MB8002-Winter2018-outline

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MB8002 Winter 2018 Page 1 of 9 MB8002

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UANTITATIVE

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ETHODS FOR

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USINESS

https://sites.google.com/site/stats4mba/

Contact Information

Professor: Murtaza Haider Office: TRS 1-083 (7th_floor)

Office Hours: 2:00 – 5:00 pm, Wednesdays E-mail: [email protected] Phone: 416-979-5000, ext. 2480

Method of Posting Marks

All marks will be posted on D2L.

E-mail Usage & Limits

Please use only the D2L or Ryerson Email address for communication with the professor. Mention “MB8002” in the subject of your emails.

Class Times and Location

Wednesdays 6:30 pm – 9:30 pm TRS 2-006

Course Description

This course equips students with basic analytical tools that support business decision making. Students learn the principles of statistics and other techniques and apply them to data analysis using computer-based tools. In addition, students develop a broader understanding of the information systems that supply these data, and how quantitative analyses support management and strategy in business organizations.

Course Objectives

The use of Big Data and Analytics has increased manifold in the recent past. Managers increasingly rely on data and synthesized information to make decisions. This course introduces graduate students to the fundamentals of research methods used to extract insights from data. The course introduces analytics/data mining techniques used in

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MB8002 Winter 2018 Page 2 of 9

R and its extensions are freeware and hence allow students to graduate with skills they can readily apply in their new roles as analysts and managers.

The specific objectives of this course are as follows:

1. Introduce students to the brave new world of data with a focus on open data 2. Introduce students to quantitative analysis and research methods

3. Expose students to advanced data mining methods in a hands-on environment, such as regression and classification methods

4. Turn analytics into deliverables with a focus on storytelling 5. Review applications of data mining methods used by businesses

Method of Evaluation

Your final grade is based upon your performance in the following course components:

COMPONENT WEIGHT DUE

Hands-on lab assignments (9 in total) 27% Weekly

Research project 23% Feb 28

Mid-term Exam 25% March 14

Term Paper 25% April 13

Any change to this evaluation scheme will be discussed in class prior to implementation.

WEEKLY LAB ASSIGNMENTS

The 09 brief work assignments will be assigned each week and will be due at the break time during the next lecture. Students must submit the assignments in-person. Emailed

assignments will not be accepted, nor any assignment submitted by anyone other than the student.

Each of the 9 weekly assignments on average will be worth 3% of the total grade in the course. The assignments will be graded with a pass-fail scheme. If the assignment is sufficiently complete, the student will be awarded a pass (3% of the grade). If the

assignment does not meet the minimum standards, the student will receive a fail (0%) for the weekly assignment. The instructor will permit resubmissions and revisions in

exceptional cases.

RESEARCH PROJECT

You are expected to prepare a short report by analyzing a dataset. The short report must be prepared as a consultant’s deliverable, where you will act as the consultant and the

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MB8002 Winter 2018 Page 3 of 9 The decisions regarding the length of the report, types of graphs and tables, and the use of analytics will be at your discretion. You will make these decisions based on the work order (assignment), which will identify research questions, duration of the contract (deadline for the assignment) and an assumed budget. You will use these details to determine how much effort is required to complete the report where you meet the client’s needs and still profit from the contract.

IN-CLASS TEST

Since this course has a heavy focus on analytics and data mining, an in-class test will be administered to determine students’ competency in data analysis. Students will have the choice to complete the test in class or in a computer lab. They will be presented with a data set and will be asked to analyze data and summarize their findings. The test duration will not exceed 150 minutes.

TERM PAPER

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Tentative Class Schedule

Week Topic Notes

Jan 17 1. Big data, data science, business analytics, and story telling

2. Intro to R/RStudio/R Commander

GSDS/Ch. 1

Jan 24 1. Data in a 24/7 connected world

2. Descriptive Statistics GSDS/Ch. 2 and 4 Weekly 1

Jan 31 Summarising data: 1. Tabulations 2. Data visualization

GSDS/ Ch. 4 & 5 Weekly 2 Feb 7 Hypothesis Testing: From T-test to Regression GSDS/Ch.6, 7

Weekly 3 Feb 14 Regression in Action

1. Margins & Counterfactuals Weekly 4 Feb 21 Family Day and Study Week

Feb 28 Impact Evaluation - 1

1. Difference-in-Difference models Handbook/ Ch. 2 & 5, 14 GSDS/ Ch. 11 First Project due Mar 7 Integrative Week

Mar 14 Mid-term Examination Weekly 5

Mar 21 Impact Evaluation – 2

1. Time Series in Business Analytics 2. Interrupted Time Series Analysis

GSDS/Ch. 11 Weekly 6

Mar 28 Choice Metrics: To Be or Not to Be

- Binary Logit and Probit Models GSDS/Ch.8 Weekly 7 Apr 4 Many more choices

- Multinomial Logit models GSDS/Ch.9 Weekly 8 Apr 11 Data Mining:

- Regression Trees, Clustering, Neural Networks GSDS/Ch.12 Weekly 9

April 13 Term Paper Due

For exam and other significant dates visit:

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Teaching Method

This course will incorporate the following teaching/learning methods:

• Lectures by the instructor where key concepts will be introduced.

• Hands-on training in data analysis

• Animated discussion by students during the class on assigned readings

• Lectures by guest speakers from academia, government, and the industry

• Interactive solutions for empirical assignments

Class time will be devoted to topics appropriate to the reading assignments for each lecture. The reading assignments and classroom discussion of problems and cases are the core of students’ learning experience. Assigned readings should be reviewed PRIOR to each class.

Textbook

Required:

Getting Started with Data Science: Making Sense of Data with Analytics by Murtaza

Haider. IBM Press and Pearson. 2016.

Supplementary Text: Available online from the World Bank

Handbook on Impact Evaluation-Quantitative Methods and Practices by Khandker

Shahidur R. and Gayatri B. Koolwal.

https://openknowledge.worldbank.org/handle/10986/2693

Reference Format

APA or MLA.

Course Assessment

Note: Satisfactory performance in a Master’s program requires completion of all courses taken for credit in the graduate program with a grade of at least B- in each course. Any grade below B – will be deemed Unsatisfactory and graded as an F.

MASTER’S GRADING SYSTEM

Letter

Grade Conversion Range Percentage Scale to Letter Grades

A+ 90-100

A 85-89

A- 80-84

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B 73-76

B- 70-72

F 0-69 (Master’s Unsatisfactory Performance Level)

F 0-72 (PhD Unsatisfactory Performance Level)

Final academic performance in each course is recorded as one of the above letter grades or as AEG, AUD, CNC, CRT, FNA, GNR, INC, INP or PSD.

INCOMPLETE GRADE (INC)

The assignment of an incomplete grade normally follows discussion between the faculty member and student concerning the work to be completed and the relevant due date(s). To confirm arrangements that are already mutually understood or to provide the necessary information where prior consultation has not taken place, when an Incomplete Grade is assigned the faculty member(s) will complete a prescribed Incomplete Grade Form that specifies work to be completed and due dates. Copies of this completed form will be provided to the graduate program department, which will in turn provide a copy to the student. The due date set by the faculty member will be no later than the end of the following semester, but will normally be earlier than this.

PASSED (PSD)

The assignment of a PSD grade is given for acceptable performance in a course graded only Pass/Fail, as predefined in the Calendar.

Highlights Of Course Management Policies

Consult the School of Graduate Studies Policies and Procedures Handbook for a complete description of course management policies.

http://www.ryerson.ca/content/dam/senate/policies/pol151.pdf

Code of Conduct: Students are expected to adhere to the Ryerson University Student Code of Academic Conduct available at:

http://www.ryerson.ca/academicintegrity/Students/Graduate/index.html

1) Alternate Arrangements: Please consult the School of Graduate Studies Policies and Procedures Handbook Section I “Academic Consideration” for a complete description of alternate arrangements

http://ryerson.ca/content/dam/senate/policies/pol151.pdf

a. IB1. Accommodations for Missed Examination and/or Assignment: Religious Observance: Students must have filed the necessary forms for

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MB8002 Winter 2018 Page 7 of 9 Accommodation of Student Religious Observance Obligations and related form.) The form can be obtained at

http://www.ryerson.ca/content/dam/senate/policies/pol150.pdf

b. IB2. Accommodations for Disability: Students who wish to utilize Academic Accommodations Support (AAS) accommodations must activate the sending of an accommodation letter via the online system used by AAS to each of their instructors outlining their approved accommodation(s) for each course. This should be done as early as possible, ideally prior to a graded assignment, test or exam, according to AAS policies and procedures.

c. IB3. Accommodations for Missed Examination and/or Assignment: Medical or Compassionate: Students shall inform professors, in advance, when they will be missing an exam, test or assignment deadline for medical or

compassionate reasons. When circumstances do not permit this, the student must inform the professor as soon as reasonably possible. Alternate

arrangements may include the setting of a make-up test, transferring the weight of a missed assignment to the final examination or extending a deadline. In addition, the program requires that in the case of illness, a Ryerson Medical Certificate, or a letter on letterhead from a physician with the student declaration portion of the Ryerson Medical Certificate attached, be submitted to Professor and the Program Administrator within 3 days of the missed test or exam. This certificate is essential for an appeal based on Medical grounds. The Ryerson Medical Certificate can be found at

http://www.ryerson.ca/surp/documents/medical.pdf

2) Exams and Tests:

a. Exam Policies: Students are expected to familiarize themselves with the University Examination policy available

at:http://www.ryerson.ca/senate/policies/pol135.pdf (Policy 135)

b. Due Dates: Students are responsible for writing tests/exams on scheduled dates and submitting assignments on the due dates. If any form of

evaluation is missed due to medical or compassionate reasons, a student must inform the Program Administrator via email ([email protected]) and the professor as soon as possible and/or within 12 hours of the missed event. Appropriate documentation must be submitted upon return to avoid a zero grade for the assignment/test/exam. Students who are too ill to participate in an evaluative component of a course (e.g. make a presentation, sit a

text/exam) must present the Ryerson Medical Certificate to the Professor and the Program Administrator within 3 days of the missed test or exam.

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MB8002 Winter 2018 Page 8 of 9 exception to this policy is stated on the course outline. Seventy percent (70%) is considered a passing grade.

d. Identification during exams: As per Ryerson University policy, students are required to ensure that photo identification is available during an exam. Backpacks, bags and coats cannot be at your desk during exams.

e. Exam scheduling: In accordance with Ryerson’s Course Management policy, no exam or tests will be scheduled during the last week of classes. This applies to all courses in your program.

f. Writing exams: Students will be informed prior to any in-class evaluation of the material they are permitted to bring into the exam.

3) Assignments:

a. Due dates: Students are responsible for ensuring that assignments are submitted to the professor on or before the designated due date. The penalty for late submission of an assignment is a loss of 1 mark per day for every day late (including weekend days). For example, if an assignment is marked out of a total of 20 marks and is submitted 5 days after the due date, the

maximum possible mark achievable for that assignment will be 15 marks. b. Copies: Students are required to keep a complete copy of each assignment, as

submitted, for their records. In the unlikely event that an assignment is lost after submission to the professor, the student will be asked to submit the copy to the professor.

4) Plagiarism is a serious offence. Plagiarism and other forms of academic misconduct will be penalized as per Ryerson’s Code of Student Conduct, available at

http://www.ryerson.ca/content/dam/academicintegrity/documents/Graduate%20Student s%20and%20Policy%2060.pdf

The minimum penalty for plagiarism is a mark of zero for the work submitted. A

disciplinary notice (DN) will be placed on your official transcript until you graduate, or for four years, whichever comes first. If you are uncertain as to what constitutes

plagiarism, please consult the Graduate Studies website at www.ryerson.ca/graduate. Click on Policies and download the “Intellectual Property Guidelines”. It is your responsibility to familiarize yourself with this document.

5) Merit of Work and Recalculation: At any time during the semester, students who believe that an assignment, test or exam, either in whole or part, has not been

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MB8002 Winter 2018 Page 9 of 9 6) Student E-Mail Accounts: All students are required to obtain and maintain a Ryerson

Matrix e-mail account. If students fail to maintain a Ryerson email account, any missed messages will be solely the responsibility of the student. Please visit http://www.ryerson.ca/accounts to activate your matrix account.