Data Science Boot Camp
May-Summer 2024
May 6, 2024
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Jun 5, 2024
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Problem Solving Session 1
Next Event
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Registration Deadlines
May 7, 2024
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All interested participants
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Category
Launch, Core Program, Boot Camp, Projects, Certificates
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 three times per year in two different formats: a 1-month long intensive boot camp each May and a semester long version each Spring & Fall.
Click here to be invited to the slack organization: The Erdős Institute
Click here to access the slack cohort channel: #slack-cohort-channel
Click here to access the slack program channel: #slack-program-channel
Click here to download the Events & Deadlines .ics calendar file
Organizers, Instructors, and Advisors
Steven Gubkin, PhD
Lead Instructor
Office Hours:
MTWRF 12pm - 1pm ET, and by appt.
Email:
Preferred Contact:
Slack
Please feel free to message me on Slack with any questions!
Alec Clott, PhD
Head of Data Science Projects
Office Hours:
By appt. only
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 after work. 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.
Project Examples
TEAM 13
Hitmakers vs. One-Hit Wonders: Predicting Sustained Success in the Music Industry
James McNally,Yundi Kong,Guillermo Sanmarco,Vishal Gupta
Question:
What early signals predict sustained success in the music industry?
Objective:
Many musicians produce hit songs, but not all are able to do so more than once. This project builds a machine learning classifier to distinguish hitmakers (artists with multiple top 20 Billboard Hot 100 hits) from one-hit wonders, using only information available at the moment of a musician’s first top 20 hit song.
Conclusions:
Our model reveals that prior charting experience, collaboration network position, chart longevity, genre breadth, and dominant genre affiliations are the strongest predictors of sustained success.
Data sources:
- MusicBrainz (artist metadata, genre tags, collaboration graph)
- Billboard Hot 100 & 200 chart data
- Spotify (artist and song metadata)
- Google Trends (relative search volume at time of first hit song)
TEAM 16
Predicting Lead Contamination in NY School Drinking Water
Ranadeep Roy,Cami Goray,Hana Lang
Lead is a toxic metal, and in children especially, lead exposure can have severe health consequences -- even small amounts of lead have the potential to affect memory, behavior, and learning ability. Despite this, numerous schools across New York State have at least one drinking water outlet with lead levels testing for above 5 ppb. In this project, we aim to predict the presence of lead contamination in school drinking water, and better understand the role of demographic, socioeconomic, infrastructural, and geographic features in elevated lead levels.
First Steps/Prerequisites
Program Content
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Course materials are available on github through the following link:
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Textbook/Notes
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Autoregressive (AR(p)) Models
07 Week: Time Series II (prerecorded)
An autoregressive model expresses the value of a time series as a linear combination of lags plus an error term.
Project/Homework Instructions
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Schedule
Click on any date for more details
Orientation & Setup
Phase 1: Instruction and Project Completion
Project Review & Judging
Phase 2: Intense Interview Prep & Career Connections
Problem Solving Session 1
May 6, 2024 at 03:00 PM UTC
EVENT
Extra Help with Setting Up
May 6, 2024 at 08:30 PM UTC
EVENT
Lecture 2: Data Collection
May 7, 2024 at 07:00 PM UTC
EVENT
Lecture 3: Regression I
May 8, 2024 at 07:00 PM UTC
EVENT
Office Hours
May 9, 2024 at 04:00 PM UTC
EVENT
Office Hours
May 10, 2024 at 04:00 PM UTC
EVENT
Office Hours
May 13, 2024 at 04:00 PM UTC
EVENT
Problem Solving Session 6
May 14, 2024 at 03:00 PM UTC
EVENT
Alternate Problem Session 6
May 14, 2024 at 11:00 PM UTC
EVENT
Lecture 7: Time Series II
May 15, 2024 at 07:00 PM UTC
EVENT
Office Hours
May 16, 2024 at 04:00 PM UTC
EVENT
Office Hours
May 17, 2024 at 04:00 PM UTC
EVENT
Lecture 9: Classification II
May 20, 2024 at 07:00 PM UTC
EVENT
Office Hours
May 21, 2024 at 04:00 PM UTC
EVENT
Problem Solving Session 11
May 22, 2024 at 03:00 PM UTC
EVENT
Problem Solving Session 12
May 23, 2024 at 03:00 PM UTC
EVENT
Alternate Problem Session 11
May 23, 2024 at 11:00 PM UTC
EVENT
Office Hours
May 28, 2024 at 04:00 PM UTC
EVENT
Office Hours
May 31, 2024 at 04:00 PM UTC
EVENT
Office Hours
May 6, 2024 at 04:00 PM UTC
EVENT
Problem Solving Session 2
May 7, 2024 at 03:00 PM UTC
EVENT
Problem Solving Session 3
May 8, 2024 at 03:00 PM UTC
EVENT
Alternate Problem Session 3
May 8, 2024 at 11:00 PM UTC
EVENT
Lecture 4: Regression II
May 9, 2024 at 07:00 PM UTC
EVENT
Project Pitch Hour
May 10, 2024 at 08:00 PM UTC
EVENT
Lecture 5: Regression III
May 13, 2024 at 07:00 PM UTC
EVENT
Office Hours
May 14, 2024 at 04:00 PM UTC
EVENT
Problem Solving Session 7
May 15, 2024 at 03:00 PM UTC
EVENT
Alternate Problem Session 7
May 15, 2024 at 11:00 PM UTC
EVENT
Lecture 8: Classification I
May 16, 2024 at 07:00 PM UTC
EVENT
Problem Solving Session 9
May 20, 2024 at 03:00 PM UTC
EVENT
Alternate Problem Session 9
May 20, 2024 at 11:00 PM UTC
EVENT
Lecture 10: Ensemble Learning I
May 21, 2024 at 07:00 PM UTC
EVENT
Office Hours
May 22, 2024 at 04:00 PM UTC
EVENT
Office Hours
May 23, 2024 at 04:00 PM UTC
EVENT
Office Hours
May 24, 2024 at 04:00 PM UTC
EVENT
Office Hours
May 29, 2024 at 04:00 PM UTC
EVENT
Erdős May 2024 Final Project Showcase
Jun 5, 2024 at 04:00 PM UTC
EVENT
Lecture 1: Introduction
May 6, 2024 at 07:00 PM UTC
EVENT
Office Hours
May 7, 2024 at 04:00 PM UTC
EVENT
Office Hours
May 8, 2024 at 04:00 PM UTC
EVENT
Problem Solving Session 4
May 9, 2024 at 03:00 PM UTC
EVENT
Alternate Problem Session 4
May 9, 2024 at 11:00 PM UTC
EVENT
Problem Solving Session 5
May 13, 2024 at 03:00 PM UTC
EVENT
Alternate Problem Session 5
May 13, 2024 at 11:00 PM UTC
EVENT
Lecture 6: Time Series I
May 14, 2024 at 07:00 PM UTC
EVENT
Office Hours
May 15, 2024 at 04:00 PM UTC
EVENT
Problem Solving Session 8
May 16, 2024 at 03:00 PM UTC
EVENT
Alternate Problem Session 8
May 16, 2024 at 11:00 PM UTC
EVENT
Office Hours
May 20, 2024 at 04:00 PM UTC
EVENT
Problem Solving Session 10
May 21, 2024 at 03:00 PM UTC
EVENT
Alternate Problem Session 10
May 21, 2024 at 11:00 PM UTC
EVENT
Lecture 11: Ensemble Learning II
May 22, 2024 at 07:00 PM UTC
EVENT
Lecture 12: Neural Networks
May 23, 2024 at 07:00 PM UTC
EVENT
Office Hours
May 27, 2024 at 04:00 PM UTC
EVENT
Office Hours
May 30, 2024 at 04:00 PM UTC
EVENT
Project/Homework Deadlines
May 9, 2024
03:59 AM UTC
Watch 5 Previous Distinguished Projects
Click the "only show projects with distinction or higher" check box, watch five previous projects and explore their githubs.
May 10, 2024
08:00 PM UTC
Project Pitch Hour
Opportunity to meet with other Erdos Fellows and form teams and propose topics.
May 14, 2024
03:59 AM UTC
Submit Team Proposal to Project Formation Page
If you want to propose a project, or have an idea for a project, submit it by this date.
May 15, 2024
03:59 AM UTC
Finalized Teams with Preliminary Project Idea
Teams need to be finalized by this point. If you proposed or created a project, you must have others in your group. If you did not propose or create a project, you must join an open group.
May 17, 2024
02:06 PM UTC
Data gathering and defining stakeholders + KPIs
Find the dataset you will be working with. Describe the dataset and the problem you are looking to solve (1 page max). List the stakeholders of the project and company key performance indicators (KPIs) (bullet points).
May 18, 2024
03:59 AM UTC
Data cleaning + preprocessing
Look for missing values and duplicates. Basic data manipulation & preliminary feature engineering.
May 25, 2024
03:59 AM UTC
Written proposal of modeling approach [Checkpoint]
Test linearity assumptions. Dimensionality reductions (if necessary). Describe your planned modeling approach, based on the exploratory data analysis from the last two weeks (< 1 page, bullet points).
May 25, 2024
03:59 AM UTC
Exploratory data analysis + visualizations [Checkpoint]
Distributions of variables, looking for outliers, etc. Descriptive statistics.
Jun 1, 2024
03:59 AM UTC
Machine learning models or equivalent [Checkpoint]
Results with visualizations and/or metrics. List of successes and pitfalls.
Jun 2, 2024
03:59 AM UTC
Final project due
Please read the submission instructions on the link below.