Spring 2021 - STAT207
Data Science Exploration

STAT 200, Section DS = STAT 207 Data Science Exploration
  • Lecture Attendance Options
    • Synchronous Lecture Option : Visit our Compass page for Zoom links for the synchronous lectures held on
      TuTh 3:30Pm-4:50pm CST.
    • Asynchronous Lecture Option : Videos of the synchronous lecture will be recorded and posted shortly after the lecture on our Compass page.
  • Open Labs
    • Labs will be held online *Tuesdays and Wednesdays 5pm-7pm CST*. Visit our Compass page for Zoom links.
    • Based on class survey results, additional lab times will be opened up at some other timtes (check back later.) Zoom links.
  • Office Hours : Held Online, Mondays 9am-10am CST, Wednesdays 9:30am-10:30am . Visit our Compass page for Zoom links.

STAT207 Instruction Team
Course Related Websites
Syllabus

Overview:Building on the foundation of STAT 107, Data Science Discovery, we use Python, Jupyter notebooks, and GitHub to explore data science techniques, statistical concepts, and data analytic workflows, combined with the statistical analysis of STAT 200. (STAT107 is not technically required for this course.) As we explore data science we:

  • Develop understanding of probability models for noisy data and how these translate into uncertainty analysis and statistical inference
  • Understand how modeling assumptions and sampling frames affect our conclusions
  • Become adept with multiple regression modeling, basic machine learning, and inference
  • Become proficient in Python coding for data management, analytics, visualization
  • Understand and use GitHub repositories, the industry standard for submitting code and reports

Prerequisites: None. Students who completed STAT 100 or 107 previously are welcome. Equally welcome are students in quantitative fields taking STAT 200 as their first course in statistical analysis. The early programming will comprise an accelerated introduction for those who did not take STAT 107 prior and a quick review for those who did. You will receive course credit and Quant I or Quant II credit for STAT 200 while participating in the launch of Data Science Exploration!

Course Topics

See: Course Topics

Course Section

The primary contact hours for this course are comprised of two major components:

Lectures

  • Synchronous Lecture Option : Visit our Compass page for Zoom links for the synchronous lectures held on
    TuTh 3:30-4:50pm CST.
  • Asynchronous Lecture Option : Videos of the synchronous lecture will be recorded and posted shortly after the lecture on our Compass page.
You aren't *required* to attend the synchronous lecture, but you're *encouraged* to attend if you can make it though. Attending lecture at a fixed time will provide you with the opportunity to learn and ask questions from the instructor, the TAs/CAs, and your classmates.

Weekly Open Labs

  • Labs will be held online Tuesdays and Wednesdays 5pm-7pm CST*. Visit our Compass page for Zoom links.
  • Go here for personalized and small group help
  • These are ideal settings to complete your lab assignments
Course Materials
Course Assignments and Grades

Course grades are computed based on your percentage out of 650 points for the course. The components are as follows:

  • Homework Labs - 250 pts
  • Midterm Exams - 200 pts total
  • Final Exam - 200 pts

Total: 650 pts

Bonus points:
  • Any homework/lab points above 250 (total possible for 10 out of 12) will be added to bonus points, up to 50 bonus points.
  • Project - Up to 25 bonus pts
Late Policies:
  • Homework that is late by 5 minutes up to 24 hours will be deducted 30% of the assignment.
  • Homework that is late by more than 24 hours will receive 0 points.
Regrade Policies: You have ONE week to request a grade correction after a homework score is posted. You should clearly present the following information to the head TA (Yuhan Li, yuhanli8@illinois.edu):
  • Which homework is involved (e.g. Homework #6)
  • A detailed explanation of the suspected error
  • The number of points you feel you should have received for the question.

Final Course Grade

Course points will be translated into a course grade at the end of the semester. The grade thresholds will be based on your percentage score out of 650:

Grade Min Pct Min Pts Grade Min Pct Min Pts Grade Min Pct Min Pts
A+ 97 630.5 A 93 604.5 A- 90 585
B+ 87 565.5 B 83 539.5 B- 80 520
C+ 77 500.5 C 73 474.5 C- 70 455
D+ 67 435.5 D 63 409.5 D- 60 390

Participation

If you are able to, we encourage you to attend the synchronous class and open labs. Being present will help you keep up with what is going on, gain hands on experience in learning activities, and benefit from interacting with other students and instructional staff. If you are unable to attend the lecture, the expectation is that you will watch the lecture video within 24 hours of it being posted. If you are unable to attend the labs and have content questions, please ask them on Campuswire!

Please ask questions whenever anything is confusing. If you find errors in the notes, please report them to the instructor. The instructor will be very happy that you detected them so they can be corrected!

Late Submissions

No late submissions are accepted. However, it is only necessary to complete 10 of 12 labs to achieve full credit.

Learning Collaboratively

Using the Breakout Rooms

While we can't all see eachother on campus this semester, we want you to collaborate and form connections with your classmates in the class :) We will try to use at least one breakout room each lecture.

Working Together

We 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.

Feel free to use my Spatial Chat room to casually meet up with students in the class. One idea might try working in this room during office hours/labs, and then go to the Zoom link when you have a question.

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 207 and all cases will be brought to the University, your college, and your department. You should understand how academic integrity applies specifically to STAT 207: the sanctions for cheating on an assignment includes a loss of all points for the assignment and that the final course grade is lowered by one whole letter grade (70 points). A second incident, or 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.

Email Note
Given that this course is completely online, please check your email regularly for important class communications.

COVID-19 Note
Given the uncertain nature of how this semester might unfold due to COVID-19. The instructor reserves the right to make any changes she considers academically advisable. Such changes, if any, will be announced in class. Please note that it is your responsibility to attend the class and keep track of the proceedings. However, we will try to adhere to this syllabus and course schedule as much as possible, and will send an email informing you of any changes.