Welcome to General Assembly's Chicago Data Science course! This is a foundational course in data science focusing on theory, applications, and real world problems. Students will learn multiple techniques for supervised and unsupervised learning in both regression and classification settings. In addition, students will gain the theoretical backbone for deciding which tools to use. Students will learn how to use the Pandas and scikit-learn packages in Python to build and validate their prediction models.
- Instructor: Brian Chung
- Expert in Residence: Scott Little
- Producer: Kevin Sella
- Staff Email: [email protected]
- Location: 444 N. Wabash Ave., 5th floor, room 3
- Class Hours: Mondays and Wednesdays, 6PM to 9PM unless otherwise noted
- Office Hours: Direct Message by Slack for quickest response, in-person office hours TBA
- Exit Tickets: Please fill out an exit ticket at the end of every session: Exit Ticket
Students are required to bring a laptop to every session with the Continuum Anaconda Python 2.7 distribution installed.
You have all been invited to use Slack to chat throughout the day and in class. Please use Slack as the primary means to communicate, ask questions, and work with other students. If you have more questions, feel free to contact the staff through Slack as well.
- Homework assignments will be listed on the github repo within the homework folder. Students must submit homeworks (i.e. ipython notebooks, PDFs, etc.) to gadschicago [at] gmail. The subject format should be [HW## - Student Name].
- In addition, there will be a Final Project.
- The project milestones will be treated as homeworks, and students are expected to submit them by beginning of class on the due dates.
- Due to the intensity and magnitude of the final project, less homework assignments will be given near the end of the course. This is to provide more time to work on the project.
Students must fulfill a number of requirements in order to receive General Assembly Letter of Completion in Data Science.
- Students must attend 80% of classes (no more than 4 classes missed)
- Students must complete and submit 80% of all course assignments to instructor satisfaction. Students wil receive timely feedback from staff.
- Students must successfully submit the course final project as outlined in the Project Description. This includes successfully submitting the project milestones, final technical paper, as well as delivering a final presentation.
- If you are having trouble with the homeworks and/or project, please communicate this with the team. We are here to help you learn and succeed.
The schedule is subject to change per class needs and desires.
| Date | Topic | Homework Assigned | Due |
|---|---|---|---|
| Mon - Dec 7 | Intro To Data Science | hw01 | Dec 9 |
| Wed - Dec 9 | Linear Algebra with Python | hw02 | Dec 16 |
| Mon - Dec 14 | K Nearest Neighbors | milestone1 | Dec 21 |
| Wed - Dec 16 | Exploratory Data Analysis | ||
| Mon - Dec 21 | Linear Regression | hw03 | Jan 4 |
| Wed - Dec 23 | No Class | ||
| Mon - Dec 28 | No Class | ||
| Wed - Dec 30 | No Class | ||
| Mon - Jan 4 | Regularization and Cross Validation | No HW4 | |
| Wed - Jan 6 | Naive Bayes | milestone2 | Jan 25 |
| Mon - Jan 11 | Logistic Regression | hw05 | Jan 20 |
| Wed - Jan 13 | K Means Clustering | ||
| Mon - Jan 18 | No Class | ||
| Wed - Jan 20 | Model Assessment/Project/Review | ||
| Mon - Jan 25 | SVM | ||
| Wed - Jan 27 | Decision Trees | milestone3 | Feb 17 |
| Mon - Feb 1 | Ensemble Techniques | ||
| Wed - Feb 3 | Dimensionality Reduction | ||
| Mon - Feb 8 | Guest Lecture, SVD, et al | ||
| Wed - Feb 10 | Recommendation Systems | ||
| Mon - Feb 15 | No Class | ||
| Wed - Feb 17 | Project Preparation Day | milestone4 | Mar 2 |
| Mon - Feb 22 | Integrating ML & Web Technologies | ||
| Wed - Feb 24 | Neural Networks | ||
| Mon - Feb 29 | Time Series | ||
| Wed - Mar 2 | Course Recap and Project | ||
| Mon - Mar 7 | Final Presentations |