Discovering Data Science is an introduction to topics that intersect between statistics, computation, and real-world applications. This 5-week-long course will be project-driven, which will give students opportunities to analyze real-world datasets and discover the impact of the data. Students will gain an understanding of complex ideas in statistics and computing, and consider the social impact and issues of Data Science such as ethics and design.
Laptop or Desktop Computer: Your computer should should run on Windows, OS X, or Linux. Android Tablets, iPads, Chromebooks, and other similar devices are not supported or difficult to use. We will use Google Colab, which is a free Google App to write Python in (Think Google Docs but for code). Students are free to install Python locally but we will not cover these instructions in class (see the resources page for a guide on installation).
Lecture Binder/Folder: Since this class is only online, you are encouraged to prepare your notes ahead of class in the way that works best for you (e.g. print notes and keep in Binder/Folder, save files in a designated folder on any device, or another system that works best for you). I recommend keeping all of your notes together in one place to stay organized in this fast-paced course. Taking notes in this class is very important to succeeding and learning about Data Science. All notes and resources will be available on the website before each class if you want to print them out (i.e. before 9am M-R).
Labs: We will have daily labs M-R for the last hour of class. The labs will provide students with hands-on experience in data science by writing code to solve various problems. Some labs will be specially designed to help students with their capstone project.
Homework: We will release one homework set each week on Monday morning. Sometimes we might do some of these problems in class if time permits. These are optional assignments but I encourage students to try them. They provide a lot of practice, checks your understanding of material, and provides some extra challenges or concepts.
Capstone Project: We will begin working on our projects on day 1! Students will gradually work on their projects throughout the 5 weeks with the help of special lab assignments and attendance to office hours. Students will complete a professional data analysis report and present a summary of their analysis and findings in a 4-6 minute presentation with slides.
There are no letter grades, but there will be point grading to track completion and performance for each individual student. Each student will receive a written evaluation/feedback each week. Only lab assignments will be graded, but we will also provide feedback on the project at the mid-checkpoint (i.e. end of week 3).
Do not turn in late work, or else it may be difficult to keep up. If you need help, please reach out to one of the instructors or UIC Chance staff members. Late work will not be graded after Saturday at 11:59pm each week.
Data Science is a collaborative science. So do work together, and do not try to do it alone.
Each time we meet, we will spend at least 1 hour working collaboratively in smaller groups. We encourage you to work together and discuss all of your course assignments with your friends and classmates! You will learn more from each other as you talk through the problems, teach each other, and share any ideas you have.
If you are experiencing technical issues please reach out to:
Collaboration is about working together. Collaboration is not giving direct answers to a friend or sharing the source code to an assignment. Please do your best to make a serious attempt to work through each assignment and discuss with others your ideas and doubts so everyone can get more out of the discussion. Your answers must be in your own words, and your code must be typed (not copied/pasted) by you.
Penalty for academic dishonesty: Student may be subject to penalty by UIC Chance code of conduct for students. Also, Jonas and Tamun will be sad.
Academic integrity includes you protecting your own work. If you share your work directly with others and they submit your works as their own, I will consider this as an academic integrity violation as if you submitted someone else’s work as your own.