Hello, dear friend, you can consult us at any time if you have any questions, add WeChat: daixieit

IRE379H1: Research and Analytics for Industrial Relations and Human Resources

Winter 2024

LEC0101

Course description

Data science is changing the way organizations make decisions. This course introduces a data analytics perspective on industrial relations and human resources, including the measurement of performance metrics, analysis of organizational policies, and data visualization. Students will develop basic data skills in the R statistical computing environment.

Prerequisite

Prerequisite:     IRE240H1/IRE244H1/IRE260H1

Exclusion:         WDW379H1

Course materials

All required readings are available for free from Quercus. There is no textbook.

The University of Toronto library offers several introductory statistics textbooks for free online. This textbook may be helpful:

(Useful for reference) Pearson, R. (2010). Statistical persuasion: How to collect, analyze, and present

data…accurately, honestly, and persuasively. SAGE Publications.

URL:https://dx-doi-org.myaccess.library.utoronto.ca/10.4135/9781452230122

Technology requirements

.   Recommended Technology Requirements for Remote/Online Learning

.    Rand RStudio statistical computing software.  Details below the course calendar.

Evaluation

Item

Weight

Date

Homework assignments (8)

20%

Due Tuesdays by 11:59 PM

Participation

10%

Every class (weeks 1-5 & 7-11)

Midterm

20%

Feb 28th  in class

Final project  presentation

10%

Presentations on Zoom (Week 12)

Final project – written report

40%

Due Thursday April 5, 11:59 PM

Time zone for online submissions: Eastern Standard Time.

March 11 is the last day to cancel an S term course without academic penalty.

Assignments

Homework assignments (8)

Statistical analysis is like a foreign language: frequent practice is the best way to learn. Weekly homework assignments are therefore a major part of this course.

Weekly assignments are due Monday nights by 11:59 PM. See class schedule below for details. Submit your assignments online using the Quercus course website.

In each problem set, students must make an individual effort at each problem before consulting  any classmates. Consulting with other students is encouraged – please do learn from each other – but you must always write-up and submit your own solutions. You are required to write the

names of any student that you consulted on your assignments.

Midterm

In addition to the weekly assignments, you will have a midterm that focuses on the conceptual and mathematical knowledge of the course. An in-class review session will summarize these    concepts prior to the midterm.

Final Project

You will form groups of maximum 4 students to develop a final project. As a group, you will choose a topic, define a research question, select an appropriate source of data to answer your question,  and use the skills obtained in this class to generate the best answer possible, given the available   data.

You may use any source of data that you can obtain. However, you need to explain how the data were collected. “A dataset of employees from Kaggle” leaves readers with many unanswered

questions: Which employees? How was data collected from them? How were the employees selected for data collection?

You will submit two deliverables related to this project:

1.       Final project  presentation (on zoom during final week)

Your team will give a presentation on your project in the final week of class.


Evaluation of the presentation is based on clearly communicating: a research question, the data obtained to answer it, the statistical techniques employed, and what the team learned from this  research. Data visualizations are encouraged. Thoughtful responses to questions from the class will also be evaluated.

2.       Final project - Written report

Your team will submit a written report based on the data analysis project.

Evaluation of the written report will be based on clarity and meaningfulness of the research topic, selection of appropriate data, use of statistical methods, clear discussion of strengths and weaknesses of your approach, and informative presentation of the results, including visualizations.

Working as a Team:

Learning to work as a team and to be a productive team member is a critical skill for professional success. You are automatically placed in a team (department or division) when you join an organization. The course simulates this experience so you can develop the necessary teamwork skills for your future   career success. You are reminded of the following expectations with respect to your behavior and contributions to the team project.

Each team member is expected to:

.     Treat other members with courtesy and respect;

.      Establish a positive and productive team dynamic;

.     Contribute substantially and proportionally to the final project;

.       Ensure enough familiarity with the entire content of the group project so as to be able to sign off on it as original work;

.     Meet the project timeline as established by the team.

Project work is often new to students; conflicts can and do occur from time to time. Teams are responsible for their internal management and are collectively expected to resolve disputes or misunderstandings as soon as they arise (and prior to submission of the final project). In cases where teams are unable to reach a mutually agreeable solution, the entire team must meet with the professor/TA as soon as possible. (Do not wait till it’s too late!) The professor/TA will listen to the team and help the team develop options for improving the team process. All members of the project team must commit to and use their action plans. No student is allowed to turn in a group work as an individual.

In cases where it is clear that an individual has made little or no contribution to the groupwork, the instructor reserves the right to adjust that individual's mark on the team part of the project grade to a mark less than that given to the group as a whole. For example, in the situation in which no contribution has been made, a mark of zero will be given. In some cases, the team in question may be asked to complete a peer evaluation of their team members. However, the assessment and the assigning of marks are the responsibility of the instructor alone.

Office Hours

General Office Hours: please email me to schedule an appointment for a zoom meeting. This appointment is 1 on 1 on a first come first served basis. Please try to give ample time (at least 24 to 48 hours) to schedule meetings where possible. Please note: due to increased demand for office hours during assignment and exam windows, office hour accommodation is subject to availability.

Course Software: R and RStudio


'R' is the language and 'RStudio' is the software you will use in your problems sets and final project. Both are free for download for your personal computer.

Downloading and Installing R and RStudio

To complete the coursework on your personal computer, you will need to first install 'R' and

then install 'RStudio'. Downloads for all operating systems available below. This is also detailed in Homework 0, which you will do before the first course meeting.

o R:https://cran.rstudio.com/

o  RStudio Desktop:https://www.rstudio.com/products/rstudio/download/

Learning R: Additional Resources


Hadley Wickham and Garrett Grolemund’s reference text R for Data Science can be accessed for free online at:https://r4ds.had.co.nz/

Cheatsheets summarize relevant commands in a few pages (they are dense!):

RIntWrooooooodrkuscVDRDthiiSaaosomtttupnaeuosastdrI,olemiiaUzoRacnopCthfdogiTheoruTteanMiidCatmsCatehshehp,ehesUeaaeeaotCnetstfshdstThehaeDMaeveataeatst:tpiahl:raeeagLbaegnibldtedp:r_lalhoDucertasby(rv)tre,ai(de) rary(ad_)dta(), etc.

Datacamp also provides anonline tutorial for learningR.You need to register for an account, but the tutorial is free. No software installation is required.

There are many other online tutorials and help resources. If you get an error message or need other answers, Google the error message or question. Often users have asked similar questions and received responses on sites likeStack Overflow,an online forum dedicated to computer programming.

Equity, Diversity and Inclusion.


The University of Toronto is committed to equity, human rights and respect for diversity. All members of the learning environment in this course should strive to create an atmosphere of mutual respect   where all members of our community can express themselves, engage with each other, and respect  one another’s differences. U of T does not condone discrimination or harassment against any persons or communities.

Students with accessibility needs

The University provides academic accommodations for students with disabilities in accordance with the terms of the Ontario Human Rights Code. This occurs through a collaborative process that

acknowledges a collective obligation to develop an accessible learning environment that both meets the needs of students and preserves the essential academic requirements of the University’s

courses and programs. For more information on services and resources available to instructors and


students, please see theAccessibility Services website.

Religious observances

The University also provides reasonable accommodation of the needs of students who observe

religious holy days other than those already accommodated by ordinary scheduling and statutory

holidays. Students have a responsibility to alert members of the teaching staff in a timely fashion to upcoming religious observances and anticipated absences, and instructors will make every

reasonable effort to avoid scheduling tests, examinations, or other compulsory activities at these  times. For more information, and to link to the University’s policy on accommodations for religious observances, please see the website of theOffice of the Vice-Provost, Students.