Course Logistics

  • Semester: Fall 2020
  • Lectures: Thursdays 6.30 PM to 9.00 PM
  • Mode: Online synchronous (i.e., location is online). The course will be delivered over Zoom (an invite will be sent before the first day of class). See the online learning page for basic technology requirements.
  • Staff
  • Communication: via slack, zoom and one note class notebook.
  • Office hours: online via slack and zoom.

Textbook and Materials

  • Data Science in Production by Ben Weber (2020, $5 for the ebook/pdf). A sample of the first three chapters is available at the publishers page linked here.


  • Any OS should be okay. If in doubt, run a virtual machine running linux (this will be discussed in the class). Some of the software we will work with are:
  • Containers
    • Docker for Desktop
    • kubectl and minikube
  • Python (Anaconda)
    • flask
    • requests
    • pandas
    • pytorch
    • scikit-learn
    • matplotlib
  • Command Line Utilities (CLIs) from AWS, Google Cloud etc.


  • There will be varied computing resources needed for this course. Try using a virtual machine with linux on your own computer if possible. A Windows virtual desktop is available at if needed. You can refer to these two help pages to get started.


  • There are no graded assignments or exams for this course. Students are expected to go over the lectures and practice the use of technologies discussed each week.


  • Students are expected to apply what they learn in the course and demonstrate a deployment of an existing machine learning model they have access to. A suitable documentation of this process along with the scripts/codes/commands used is to be submitted on October 14th (with no exceptions). The evaluation criteria and other details are in the project instructions page. Submission deadline is BEFORE 11.59 PM on the concerned day. Late submissions will have an automatic 20% penalty per day. Use Blackboard for uploading your work as a single zip file.


  • Grades will be assigned based on the project (see project evaluation criteria above) (80%) and course participation (20%).

Miscellaneous Information

  • This is a 2 credit graduate level course offered by the Information and Decision Sciences department at UIC.
  • See the academic calendar for the semester timeline.
  • Students who wish to observe their religious holidays ( should notify the instructor within one week of the first lecture date.
  • Contact the instructor at the earliest, if you require any accommodations for access to and/or participation in this course.
  • Refer to the academic integrity guidelines set by the university.