University of Alberta School of Business Introduction to Business Analytics Winter 2021 Department of ABA MGTSC645/488 (LEC X50) Tuesdays 18:00-21:00 in BUS 3-5
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COURSE SYLLABUS
Course Description_______________________________________________________
The merging of massive data-sets with analytical tools from Statistics, Computer Science, Operations Research, Management Science, and Industrial Engineering has created the emerging field of Analytics. Methods are developing rapidly and are encoded on statistical platforms such as SAS and R, or more general-purpose programming tools such as Python. This course will build on the basis from MGTSC 501 to provide an overview of business analytics, and develop programming and methodological skills to acquire, analyze, and present analysis.
Learning Goals_______________________________________________________
Acquire an overview of analytics in general, especially as applied to business, as well as the business of analytics
o Analytics versus Data Science o Statistics versus Computer Science
Learn about some of the challenges and stages of analytics consulting, and the role of business majors and MBAs in the business analytics eco-system
Learn about programming languages used in analytics, well enough to edit code to get results based on data (writing code from scratch is not expected)
o SAS, R, and Python are the key languages
Gain (some) experience in data wrangling, and understand some of the problems in data, as well as some of the weaknesses of standard solutions
Gain a foundation in the methods in analytics so that independent further development is possible
Provide a foundation for the CAP or the SAS endorsements
Materials_______________________________________________________________
Required textbooks:
Provost & Fawcett (Updated 2019) “Data Science for Business: What you need to know about data mining and data analytic thinking”. O’Reilly. http://data- science-for-biz.com/
James, Witten, Hastie & Tibshirani (2013) “An Introduction to Statistical Learning with Applications in R”, Springer. Free at:
https://link.springer.com/content/pdf/10.1007/978-1-4614-7138-7.pdf
Additional textbooks for reference (i.e. not required):
Cochran, J.J. (ed) (2018) “INFORMS Analytics Body of Knowledge”, Wiley.
Hastie, Tibshirani, Friedman (2001) “The Elements of Statistical Learning”, Springer.
Raschka & Mirjalili (2019) “Python Machine Learning: Machine Learning and Deep Learning with Python, scikitlearn, and TensorFlow”, 3rd Edition, Packt Publishing.
VanderPlas (2016), “Python Data Science Handbook”, O’Reilly
Wickham & Grolemund (2016), “R for Data Science”, O’Reilly
Witten, Frank, Hall, Pal (2017), “Data Mining – Practical Machine Learning Tools and Techniques”, 4 ed., Elsevier
eClass will have links to videos, Zoom, and all other materials (some via Google Drive).
This syllabus is subject to revision and will be updated to contain key information.
Information contained in the syllabus is considered “received” 24 hours after updating in eClass. Please check this syllabus for answers to frequently asked questions (FAQs).
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Course Grade___________________________________________________________
Your grade will be determined as a weighted average of the following. The overall score may be scaled.
Class Participation 10 %
Homework 30 %
Project 30 %
Final Exam 30 %
Any missed work for cause will have the weight added to the final exam.
Attendance Policy _________________________________________________________
Attendance is not required, but Class Participation depends on it. On Zoom, video is strongly encouraged for class participation. Videos of Zoom content will be provided to the extent possible, but these are not to be downloaded. Students are responsible for material covered in class, regardless of attendance.
Contact Information (online only for Winter 2021):____________________________
Email: [email protected]
Office hours; Thursdays 1100-1200 and by appointment
Teaching Assistant:
Jarrod Crone [email protected]
Assignments___________________________________________________________________
Homework and projects may be done in groups. Expect one assignment or project due for each synchronous session.
Honor Code___________________________________________________________________
You are responsible for following the University of Alberta honor code explicitly and in spirit.
At the beginning of a course, Instructors will discuss with their class the expectations with respect to academic integrity and outline both permitted and prohibited behaviour.
Tentative Schedule – details available on eClass. Expected synchronous sessions (Zoom):
Date Topics
12-Jan Introduction. Review. Data Science, Analytics, and Statistics.
2-Feb Linear & logistic regression. Neural networks. Analytics Management Consulting 2-Mar Visual analytics. Reinforcement learning. Decision trees. Model performance metrics.
23-Mar Decision analysis, Bayesian methods, Support Vector Machines. Unsupervised Learning.
13-Apr Strategy and Data Science, ensembles, latent variables. Text analytics. Projects and Q&A
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Mandatory University of Alberta Policy Statements______________________________
Policy about course outlines can be found in Course Requirements, Evaluation Procedures and Grading of the University Calendar.
The University of Alberta is committed to the highest standards of academic integrity and honesty.
Students are expected to be familiar with these standards regarding academic honesty and to uphold the policies of the University in this respect. Students are particularly urged to familiarize themselves with the provisions of the Code of Student Behaviour (online at www.governance.ualberta.ca) and avoid any behaviour which could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation in an offence. Academic dishonesty is a serious offence and can result in suspension or expulsion from the University.
Audio or video recording, digital or otherwise, of lectures, labs, seminars or any other teaching
environment by students is allowed only with the prior written consent of the instructor or as a part of an approved accommodation plan. Student or instructor content, digital or otherwise, created and/or used within the context of the course is to be used solely for personal study, and is not to be used or distributed for any other purpose without prior written consent from the content author(s)
Selected items from the Code of Student Behavior, found at:
https://cloudfront.ualberta.ca/-/media/universitygovernance/documents/resources/policies- standards-and-codes-of-conduct/cosb-updated-july-1-2018.pdf
30.3.2(1) Plagiarism
No Student shall submit the words, ideas, images or data of another person as the Student’s own in any academic writing, essay, thesis, project, assignment,
presentation or poster in a course or program of study.
30.3.6(4) Misrepresentation of Facts
No Student shall misrepresent pertinent facts to any member of the University community for the purpose of obtaining academic or other advantage. This includes such acts as the failure to provide pertinent information on an application for admission or the altering of an educational document/transcript. (EXEC 04 MAY 2009)
30.3.6(5) Participation in an Offence
No Student shall counsel or encourage or knowingly aid or assist, directly or indirectly, another person in the commission of any offence under this Code.