University of Alberta School of Business Introduction to Business Analytics Winter 2020 Department of AOIS 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 ‘Big Data’ and analytics, and develop programming and methodological skills to acquire, analyze, and present analysis.
Learning Goals_______________________________________________________
Learn about Analytics in general, especially in the context of business
oThe business of analytics
o
The analytics of business
Learn the stages of analytics consulting
Learn at least one programming language well enough to produce results based on data
oSAS, R, and Python are the key languages
Gain (some) experience in data wrangling
Learn enough about tools and methods in analytics that independent further development is possible
Provide a foundation for the CAP or the SAS endorsements
Materials_______________________________________________________________
Textbook: None is required, but the following have useful information. We will use publicly available literature as needed (specified on eClass).
Hastie, Tibshirani, Friedman (2001) The Elements of Statistical Learning, Springer.
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
My teaching calendar is meant to collect all due dates, office hours, & deliverables.
https://calendar.google.com/calendar?cid=dWFsYmVydGEuY2FfYjhxNmltdTFtZmc4aDJobDd0 aGhrYnIyNnNAZ3JvdXAuY2FsZW5kYXIuZ29vZ2xlLmNvbQ
eClass will have links and other downloads. Examples of evaluative materials will be made available there, provided any exists.
DataCamp link (free, but you must use your ualberta id to register)
This syllabus is subject to revision and will be updated to contain key information (such
as final exam time and place). Any information contained in the syllabus is of necessity
considered “received”. 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 10 %
Midterm exam 20 %
Project 20 %
Final Exam 40 %
Any missed work for cause will have the weight added to the final exam. The final exam may be take- home.
Attendance Policy _________________________________________________________
Attendance is not required, but Class Participation depends on it. Students are always responsible for material covered in class.
Contact Information: ______________________________________________
Office: Business Building 2-43
Email: [email protected]
Phone: 780-248-1262
Office hours; Tuesdays & Thursdays 1100-1230
Assignments___________________________________________________________________
Homework and projects may be done in groups. The projects will be presented in class.
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_____________________________________________________________
Day Date Details
1 7-Jan Introduction, definitions, scope, expectations; software & resources; optimization 2 14-Jan Data wrangling and software tools
3 21-Jan Regression models for estimation and classification: linear, logistic, to neural network models 4 28-Jan Regression models for estimation and classification: linear, logistic, to neural network models 5 4-Feb Regression models for estimation and classification: linear, logistic, to neural network models
6 11-Feb Midterm
7 18-Feb Reading Week
8 25-Feb ML/AI - Unsupervised learning - clustering 9 3-Mar ML/AI - Reinforcement learning
10 10-Mar ML/AI - Other (non-regression) forms of supervised learning 11 17-Mar Text analytics
12 24-Mar Text analytics
13 31-Mar Project presentations 14 7-Apr Scheduled Final Exam
There will be guest speakers, who will address select areas that they are experts in.
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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.