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- Join Piazza and Discord
- Create a GitHub account
- Create a Kaggle account
- Fill out this form (requires BU email) with your GitHub and Kaggle account username
- Install Python and Jupyter Notebook
- Sign up for GradeScope (code: WBZEWG)
The goal of this course is to provide students a hands-on understanding of classical data analysis techniques and to develop proficiency in applying these techniques in modern programming languages (Python) while also learning about the social and ethical challenges of collecting and mining data by studying real world examples.
The course introduces students to a wide range of techniques that are commonly used in the analysis of data such as clustering, classification, regression, and neural networks.
Note that this is not a Python (or an introduction to programming) course, so self-study will be necessary for those students who do not already know the language.
There is no textbook for this course, all the material is available in the course github repo (also linked above). Additional notes and articles related to this course can be found on Lance Galletti's Medium.
Students taking this class must have some prior familiarity with programming at the level of CS 105, 108, or 111, or equivalent. CS 132 or equivalent (MA 242, MA 442) is required. CS 112 is also helpful.
BU Spark! offers students an opportunity to work on technical projects provided by companies or organizations in the Greater Boston area through our experiential learning lab (X-Lab). For this semester, Spark! has partnered with CS506 to offer a diverse selection of external data science projects scoped to support the course’s learning outcomes and enhance the student experience. To learn more about Spark!, please visit their website.
Spark! projects are a great opportunity for students to get real-world project experience to highlight on their github and CV. These projects have already been curated and will be presented during “Pitch Day”. Project descriptions will be made available at the start of the semester. BU Spark! projects will be led by one of the Spark! Project managers. Each project will be assigned a Spark! Technical Engineer to review PRs, review code, and provide technical support.
Teams will have ~5 students. Students that decide to take on the role of team lead will receive extra credit. Teams will be formed based on availability and a project preference form that you will be asked to submit after Pitch Day.
At the end of the semester, one team will be selected to present their project on the Spark Demo day stage. One team per project will be selected to present a poster of their project on Demo Day.
Labs will be dedicated to helping you with your project. All team members must attend the same lab to provide an update on the project and plan the week to come on Trello - which will count toward the project grade. Technical Experts (TEs) as well as TAs will be available during labs to help.
The midterm will be a Kaggle Data Science competition among the students in the class with a live leaderboard. Students will need to submit predictions based on a training dataset and a report detailing the methods used and decisions made.
Every lecture is accompanied with a worksheet. Monday worksheets are due at 11:59PM on Tuesday night. Wednesday worksheets will contain a take home challenge / exercise and they will be due at 11:59PM on Sunday night. There will be no partial credit for worksheet questions and there is no late policy (if you miss the deadline you get no points). 3 worksheets can be dropped at the end of the semester.
Collaboration is highly encouraged in this class. You can submit worksheets as a group / team on gradescope - everyone on the team will receive the same grade. Working in a team is a great way to share knowledge and help alleviate workload and stress.
If you notice an issue with a grade you’ve received, please don't email the teaching staff. Instead, please submit a regrade on Gradescope within 48h of receiving the grade. Anything beyond 48h will not be accepted for a re-grade.
If emailing the CS506 staff, or creating a private Piazza post, please always CC or include the instructor, the TF, and all TAs.
- 40% worksheets
- 20% midterm
- 40% final project
- 8% early insights
- 14% mid-semester report
- 18% final report
- 5% extra credit
| Letter | Grade |
|---|---|
| A | 95% + |
| A- | 90% - 95% |
| B+ | 87% - 90% |
| B | 83% - 87% |
| B- | 80% - 83% |
| C+ | 77% - 80% |
| C | 73% - 77% |
| C- | 70% - 73% |
| D | 60% - 70% |
| F | below 60% |
Extra credit can be earned by consistently:
- Asking and answering questions on Piazza
- Contributing to our class repository or course website via PRs (e.g. by fixing typos, providing clarification edits, sharing class notes, etc.)