Fridays: Tutorial either 10:00-12:00 or 14:00-16:00, corresponding to your lecture time. Your tutorial assignment and location will be posted on the the UofT Portal page for the course https://portal.utoronto.ca/. To receive course credit for tutorials you should only attend the tutorial in which you are enrolled.
Statistics is about how we can learn from data. Data Science is a relatively new interdisciplinary field that also includes the computational aspects of carrying out a data analysis, including acquisition, management, and analysis of data. Statistical reasoning and computing with data play important roles in this emerging discipline. The purpose of this course is to give you a broad introduction to many of the ways statisticians learn from data. In addition to statistical reasoning, learning from data involves computation and communication. We will use the R programming language and environment for statistical computing, and tutorials will introduce students to communicating statistical knowledge.
By the end of this course, you should be able to:
Describe how statistical methods can be used to learn from data, including methods for description, explanation, and prediction.
Carry out a variety of statistical analyses in R and interpret the results of the analyses.
Implement the computational steps involved in the management and statistical analysis of data using R.
Identify appropriate uses of statistical methods to answer questions, including their strengths and limitations.
Clearly communicate the results of a data analysis to both technical and non-technical audiences.
Class slides, notes, and other important information can be found on the course website.
Baumer, B.S., Kaplan, D.T., and Horton, N.J. Modern Data Science with R. 2017. CRC Press.
Diez, D. M., Barr, C.D., and Cetinkaya-Rundel, M. Introdcutory Statistics with Randomization and Simulation First Edition. This is a free book.
Hands-On Programming with R, by Garrett Grolemund. This is a free book.
R for Data Science, by Hadley Wickham and Garrett Grolemund is a wonderful resource. This is a free book.
See the R Resources section of the course website.
We will be using Piazza as a platform for discussions. You can find our class page at: https://piazza.com/utoronto.ca/fall2018/sta130h1/home. Students will be able to post anonymously to classmates, but not instructors.
Be sure to read Piazza’s Privacy Policy and Terms of Service carefully. Take time to understand and be comfortable with what they say. They provide for substantial sharing and disclosure of your personal information held by Piazza, which affects your privacy. If you decide to participate in Piazza, only provide content that you are comfortable sharing under the terms of the Privacy Policy and Terms of Use.
| Assessment | Weight | Date | Time | Location |
|---|---|---|---|---|
| Survey completion | 2% | Distributed during last week of classes | NA | NA |
| Mentorship program | 3% | Details will be available at your second tutorial (Friday, September 21) | NA | NA |
| Tutorial | 20% | Weekly assignments due in tutorial | 10-12, 2-4 | TBA |
| Term Test | 20% | Friday, October 26 | 10-12, 2-4 | TBA |
| Final Project | 20% | Thursday, December 6 | 10-12, 2-4 | TBA |
| Final Exam | 35% | TBA | TBA | Scheduled by Faculty Arts & Science |
Your tutorial grade includes any assigned work that is due before tutorial and any work done during tutorial. Note that if you miss a tutorial, there is no way to make up this component of your mark.
If you are enrolled in the 10-12 lecture section then your tutorial will be on Friday 10-12, and if you are enrolled in the 2-4 lecture section then your tutorial will be on Friday 2-4.
Any requests to have your test remarked must contain a written justification for consideration. Marking requests should be made within one week of receiving your test. Please note that we reserve the right to look at all questions on your test when you re-submit an assessment for reconsideration.
Questions about course material or organization, such as,
should be posted on the discussion forums on Piazza or asked in person. Questions can be posted anonymously (so that the author is anonymous to other students but not to the instructors), if desired.
If your communication is private, such as, I missed the test because I was ill, then e-mail your instructor. If you missed a tutorial then e-mail your TA. Use your utoronto.ca e-mail account to ensure that your message doesn’t automatically go to a Junk folder and include your full name and student number.
You are responsible for knowing the content of the University of Toronto’s Code of Behaviour on Academic Matters.
As a general rule, we encourage you to discuss course material with each other and ask others for advice. However, it is not permitted to share complete solutions or to directly share code for anything that is to be handed in. When an assignment is required to be completed as a team, you may share solutions and code with other members of your team, but not with another team in the class. For example, “For question 2.1 what R function did you use?” is a fair question; “Please show me your R code for question 2.1” is not.
If you have any questions about what is or is not permitted in this course, please do not hesitate to contact your instructor (Prof. Gibbs if your last (family) name starts with A-L and Prof. Taback if your last (family) name starts with M-Z).
The University of Toronto is committed to accessibility. If you require accommodations for a disability, or have any accessibility concerns about the course, the class room, or course materials, please contact Accessibility Services as soon as possible: accessibility.services@utoronto.ca or http://accessibility.utoronto.ca
The course is designed to actively engage you in the course material. We hope you’ll find the statistical reasoning and data science interesting, challenging, and fun. In order for classroom sessions and tutorials to be effective, prepare by learning about the week’s concepts through completing the recommended problems and readings.