Link Search Menu Expand Document
BU CS 506

Data Science Tools and Applications

Supplemental material can be found on YouTube

Getting Started Checklist

  1. Join Discord
  2. Create a GitHub account
  3. Create a Kaggle account
  4. Fill out this form (requires BU email) with your GitHub and Kaggle account username
  5. Install Python and Jupyter Notebook
  6. Join Gradescope (code: BKJNW7)

About

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).

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 material will be made available online.

Prerequisites

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.

Workload

There are a number of components to this course:

  1. Weekly labs
  2. A written midterm
  3. An applied midterm
  4. A final project
  5. Participation

Labs

Labs will be graded and attendance recorded. Lowest lab will be dropped.

Participation

You can earn participation by attending class and answering in class polls as well as being active on the course’s discord server.

Written Midterm

In class, closed notes, multiple choice exam.

Applied Midterm

The applied 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 meet certain benchmarks to earn points.

Final Project

The final project can be done as an individual or a group of up to 5 students.

A project proposal will need to be submitted at the end of the first month of the semester. Details will be provided at the start of the semester.

You can select among a number of BU Spark curated projects or you can create your own. You can find a list of projects here

At the end of the semester, some teams will be selected to present a poster of their project on Demo Day (details to follow) and add your project video and/or code to the BU Spark website.

Spark also host “Syntax & Snax” every Thursday 4-7 in CDS.

Final Exam

The final exam will be a closed notes, multiple choice exam.

Grading

  • 5% participation
  • 15% labs
  • 15% written midterm
  • 15% applied midterm
  • 30% final project
  • 20% final exam

Letter Grades may be curved depending on class performance. We will never curve down.

Re-Grades

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.

Emails

If emailing the CS506 staff, please always CC or include the instructor, and all TAs.