Data Science Boot Camp
Spring 2023
May 9, 2023
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Jun 8, 2023
Application/Registration Deadlines
Mar 16, 2023
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Academics from Member Institutions/Departments
Mar 16, 2023
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Academics from Non-Member Institutions paying the $500 membership fee
Jan 16, 2023
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Academics from Non-Member Institutions applying for Corporate Sponsored Fellowships
Application/Registration Link
You are registered for this program.
Overview
The Erdős Institute's signature Data Science Boot Camp has been running since May 2018 thanks to the generous support of our sponsors, members, and partners. Due to its popularity, we now offer our boot camp online twice per year in two different formats: a 1-month long intensive boot camp each May and a semester long version each Fall.
Instructional Team
Matthew Osborne, PhD
Head of Boot Camps
Office Hours:
TBD
Email:
Preferred Contact:
Slack
Don't hesitate to contact me with any questions or concerns, I'm looking forward to this May's boot camp!
Alec Clott, PhD
Head of Data Science Projects
Office Hours:
TBD
Email:
Preferred Contact:
Slack
Participants are welcome to reach out to me via slack or email. I normally work standard EST hours (9am-5pm), but can always find time to meet folks via Zoom too. Let me know how I can help!
Objectives
The goal of our Data Science Boot Camp is to provide you with the skills and mentorship necessary to produce a portfolio worthy data science/machine learning project while also providing you with valuable career development support and connecting you with potential employers.
Those who successfully complete a team project will receive a digital certificate of completion with a sharable URL.
Project Examples
TEAM
Lime
Yuchen Luo, Ritika Khurana, Aditya Chander, Taylor Mahler
We built a podcast recommendation engine that suggests episodes to a listener based on either a previous episode that they've heard or an episode description that they can input with freeform text entry.
TEAM
Supermassive Black Hole
Anna Brosowsky, Sayantan Khan, Nancy Wang, Ethan Zell, Yili Zhang
We built a movie finder app that allows a user to enter some details they remember about a movie (along with some optional filter info on the genre and release year) and then predicts what movie the user is thinking of. To solve this NLP problem, our tool uses an embed-and-rerank model. We have precomputed vectorizations of movie plot information for the approximately 34,000 movies in our dataset.
Our model’s first step is to vectorize the user’s query and do a fast comparison to find the 100 closest plot vectors. Then it reranks these top 100 closest plots, performing a more thorough comparison using a neural network that semantically compares the plot fragments with the original query. Finally, we output the 10 movies which show up at the top of this new ranking.
First Steps/Prerequisites
Participants should have a base-level familiarity with Python. Participants should also be familiar with some basic math concepts. Finally, you will also need to have your laptop or desktop computer set up for the course.
If you are new to Python, need a quick math refresher, or if you need help setting up your computer, then please follow the link below.
To access the program schedule and content, you must first create an account and member profile and be logged in.
Program Content
Textbook/Notes
Welcome!
Introduction
In this video we welcome you to our data science content.
Data Repositories
Data Collection
One source of data are public data repository sites. In this video we explain what those are and show a few examples.
Web Scraping with BeautifulSoup I
Data Collection
In part one of a two part series we uncover the hidden secrets of the world wide web with BeautifulSoup. The broth of the internet is HTML.
A Broad Overview
Introduction
In this video we give an eagle's eye view of what we will cover in our data science content.
Data Competition Sites
Data Collection
Data competition sites can be another source of data sets. In this video we discuss such sites and demonstrate pulling a data set from one.
Web Scraping with BeautifulSoup II
Data Collection
Soups on! In part two of this series we wrap up web scraping with python.
Data File Types
Data Collection
We quickly review some of the most common data file types you will encounter working on a data based project. We then show you how to load such data using python.
Data in Databases
Data Collection
Your data is stuck in a database, can you get it out? Learn how in this video.
Python and APIs
Data Collection
Let's cover that API in some Python wrapping paper.
Project/Homework Instructions
Schedule
Click any date for more details
Please check your registration email for program schedule and zoom links.
Project/Homework Deadlines