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.
General Education Credit: Quantitative Reasoning I (QR1)
Open office hours are held online via Zoom:
- Mondays, 4:00pm - 6:00pm, online via Zoom (Link on Compass 2g)
- Tuesday, 4:00pm - 6:00pm, online via Zoom (Link on Compass 2g)
- Wednesdays, 4:00pm - 6:00pm, online via Zoom (Link on Compass 2g)
- Thursdays, 4:30pm - 6:30pm, online via Zoom (Link on Compass 2g)
- Fridays, 4:00pm - 6:00pm, online via Zoom (Link on Compass 2g)
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 TAs and CAs available to help you out! Prof. Wade and Prof. Karle will be at the open office hours at various times throughout the week but also hold professor office hours:
- Wednesdays, 9:00am - 11:00am, online via Zoom (Link on Compass 2g)
This course is comprised of two sections:
Lecture Section: Meets three times a week (M/W/F), 50 minutes each lecture, lead by Prof. Wade Fagen-Ulmschneider and Prof. Karle Flanagan
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. Some sections will be BYOD (“Bring Your Own Device”) and others will be in computer labs for students who do not have or choose not to use their own device.
You are required to be registered for BOTH one lecture section and one lab/discussion section.
Laptop Computer (required for BYOD sections): If you are in a “Bring Your own Device” section, 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).
iClicker (highly recommended): Each day in class, a few iClicker questions will be asked and you can get extra credit for answering them in lecture.
Lecture Binder (highly recommended): As you arrive at each class, we will hand out that day’s lecture notes. We recommend getting a binder to keep all of your notes in one place. Taking notes in class is key to succeeding in this course. We’ll have the pdfs of the notes on the website before each class if you want to print them out yourself (or to use a tablet in lecture).
Course Assignments and Grades
Course grades are given in points, totaling 1,000 points throughout the semester. The breakdown of points is as follows:
- Labs: 180 points (12 × 15 points), points over 180 are extra credit
- Homework: 220 points (22 × 10 points)
- Project: 200 points
- Midterm Exam 1: 100 points
- Midterm Exam 2: 100 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|
|[1070, 1100]||A+||[930, 1070)||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-|
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.)
There is an opportunity for significant 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.
- Weekly extra credit Python notebooks
- Daily iClicker questions
One significant component to this course is the completion of the course project (145 points). You will have ~5 weeks to complete the project, including a lab/discussion dedicated to working with the course staff on your project. With the project, you will focus on the analysis of a single real-world dataset to discover interesting and insightful features and perform a detailed reflection on your findings to understand the social issues that arise from such analysis.
No late submissions are accepted. However, you need to complete only 12/15 labs to earn all 180 points for lab section. Additional points earned are counted as extra credit. Other extra credit opportunities will be offered. All sources of extra credit cannot exceed +107 points to your final grade.
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.
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.