This is a lecture format class. We meet Tuesdays and Thursday from 8:30-10am in 2548 NUB (North University Building). We will end by 9:50am.
No Class:
Final Class: Th Dec 9
Anticipated due dates of problem sets (revised Sep 20):
If assignment due dates interfere with religious holidays you observe please let me know prior to October 1 to work out alternative arrangements.
Your grade will be based on 10 8 problem sets worth 100 points each and a series of surveys worth 100 points total. This is 900 total points, your best score will be counted twice to give 1,000 so that 1 point = 0.1% of the course grade. The course will use the following grading scale:
Totals less than 760 points will receive a grade on a case-by-case basis. I reserve the right to modify this scale and other grading policies, but grade thresholds will be no higher than shown here.
There are no required texts for this course, but there are required readings from texts and other materials available online or through the library. There are three recommended texts:
Think Python 2, Allen B. Downey
Python for Everybody, Charles R. Severance
Python for Data Analysis, Wes McKinney. (Accessible online to UM students.)
You should have taken or be currently enrolled in an intermediate applied statistics course such as Stats 500 or equivalent. You should be comfortable programming in at least one programming language or scripting in a statistical software language.
The primary goals of Stats 507 are to help you: learn Python from the ground up, become proficient in using Python for data analysis, gain fluency in other data science tools (e.g. git, Linux, SQL), and to make you comfortable reading documentation and independently learning new computing skills.
The topics below represent an approximate scope for the course. I reserve the right to modify this scope. Materials related to each topic will include lectures and readings and, less frequently, examples and/or case studies.
Introduction to Python - the Python ecosystem, programming patterns, data structures, I/O, notebooks, classes and objects, functional programming.
Python’s core scientific modules - NumPy, Pandas, and (some) SciPy.
Communicating with and about data - markdown, Latex, minimal HTML, writing “polished” and professional reports.
Linux shell skills and git - using git, the Linux file system, data processing in the Linux shell, ssh.
Visualization - matplotlib, plotnine, and (possibly) seaborn.
Statistical models - linear and generalized linear models in statsmodels.
Machine learning - core ML models using scikit-learn, specifying, training, and evaluating models, neural network models using keras and tensorflow.
Big data and distributed processing - parallel and asynchronous computing, SQL, mapReduce, Spark.
All of the necessary software in this course is freely available (most is also open source) and can be installed on your personal computer.
You should install a Python 3 distribution and necessary modules for using Jupyter notebooks. I recommend installing Python 3.9. You will also need an ssh client, such as Terminal (Mac, *nix), Putty (Windows) or git for windows (Windows).
If you don’t already have one, you should request a Great Lakes user account.
Owing to the large size of this class, neither I nor the GSIs are here to provide tech support in terms of installation. While we may offer generic advice, please work with your peers and an internet search engine to solve installation or other technology issues.
You are expected to turn in all problem sets by the due dates listed on Canvas. However, to accommodate unexpected circumstances you may utilize “late days” when turning in problem sets. Late days work as follows:
There is no exam for the course, however, late days may not be used to extend any assignment due date past the time of the course’s scheduled exam time of Monday December 13 by 3:30pm.
Course lectures, including slides and classroom audio, may be recorded and made available to students in this course through YouTube, Canvas,or Dropbox. As part of your participation in this course, you may be recorded. If you do not wish to be recorded, please contact the instructor by Tuesday September 7 or as soon as you enroll in the course (whichever is latest) to discuss alternative arrangements.
Students are prohibited from distributing recordings of any class activity without prior written permission from the instructor, except as necessary as part of approved accommodations for students with disabilities. Any approved recordings may only be used for the student’s own private use.
I intend to provide recordings to all students as a courtesy to students who have requested them. While I will invest reasonable effort to provide recordings, I do not guarantee the quality of the recordings nor that all recordings will be available. Finally, I reserve the right to alter this recording policy at any point.
If you think you need an accommodation for a disability, please let me know at your earliest convenience. Some aspects of this course, such as assignments, in-class activities, and recorded lectures may be modified to facilitate your participation and progress. As soon as you make me aware of your needs, we can work with the Services for Students with Disabilities (SSD) office 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.
Unless specifically stated otherwise, students are expected to complete problem sets and any other assignments independently without copying code or text from other students in this course. Students are encouraged to discuss assignments and to help one another with concepts and syntax, but such discussions should not reach the point of representing one student’s work as another’s.
Use of materials from other sources should fall within the license for those materials and include proper attribution.
For more on academic integrity, please be advised of Rackham policy: http://www.rackham.umich.edu/academic-policies/section8/.
The University of Michigan is committed to advancing the mental health and wellbeing of its students. If you or someone you know is feeling overwhelmed, depressed, and/or in need of support, services are available. For help, contact Counseling and Psychological Services (CAPS) at 734.764.8312 and caps.umich.eduduring and after hours, on weekends and holidays, or through its counselors physically located in schools on both North and Central Campus. You may also consult University Health Service (UHS) at 734.764.8320 and https://www.uhs.umich.edu/mentalhealthsvcs, or for alcohol or drug concerns, see https://www.uhs.umich.edu/aodresources.
For a listing of other mental health resources available on and off campus, visit https://uhs.umich.edu/stressresources.
Title IX prohibits sex discrimination to include sexual misconduct: harassment, domestic and dating violence, sexual assault, and stalking. If you or someone you know has been harassed or assaulted, you can receive confidential support and academic advocacy at the Sexual Assault Prevention and Awareness Center (SAPAC). SAPAC can be contacted on their 24-hour crisis line, 734-936-3333 and online at https://sapac.umich.edu. Alleged violations can be reported non-confidentially to the Office for Institutional Equity (OIE) at institutional.equity@umich.edu. Reports to law enforcement can be made to University of Michigan Police Department at 734-763-3434.
As an instructor, one of my responsibilities is to help create a safe learning environment on our campus. I also have a mandatory reporting responsibility. I am required to share information regarding sexual misconduct or information about a crime that may have occurred on U-M’s campus with the University. Students may speak to someone confidentially by contacting SAPAC’s Crisis Line at (734) 936-3333.