Fall 2022 - STAT 260 D100
Introductory R for Data Science (2)
Class Number: 4699
Delivery Method: In Person
Course Times + Location:
Tu 2:30 PM – 4:20 PM
SSCB 9200, Burnaby
Exam Times + Location:
Dec 11, 2022
7:00 PM – 10:00 PM
SWH 10081, Burnaby
Prerequisites:One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, ECON 233, or POL 201, with a grade of at least C- or permission of the instructor. Corequisite: STAT 261.
An introduction to the R programming language for data science. Exploring data: visualization, transformation and summaries. Data wrangling: reading, tidying, and data types. No prior computer programming experience required. Students who have taken STAT 341 or STAT 360 first may not then take this course for further credit.
STAT 261 Labs will start on Tuesday Sept 13 and will run until Thursday Dec 1. There will not be any Labs on Wednesday Sept 7, Thursday Sept 8, nor Tuesday Dec 6.
|Week Number||STAT 260||STAT 261|
|1||Getting started: installing R, RStudio, the tidyverse and other packages, RStudio projects, RMarkdown files and basics||In-class exercises for setting up R|
|2 and 3||Exploring Data: visualisation||Using ggplot to create plots of different types of data|
|4||Exploring Data: transformation and summary statistics||Plotting transformed data (e.g., bar plots, smoothed functions)|
|5||Data Wrangling: data frames and tibbles, importing data||Reading in data in different formats|
|6||Data Wrangling: tidy data||Cleaning a messy data set|
|7||Data Wrangling: relational data||Linking multiple data sets|
|8||Data Wrangling: strings||Handling text data|
|9||Data Wrangling: factors, dates and times||Handling factors and date and time data|
|10||Programming: functions||Writing functions to minimize code length, prevent bugs, and improve readability|
|11||Programming: vectors||Types of vectors and incorporating vectors in functions|
|12||Programming: vectors||For loops|
- Quizzes 20%
- Midterm 30%
- Final Exam 50%
Above grading is subject to change.
MATERIALS + SUPPLIES:
Access to high-speed internet, webcam
R for Data Science, by Garrett Grolemund and Hadley Wickham, available online for free at https://r4ds.had.co.nz/
REQUIRED READING NOTES:
Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.
Department Undergraduate Notes:
Students with Disabilities:
Students requiring accommodations as a result of disability must contact the Centre for Accessible Learning 778-782-3112 or firstname.lastname@example.org.
Students looking for a tutor should visit https://www.sfu.ca/stat-actsci/all-students/other-resources/tutoring.html. We accept no responsibility for the consequences of any actions taken related to tutors.
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html