Syllabus

Data Science Discovery is the intersection of statistics, computation, and real-world relevance. As a project-driven course, students perform hands-on-analysis of real-world datasets to analyze and discover the impact of the data. Throughout each experience, students reflect on the social issues surrounding data analysis such as privacy and design.

Prerequisites: None

General Education Credit: Quantitative Reasoning I (QR1)

Office Hours

Open office hours are fantastic for getting help on understanding course concepts, getting help on assignments, debugging your code, and more! All open office hours will have multiple TAs and/or CAs available to help you out!

In-person open office hours are held most days in 0060 Siebel Center for Design (south campus):

  • Mondays, 4:00pm - 6:00pm, 0060 SCD
  • Wednesday, 4:00pm - 6:00pm, 0060 SCD
  • Thursdays, 4:00pm - 6:00pm, 0060 SCD
  • Fridays, 4:00pm - 6:00pm, 0060 SCD

Online office hours are held via Zoom:

Course Section

This course is comprised of two sections:

  • Lecture Section: Synchronous in 100 Noyes each M/W/F at 12:00pm, lead by Prof. Wade Fagen-Ulmschneider and Prof. Karle Flanagan

  • Lab Section:

    • Small-group, weekly conceptual and problem-solving discussion sections lead by a Teaching Assistant (TA). Not computer-based. (~15 minutes /week)

    • Small-group, weekly computer-based programming sections with the assistance of course staff. Sections are BYOD (“Bring Your Own Device”). Participation in the lab section makes up half of your lab score each week.

You are required to be registered for BOTH one lecture section and one lab/discussion section.

Course Materials

  • Laptop Computer: You need a laptop running Windows, OS X, or Linux. Android Tablets, Chromebooks, and iPads are not supported. You will need to be able to install both Python and git to complete the labs (instructions provided).

  • Course Booklet: This course has a course booklet you can purchase from the Illini Union Bookstore (IUB) for ~$10. This includes all worksheet puzzles you will complete in class (~100 pages). These documents will be available in PDF form if you’d prefer to print them yourself. Completing all of puzzles (either in the booklet or on your own printouts) will earn you extra credit at the end of the semester.

Course Assignments and Grades

Course grades are given in points, totaling 1,000 points throughout the semester. The breakdown of points is as follows:

  • Hello Survey: 2 points
  • Labs: 260 points (13 × 20 points; 10 for participation, 10 for completion)
  • Homework: 224 points (35 total, 32 × 7 points and lowest 3 dropped)
  • Project: 100 points
  • Midterm Exam 1: 107 points
  • Midterm Exam 2: 107 points
  • Comprehensive Final Exam: 200 points

Final Course Grade

Course points will be translated into a course grade at the end of the semester.

Points Earned Minimum Grade Points Earned Minimum Grade Points Earned Minimum Grade
Exceptional A+ [930, 1107] A [900, 930) A-
[870, 900) B+ [830, 870) B [800, 830) B-
[770, 800) C+ [730, 770) C [700, 730) C-
[670, 700) D+ [630, 670) D [600, 630) D-
[600, 0) F        

We might lower these cutoffs; for example, perhaps 670 points will turn out to be enough for a C-; however, we won’t raise them. (In recent semesters these cutoffs have not moved significantly from these targets.)

Extra Credit

There is an opportunity for extra credit in this course (usually called “+1 points”). Points for extra credit work will be assigned after grade cutoffs are determined, so they are a true bonus to your score. The total amount of extra credit you can earn is capped at 107 points, or slightly more than one letter grade.

Projects

One significant component to this course is the completion of the course projects. You will have at least one week (and usually more) to complete the project. The projects are an opportunity to apply everything you’ve learned in DISCOVERY and explore your individual interests and passions.

Late Submissions

No late submissions are accepted. However, we do drop your 3 lowest HW assignments at the end of the semester. So missing 3 assignments won’t hurt your grade.

Learning Collaboratively

Data Science is a collaborative science. Do not try to tackle this course alone.

We strongly encourage you to discuss all of your course activities (with the exception of exams) with your friends and classmates! You will learn more though talking through the problems, teaching others, and sharing ideas.

Continue to read on “Academic Integrity” to understand the difference between collaboration and giving an answer away.

Academic Integrity

Collaboration is about working together. Collaboration is not giving the direct answer to a friend or sharing the source code to an assignment. Collaboration requires you to make a serious attempt at every assignment and discuss your ideas and doubts with others so everyone gets more out of the discussion Your answers must be your own words and your code must be typed (not copied/pasted) by you.

Academic dishonesty is taken very seriously in STAT 107 and all cases will be brought to the University, your college, and your department. You should understand how academic integrity applies specifically to STAT 107: the sanctions for cheating on an assignment includes a loss of all points for the assignment, the loss of all extra credit in STAT 107, and that the final course grade is lowered by one whole letter grade (100 points). A second incident, or any cheating on an exam, results in an automatic F in the course.

Academic integrity includes protecting your work. If you work ends up submitted by someone else, we have considered this a violation of academic integrity just as though you submitted someone else’s work.