Welcome to STATS 306 / DATA SCI 306

This is an introductory statistical computing course based on the R programming language and the tidyverse package. Topics covered include data wrangling, data visualization, basics of programming in R, and basics of statistical modeling.

• Textbook: We will use R for Data Science by Grolemund and Wickham. It is available both as a printed book and as an online resource.
• Canvas: You should access the Canvas class page for this course frequently. It will let you access important announcements, homework assignments, and exams. (requires UM login)
• Slack: The slack workspace for the course is at um-wn22-stats306.slack.com (requires UM login)
• Colab: All lecture notebooks can be accessed in Google Colab by clicking here:
• Binder: All lecture notebooks can be accessed in Binder by clicking here:
• Days and Times: Mondays and Wednesdays, 10-11:30
• Location: 1202 SEB (for virtual lectures, you can find the zoom link in canvas and slack)

Instructor Information

Note: Due to the omicron surge, my office hours will be held via zoom until further notice.

Name: Ambuj Tewari
Office Hours: Tue and Thu, 11:30-1 (zoom link)
Email: tewaria@umich.edu

GSI Information

Note: Lab webpages also have GSI office hour information.

Name: Benjamin Osafo Agyare (Lab 002)
Lab Webpage: https://bosafoagyare.netlify.app/courses/stats306-w22/
Email: bagyare@umich.edu

Name: Prayag Chatha (Lab 003)
Lab Webpage: https://chathasphere.github.io/teaching/stats306/
Email: pchatha@umich.edu

Name: Victor Verma (Lab 004)
Lab Webpage: https://victorverma.github.io/stats_306_w22
Email: vkverma@umich.edu

Name: Heather Johnston (Lab 005)
Lab Webpage: https://sites.google.com/umich.edu/lab005/
Email: hajohns@umich.edu

Grading

The final grade in the course will be determined by your scores in homework, a midterm exam, and a final exam using the weights given below.

• Homeworks (30%) Note: your lowest homework score will be dropped when calculating the final course grade
• Midterm Exam (30%)
• Final Exam (40%)

Late submissions will not be accepted unless there is a documented health or family emergency.

Final grade in this course grading will be awarded using the grading scheme below.

• 99-100: A+
• 95-98.9: A
• 90-94.9: A-
• 85-89.9: B+
• 80-84.9: B
• 75-79.9: B-
• 70-74.9: C+
• 65-69.9: C
• 60-64.9: C-
• 0-59.9: D+ or below

Academic Integrity

The University of Michigan community functions best when its members treat one another with honesty, fairness, respect, and trust. The college promotes the assumption of personal responsibility and integrity, and prohibits all forms of academic dishonesty and misconduct. All cases of academic misconduct will be referred to the LSA Office of the Assistant Dean for Undergraduate Education. Being found responsible for academic misconduct will usually result in a grade sanction, in addition to any sanction from the college. For more information, including examples of behaviors that are considered academic misconduct and potential sanctions, please see https://lsa.umich.edu/lsa/academics/academic-integrity.html

Accommodation for Students with Disabilities

If you think you need accommodation for a disability, please let me know at your earliest convenience. Some aspects of this course, the assignments, the in-class activities, and the way the course is usually taught may be modified to facilitate your participation and progress. As soon as you make me aware of your needs, we can work with the Office of Services for Students with Disabilities (SSD) to help us determine appropriate academic accommodations. SSD (734-763-3000; http://ssd.umich.edu/) typically recommends accommodations through a Verified Individualized Services and Accommodations (VISA) form. Any information you provide is private and confidential and will be treated as such.

Mental Health and Well-Being

Students may experience stressors that can impact both their academic experience and their personal well-being. These may include academic pressures and challenges associated with relationships, mental health, alcohol or other drugs, identities, finances, etc. If you are experiencing concerns, seeking help is a courageous thing to do for yourself and those who care about you. If the source of your stressors is academic, please contact me so that we can find solutions together. For personal concerns, U-M offers a variety of resources, many which are listed on the Resources for Student Well-being webpage. You can also search for additional well-being resources here.

Syllabus

Introduction to Data Science

Data Visualization: Aesthetic mappings, scatter plots, facets, geometric objects, statistical transformations, position adjustments, coordinates

Data Transformation: Filtering, arranging, selecting, mutating, summarizing, grouping, pipes

Exploratory Data Analysis (EDA): Visualizing distributions, typical and unusual values, missing values, covariation

Tibbles and Data Import

Tidy Data: Pivoting, separating and uniting variables, handling missing values

Strings and Regular Expressions: String basics, pattern matching, anchors, character classes, alternatives, repetition, grouping, detection, extraction, stringi

Functions

Vectors

Iteration

Introduction to Modeling: A simple model