepts and tools to answer questions and solve problems. 2. Data Understanding and Preparation: Identify sources of data, collect and extract data, get familiar with data structure, identify quality issues, clean and transform data for analysis. Each of the School of Accounting and Finance’s Program Level learning outcomes identifies a knowledge, skill or value of a financial professional. These outcomes are organized into seven areas as reflected in the graphic. The puzzle pieces reflect the integration of all areas. All outcomes are developed through experiential learning. 3. Modeling:Explaindataminingclassificationand/orpredictionmodelsinplainEnglish,using simple examples and tools. 4. Evaluation: Leverage mathematical (i.e., test statistics) and logical techniques to evaluate how valuable a model is, what it has found, and what you may want to do with the results. 5. Deployment: Communicate your results and use the new insight to answer questions and solve problems. This course will pursue these objectives by discussing the basic theory of data analytics and implement data analytics using R in a business context, using real-world data sets (in the measure possible) and with a view of developing professional skills. This course’s learning outcomes map to the Program Level learning outcomes as follows: 1.Business X X X understanding Intended Learning Outcomes By the end of the course you will be able to: Knowledge Base for a Financial Professional Communicati on Capabilities Problem- Solving Capabilities Fluency in the Languages of Business, Entrepreneurs hip and Technology Ethical Conduct and Social Responsibility Leadership and Collaboratio n Attributes / Qualities of a Financial Professional 2. Data understanding & preparation 3. Modelling 4. Evaluation 5. Deployment X X X X X X X X X X X X X Learning Activities XX X Intended Learning Outcomes 1. Business Understanding 2. Data Understanding and Preparation 3. Modeling 4. Evaluation 5. Deployment Business cases, discussion and presentations Programming exercises Programming exercises Programming exercises Mining. Waterloo, ON. Other Materials: 1. R–aprogramminglanguagethatisplatformagnosticandfreetoacquire–installation instructions to be given 2. ExcelforMicrosoft365,availabletoallstudentsviaUW’sOffice365subscription Business cases, discussion and presentations ● Textbook – Stratopoulos, T. (2022). Analytic Methods for Business: Foundation of Data Course Resources: Course Evaluation: Assessment Method Date Percentage Weekly Quizzes – listed Fridays at 2:00PM EST – 2:30PM EST 1. May13 2. May20 3. May27 4. June3 5. June10 6. June17 9. July15 7. June24 10.July22 8. July8 10% Class Participation – Top Hat Throughout term Due by 11:59PM EST on the day the lecture takes place Monday 11:59PM and Wednesday 11:59 PM 15% Midterm exam – Open book/note Stage 1 (synchronous) - Friday June 17 10:00AM – 11:00AM Stage 2 (async) – Friday June 17 12:00PM – 8:00PM Stage 3 (async) – Friday June 17 9:00PM – Saturday June 18 5:00PM Times subject to change 20% Final exam – Open book/open note Stage 1 (synchronous) - Friday July 22 10:00AM – 12:00PM Stage 2 (async) – Friday July 22 1:00PM – 8:00PM Stage 3 (async) – Friday July 22 9:00PM – Saturday July 23 5:00PM Times subject to change 25% Group project Class Participation Friday July 29, 11:59PM EST 30% 100% Active participation leads to higher retention and understanding. Students should review assigned material before they come to class. Participation includes answering questions, making comments, and asking questions that help students understand the material, as well as working individually and in teams on class assignments/presentations. Participation questions will be available on Top Hat until 11:59PM the day of the lecture. To facilitate class participation and class interaction in lectures, we will use Top Hat. The most important component in these exercises is the opportunity to participate and if necessary, discuss the question within your team. At least 50% of the class attendance/participation grade will be assigned for submitting an answer; the remaining 50% will be assigned for submitting a correct answer. ● There are no make-ups for attendance and participation missed. ● Impersonation, including the use of someone else’s Top Hat account carries a penalty of zero in class participation and will be reported as a violation of academic integrity Weekly Quizzes Weekly online quiz on topics and concepts covered in class. The quiz questions may be multiple choice, true/ false, or numeric (based on completion of R script analysis of assigned data sets) questions. Team Assignment This team project is designed to help you develop your analytics mindset. By way of a reminder, an analytics mindset is the ability to: 1) Ask the right questions. 2) Extract, transform and load relevant data. 3) Apply appropriate data analytics techniques. 4) Interpret and share the results with stakeholders. More specifically, I will provide you with an accounting analytics case and you will have to work on this with your team. The project has two deliverables: the data analytics component, and the communication component. You will have to use R to complete the data analytics part and create a presentation. Details to be given during the semester. Students are allowed to make their own groups of 5, but students who have not by May 30, will be auto-enrolled into a group. Team Peer Evaluations At the end of the course you will be asked to complete a summative peer evaluation of each of the members of your team along with a self-evaluation. These will impact your team assignment grade. Significant group issues that cannot be remedied by the peer evaluation should be raised to be resolved as early as possible. Case-Based Exams There are two case-based exams (midterm and final). Both exams may include material from the text, assigned additional readings, assignments, and lectures Both exams are cumulative, open book and open note. Further details on each exam will be provided ahead of the scheduled exam date. Both examinations must be the exclusive work of the individual student. Late Submission Policy All submissions that are any time after the required submission date, that have not been granted an exception based on extenuating circumstances – with documentation, will be considered late. Late submissions are reduced by a nominal 10% of the mark. For example, consider a submission due June 17, 2022 11:59PM, deserving of 80%. If submitted June 18, 2022 12:00AM would receive a mark of 70%. Every subsequent day will be an additional 10% deduction. Submission Times Please be aware that the University of Waterloo is located in the Eastern Time Zone (GMT or UTC- 5 during standard time and UTC-4 during daylight saving time) and, as such, the time for your activities and/or assignments are due is based on this zone. If you are outside of the Eastern Time Zone and require assistance converting your time, please try the Ontario, Canada Time Converter. Re-grade Requests In order to receive a timely response to a re-grade request, written requests for (examinations, assignments etc.) should be made within one week after the examination/assignment return day. For all re-grade requests, a written re-grade request must be submitted to the course instructor indicating the reasons for believing that the assessment was improperly graded. The instructor reserves the right to re-grade the entire assessment; as a result, marks may increase, decrease or remain the same, upon re-grade. Policy 70 dictates the challenge process. Turnitin Policy Turnitin.com and alternatives: Text matching software (Turnitin) may be used to screen assignments in this course. Turnitin is used to verify that all materials and sources in assignments are documented. Students' submissions are stored on a U.S. server, therefore students must be given an alternative (e.g., scaffolded assignment or annotated bibliography), if they are concerned about their privacy and/or security. Students will be given due notice, in the first week of the term and/or at the time assignment details are provided, about arrangements and alternatives for the use of Turnitin in this course. It is the responsibility of the student to notify the instructor if they, in the first week of term or at the time assignment details are provided, wish to submit the alternate assignment. Course Schedule – Tentative Week # 1 2 3 4 5 6 7 8 Date May 2, 4 May 9, 11 May 16, 18 May 25, 30 June 1, 6 June 8, 13 June 17 June –15, 20 June –22, 27 Topic Introduction Applying stages of the CRISP-DM process Applying stages of the CRISP-DM process Database theory Audit Data analytics Textbook Chapter Chapter 1 - Introduction Chapter 2 - Integrated Case: Industry Analysis Chapter 3 - Integrated Case: Telco Customer Analysis Chapter 4 – Integrated Case: Bibitor Internal Audit Chapter 5 – Integrated case: Bibitor Inventory Midterm review Midterm Tax analytics Regression analysis Chapter 6 – Tax analytics Chapter 7 – H&S forecast revenues 1 9 10 11 12 June 29, July 4 July 6, 11 July –13, 18 July –20, 25 Dummy variables Chapter 8 – H&S forecast revenues 2 Data visualization Guest Lecturer: Greg Berberich Classes will be held in person in rooms to be announced Final review + Final University of Waterloo and School of Accounting & Finance Policies: Details regarding School of Accounting and Finance (SAF) policies and University of Waterloo policies can be found on the SAF LEARN site “My SAF Community” at: My SAF Community Policy document- accessible for Learn - updated April 2022 - My SAF Community (uwaterloo.ca) within the Learn – SAF Course Syllabus – Policies for Students folder. These policies are an integral part of this course syllabus. They have been posted on the SAF LEARN site as they are not course specific but are common for all SAF program courses. Please ensure that each term you are informed regarding these policies. They include: School of Accounting and Finance Policies: • Accommodations for missed assessments • SAF Process for Requesting Accommodation for Missed Assessments • Recording of Lectures • Textbooks and Intellectual Property Rights • Attendance at the Registered Section University of Waterloo Policies: • Academic Integrity • Grievance • Discipline • Appeals • Academic Offenses and Implications • Accommodation for Students with Disabilities • I-clickers • Mental Health Support • Territorial Acknowledgement • Chosen/Preferred First Name