Fall 2022 - STAT207
Data Science Exploration

Course Components

This course is comprised of the following four components.

Main Lecture

  • Lecture 1 Time: TuTh 11:00AM - 12:20PM CST
  • Lecture 2 Time: TuTh 12:30PM - 01:50PM CST
  • Location: 101 Transportation Building
  • Instructor: Dr. Tori Ellison
  • Lecture Attendance Policy: Not part of participation grade, but highly encouraged! For every lecture you attend and submit a post lecture summary for, you will earn a bonus point towards your overall grade (see more information below about how to complete a bonus post lecture summary).

During the lectures we will work through the lecture materials for that day. New lecture materials for the given day will be posted on the Course Schedule (in a zip file) *at least* one hour before class.

The course lectures materials will be deliverd via about 20 units. Each new lecture unit will most likely contain the following files.

Lecture Unit Materials
  • Slides pdf
    • This pdf will focus more on the theoretical content of the class. This pdf contains "skeleton notes" that we will fill out in class. I would suggest downloading (perhaps printing) these pdf's yourself and taking notes in class.

  • csv file (usually)

  • Jupyter notebook file
    • The Jupyter notebook file will introduce new Python functions, parameters, packages etc. We will use it to go through real-world statistical applications of the theoretical content that we talk about in the slides pdf.

    I would suggest opening this Jupyter notebook and going through the code yourself in class. Try changing some of the parameters/values in the code to see what it does!

  • Notebook pdf
    • This is simply just a pdf copy of the Jupyter notebook file. I would suggest downloading (perhaps printing) these pdf's yourself and taking notes in class.

Assigned Lab Sections

These are the lab sections/times that you specifically enrolled in. You should only attend the lab that you enrolled in.

Assigned Labs - Held in Person Day Time Location TA CAs
Lab Section 1: W 9:00am-10:50 2018 Campus Instructional Facility Jim Yan Junseok Yang, Divya Jain
Lab Section 2: W 11:00am-12:50 3038 Campus Instructional Facility Jim Yan Kelly Yuen, Donlapun Wongkarnta
Lab Section 3: W 1:00pm-2:50 3038 Campus Instructional Facility Jordan Deklerk Junseok Yang, Adarsh Kamath
Lab Section 4: W 3:00pm-4:50 3038 Campus Instructional Facility Jordan Deklerk Afnan Dzhaharudin, Adarsh Kamath

Lab Attendance Policy: Your attendance at the labs is part of your participation grade.
  • For each lab section that you attend and participate in you will get 5 points towards your participation grade. A perfect participation grade in the class is worth 50 points.
  • Thus this means that you can miss up to 4 labs penalty free.

  • Your lab TA (Jim or Jordan) will take attendance at each lab. Arriving more than 10 minutes late to the lab or leaving more than 10 minutes early from the lab counts as an absence.
  • If you have to miss more than 4 labs due to health issues, religious observances, varsity obligations, or other extenuating circumstances you should email Dr. Ellison and we can discuss options at that point.

Lab Purpose: The purpose of the assigned lab sections is to give you a place to work on the weekly lab assignments where you can ask for help and get quick feedback from the teaching assistant and course assistants assigned to your lab section. Later in the semester, you will use these lab sections to work on your final projects.

Lab Assignment Components:Your weekly lab assignments will be comprised of two parts: a.) an individual lab assignment and b.) a group lab assignment. Thus, these assigned labs will give you the space to meet and collaborate with your group in person.

Tip: You should try to work on both the group and individual parts of the lab assignment in your assigned lab. Furthermore, you should try to get as much as you can done in these labs, while you have the immediate feedback of the TA and CAs.

Additional (Optional) Lab

The lab assignments will generally be due Tuesdays at 11:59pm CST. So to get additional help answering any last minute questions we will have additional (optional) lab times on Tuesdays 5pm-7pm CST, 106B3 Engineering Hall for every Tuesday except on September 6. On 9/6, they will be held in 305 MSEB.. There will be several course assistants to help answer your questions. You are not required to attend these additional open labs.

These optional labs will be held in-person, however you can also attend online if you'd like (see Canvas for Zoom links.)

Instructor Office Hours

These office hours will be held in-person, however you can also attend online if you'd like (see Canvas for Zoom links.)
Mondays 1:30pm-3:30pm CST
Computer Applications Building 138





STAT207 Instruction Team
  • Instructor: Tori Ellison, Department of Statistics

  • Teaching Assistants:
    TA Lab Section Time Email
    Jim Yan W 9:00am-10:50 yiciyan2@illinois.edu
    Jim Yan W 11:00am-12:50 yiciyan2@illinois.edu
    Jordan Deklerk W 1:00pm-2:50 deklerk3@illinois.edu
    Jordan Deklerk W 3:00pm-4:50 deklerk3@illinois.edu

  • Course Assistants: More Info Here


Course Related Websites


Learning Outcomes

Overview:Building on the foundation of STAT 107, Data Science Discovery, we use Python, Jupyter notebooks, and GitHub to explore statistical concepts and the data science pipeline, combined with the statistical analysis of STAT 200. As we explore data science we will do the following.

  • Develop an 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


Preqrequisites

STAT107



Course Topics

See: Course Topics



Course Materials


Course Assignments and Grades

Graded Components

Course grades are computed based on your percentage out of 800 points for the course. The graded components are as follows:

  • Lab Attendance - 50 points
  • Two Midterm Exams - 200 pts total
  • Individual Lab Assignment Part - 250 pts
  • Group Lab Assignment Part - 50 pts
  • Project- 75 points
  • Final Exam - 175 pts

Total: 800 pts

Drop the 2 Lowest Assignments Policy:

Each lab assignment will be worth a total of 30 points total:
  • 25 points for the individual lab assignment part
  • 5 points for the group lab assignment part.
There will be 12 lab assignments, however we will drop the lowest two assignment grades. Thus a perfect total lab assignment score will amount to 300.

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 YOUR lab TA (ie. either Jim Yan yiciyan2@illinois.edu or Jordan Deklerk deklerk3@illinois.edu):
  • Which lab assignment is involved (e.g. lab assignment #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 800:

Grade Min Pct Min Pts Grade Min Pct Min Pts Grade Min Pct Min Pts
A+ 97 776 A 93 744 A- 90 720
B+ 87 696 B 83 664 B- 80 640
C+ 77 616 C 73 584 C- 70 560
D+ 67 536 D 63 504 D- 60 480

Lab Attendance

For each lab section that you attend and participate in you will get 5 points towards your participation grade. A perfect participation grade in the class is worth 50 points. Thus this means that you can miss up to 4 labs penalty free.

Bonus Lecture Attendance Points

For each lecture that you attend, you can earn an additional bonus point towards your overall grade. With perfect lecture attendance, this means that you can boost your final course grade by 3.6% (=29/800)

In order to be counted as "attending" you need to submit your post lecture summary report on a piece of paper containing:
  • Your full name
  • Your signature
  • At least two sentences about what the lecture was about in your own words
  • A question that you had (if you understood everything in the lecture, you can just write "no questions")
Showing up more than 15 minutes late also disqualifies you from the bonus point.

Midterm Exams

Each of the midterm exams will contain two parts.

  • An in-class part
    The in-class exam component will be more conceptual, and not involve any coding. You should bring a calculator for basic arithmetic calculations.

  • A take-home part
    • The take-home component will involve coding in a Jupyter notebook and may look similar to a lab assignment. There will be multiple versions of the takehome exam. On the dates/times listed on the course schedule you will be emailed a personalized copy of your exam. You will then have 24 hours to complete the exam and submit it on Canvas.

    You are allowed to use the notes/book. You are NOT allowed to discuss this exam, open someone else's exam, or share/send your exam to anyone else except for Dr. Ellison until after the 24 hour exam period is over.

Final Exam

The final exam will just be a take-home.

  • A take-home final exam
    • The take-home final exam will involve coding in a Jupyter notebook and may look similar to a lab assignment. There will be multiple versions of the takehome exam. On the dates/times listed on the course schedule you will be emailed a personalized copy of your exam. You will then have 24 hours to complete the exam and submit it on Canvas.

    You are allowed to use the notes/book. You are NOT allowed to discuss this exam, open someone else's exam, or share/send your exam to anyone else except for Dr. Ellison until after the 24 hour exam period is over.

Lab Assignments


Individual Lab Assignment

The individual part of the lab assignment is to be completed by each student individually. See the assignments tab for how to download and submit the individual lab assignment to your Github repository for each week.

Group Lab Assignment

How to Complete and Submit: The group part of the lab assignment is to be completed in groups of 2-3 from students in your assigned lab section. Only one group member needs to push their group assignment Jupyter notebook to their net id repository. Make sure to put all group member names in the notebook!

Back Up Plans:If you are unable to attend your lab section that week and all of the students in your lab section have already started working on the group lab assignment, then you can complete the group part on your own. See the assignments tab for how to download and submit the group lab assignment to your Github repository for each week.

The group lab assignments will be more flexible in the questions that we are asking and will be more project-based.

Project

This project will be completed by the end of the semester. This will involve:
  • a final report (in a Jupyter notebook file)
  • a presentation during your final lab section.
You should work in groups of 2-3 on this project.

Learning Collaboratively

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.

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
You should 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.