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Data Science Boot Camp

Summer 2025

May 8, 2025

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Aug 15, 2025

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Lecture 01: Orientation / Computer Setup Day

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Registration Deadlines

May 8, 2025

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Summer 2025 Cohort participants

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Category

Launch, Core Program, Boot Camp, Projects, Certificates

Overview

In this bootcamp, we will develop the skills needed to complete a data science project from start to finish. This includes defining a problem in quantitative terms, identifying key performance indicators (KPIs), acquiring and cleaning data, exploring patterns and trends, and transforming raw data into meaningful variables. We will then build models for prediction and inference, focusing primarily on supervised learning methods for regression and classification.

Slack

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

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Click here to download the Events & Deadlines .ics calendar file

Organizers, Instructors, and Advisors

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Steven Gubkin, PhD

Lead Instructor

Office Hours:

By appt. only

Email:

Preferred Contact:

Slack

Please feel free to message me on Slack with any questions!

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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 16

Predicting Lead Contamination in NY School Drinking Water

Ranadeep Roy,Cami Goray,Hana Lang

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github URL

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.

TEAM 33

Tuning Up Music Highway

James O'Quinn, Yang Mo, john hurtado cadavid, Ruixuan Ding, Chilambwe Natasha Wapamenshi

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github URL

Known as the most dangerous highway in Tennessee, Music Highway, the stretch of Interstate 40 between Memphis and Nashville, could use a serious tuning up. This project investigates the effectiveness and cost-efficiency of potential physical safety interventions along its Madison and Henderson County segments, with the goal of reducing crash severity. We used a data-driven geospatial modeling approach to assess whether adding specific safety features to targeted segments predicts statistically significant changes in crash injury outcomes.

First Steps/Prerequisites

Course Orientation / Computer Setup Day
Our first meeting is Thursday, May 8, from 1:30 PM - 3:00 PM ET. I will give a brief orientation to the course. The remainder of the time will be spent on the following very simple goal: to clone the repo, install the conda environment, and use that conda environment to run a Jupyter notebook. It is impossible to participate in the course without these abilities, so it is important to attend this session. If you can do these things, please show up to help the other participants!
 
Detailed instructions (created by teaching assistant Ness Mayker Chen) can be found at this link.
 
We will test your ability to do these things by having you submit a "secret code". You will obtain this code by successfully running the notebook
 
computer_setup_day/find_secret_code.ipynb
 
When you have obtained the code put it in the textbox at https://www.erdosinstitute.org/ds-boot-camp-prep
 
If you can do these things independently please show up to help your colleagues!
If you cannot do these things independently please show up to get help from your colleagues!
 
Prerequisites
 
In addition to these computer setup steps there are also some content prerequisites:
  1. Base level familiarity with Python
  2. Differential calculus. Ideally you also know some multivariate differential calculus and linear algebra.
  3. Basic statistics and probability

Program Content

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Course materials are available on github through the following link:

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Request Access to GitHub

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Program Content

Textbook/Notes

Note: our video player does not support playback speed options. You can find a third party browser extension which will allow you to modify video playback speed. For example, this one works for Chrome: video-speed-controller. If you would prefer to avoid a browser extension you can manually modify the playback speed in the javascript console as well: Speed up any HTML5 video player!

A First Predictive Modeling Project

01 Week: Regression I (prerecorded)

We review a typical workflow for a predictive modeling project with some baseball data.

Slides
Code

Polynomial Regression and Nonlinear Transformations

02 Week: Regression II (prerecorded)

We can make polynomial and nonlinear transformations of our features to make additional regression model types.

Slides
Code

Linear Regression Diagnostic Plots

02 Week: Regression II (prerecorded)

Residual plots can help us better understand our linear regression model.

Slides
Code

PCA

03 Week: Regression III (prerecorded)

We introduce the theory behind principal components analysis and demonstrate how it is implemented in python.

Slides
Code

Multiple Linear Regression

01 Week: Regression I (prerecorded)

We introduce multiple linear regression.

Slides
Code

Scaling Data

02 Week: Regression II (prerecorded)

Having features with very different scales can impact the performance of some models. In this video we show how to use sklearn's StandardScaler object to standardize our features.

Slides
Code

Bias-Variance Trade-Off

03 Week: Regression III (prerecorded)

The expected generalization error of a learning algorithm can be decomposed into two terms: the bias and the variance.

Slides
Code

PCA and Basketball

Bonus content (prerecorded)

A cool PCA example where the components have an interesting interpretation.

Slides
Code

Categorical Variables and Interactions

02 Week: Regression II (prerecorded)

Adjusting our regression set up to accommodate categorical variables.

Slides
Code

Basic Pipelines

02 Week: Regression II (prerecorded)

Pipelines are a nice way to put all modeling steps into one neat package. Here we introduce the most basic pipeline creation method.

Slides
Code

Regularization

03 Week: Regression III (prerecorded)

Regularization adds a term to our loss function which penalizes large parameters. This can help prevent overfitting.

Slides
Code

Feature Selection Approaches

03 Week: Regression III (prerecorded)

We review a few different approaches to choosing what features to include in a linear regression model.

Slides
Code

Project/Homework Instructions

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Project/Team Formation
Project Submission
Projects README

Schedule

Click on any date for more details

Orientation & Setup Week: May 8 - May 12, 2025
Phase 1 - Instruction and Project Completion: May 13 - Jul 2, 2025
Project Review & Judging: Jul 3 - Jul 9, 2025
Phase 2 - Intense Interview Prep & Career Connections for Certificate Holders: Jul 10 - Aug 15, 2025

Lecture 01: Orientation / Computer Setup Day

May 8, 2025 at 05:30 PM UTC

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Problem Session 02

May 14, 2025 at 05:00 PM UTC

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Problem Session 03

May 16, 2025 at 05:00 PM UTC

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Math Hour 04

May 21, 2025 at 02:00 PM UTC

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Math Hour 05

May 23, 2025 at 02:00 PM UTC

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Math Hour 06

May 28, 2025 at 02:00 PM UTC

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Math Hour 07

May 30, 2025 at 02:00 PM UTC

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Cancelled: Math Hour 08

Jun 4, 2025 at 02:00 PM UTC

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Math Hour 09

Jun 6, 2025 at 02:00 PM UTC

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Math Hour 10

Jun 11, 2025 at 02:00 PM UTC

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Math Hour 11

Jun 13, 2025 at 02:00 PM UTC

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Math Hour 12

Jun 18, 2025 at 02:00 PM UTC

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Math Hour 13

Jun 20, 2025 at 02:00 PM UTC

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DS Practice Interview, Option A (Link in Slack Channel)

Jul 10, 2025 at 04:00 PM UTC

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Project Showcase and Commencement

Jul 15, 2025 at 04:00 PM UTC

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DS Practice Interview, Option B

Jul 18, 2025 at 12:00 AM UTC

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DS Phase II Office Hour

Jul 23, 2025 at 06:00 PM UTC

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DS Practice Interview, Option C

Jul 25, 2025 at 01:00 PM UTC

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DS Practice Interview, Option B

Aug 1, 2025 at 12:00 AM UTC

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DS Practice Interview, Option A

Aug 7, 2025 at 04:00 PM UTC

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DS Phase II Office Hour

Aug 13, 2025 at 06:00 PM UTC

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DS Practice Interview, Option C (Link in Slack Channel)

Aug 15, 2025 at 01:00 PM UTC

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Lecture 02: Regression I

May 13, 2025 at 05:30 PM UTC

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Lecture 03: Regression II

May 15, 2025 at 05:30 PM UTC

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Project Pitch Hour

May 16, 2025 at 08:00 PM UTC

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Problem Session 04

May 21, 2025 at 05:00 PM UTC

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Problem Session 05

May 23, 2025 at 05:00 PM UTC

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Problem Session 06

May 28, 2025 at 05:00 PM UTC

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Problem Session 07

May 30, 2025 at 05:00 PM UTC

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Problem Session 08

Jun 4, 2025 at 05:00 PM UTC

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Problem Session 09

Jun 6, 2025 at 05:00 PM UTC

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Problem Session 10

Jun 11, 2025 at 05:00 PM UTC

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Problem Session 11

Jun 13, 2025 at 05:00 PM UTC

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Problem Session 12

Jun 18, 2025 at 05:00 PM UTC

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Problem Session 13

Jun 20, 2025 at 05:00 PM UTC

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DS Practice Interview, Option B (Link in Slack Channel)

Jul 11, 2025 at 12:00 AM UTC

EVENT

DS Phase II Office Hour

Jul 16, 2025 at 06:00 PM UTC

EVENT

DS Practice Interview, Option C

Jul 18, 2025 at 01:00 PM UTC

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DS Practice Interview, Option A

Jul 24, 2025 at 04:00 PM UTC

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DS Phase II Office Hour

Jul 30, 2025 at 06:00 PM UTC

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DS Practice Interview, Option C

Aug 1, 2025 at 01:00 PM UTC

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DS Practice Interview, Option B

Aug 8, 2025 at 12:00 AM UTC

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DS Practice Interview, Option A (Link in Slack Channel)

Aug 14, 2025 at 04:00 PM UTC

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DS Phase II Office Hour

Aug 20, 2025 at 06:00 PM UTC

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Math Hour 02

May 14, 2025 at 02:00 PM UTC

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Math Hour 03

May 16, 2025 at 02:00 PM UTC

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Lecture 04: Regression III

May 20, 2025 at 05:30 PM UTC

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Lecture 05: Inference I

May 22, 2025 at 05:30 PM UTC

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Lecture 06: Inference II

May 27, 2025 at 05:30 PM UTC

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Lecture 07: Time Series I

May 29, 2025 at 05:30 PM UTC

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Lecture 08: Time Series II

Jun 3, 2025 at 05:30 PM UTC

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Lecture 09: Classification I

Jun 5, 2025 at 05:30 PM UTC

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Lecture 10: Classification II

Jun 10, 2025 at 05:30 PM UTC

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Lecture 11: Ensemble Learning I

Jun 12, 2025 at 05:30 PM UTC

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Cancelled: Lecture 12 ( Ensemble Learning II )

Jun 17, 2025 at 05:30 PM UTC

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Lecture 13: Introduction to Neural Networks

Jun 19, 2025 at 05:30 PM UTC

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DS Technical Interview Prep Overview

Jul 7, 2025 at 08:00 PM UTC

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DS Practice Interview, Option C (Link in Slack Channel)

Jul 11, 2025 at 01:00 PM UTC

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DS Practice Interview, Option A

Jul 17, 2025 at 04:00 PM UTC

EVENT

DS Phase II Office Hour

Jul 23, 2025 at 06:00 PM UTC

EVENT

DS Practice Interview, Option B

Jul 25, 2025 at 12:00 AM UTC

EVENT

DS Practice Interview, Option A

Jul 31, 2025 at 04:00 PM UTC

EVENT

DS Phase II Office Hour

Aug 6, 2025 at 06:00 PM UTC

EVENT

DS Practice Interview, Option C

Aug 8, 2025 at 01:00 PM UTC

EVENT

DS Practice Interview, Option B (Link in Slack Channel)

Aug 15, 2025 at 12:00 AM UTC

EVENT

Project/Homework Deadlines

May 8, 2025

03:59 AM UTC

Last chance to switch bootcamps

Email Amalya Lehmann at amalya@erdosinstitute.org if you would like to switch to a different bootcamp.

May 15, 2025

03:59 AM UTC

Watch 3 Previous Top Projects

Consult the project database, and watch at least 3 previous top projects from Erdos Alumni.

May 15, 2025

03:59 AM UTC

Watch video about Project Formation

This should help answer any Q's you may have going into project formation

May 16, 2025

08:00 PM UTC

Project Pitch Hour

Opportunity to meet with other Erdős Fellows and form teams and propose topics.

May 20, 2025

03:59 AM UTC

Last day to defer enrollment to a future cohort

Contact Amalya Lehmann (amalya@erdosinstitute.org) if you would like to unenroll this cohort and defer to a future cohort.

May 20, 2025

03:59 AM UTC

Finalized Teams with Preliminary Project Ideas

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 24, 2025

03:59 AM 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 31, 2025

03:59 AM UTC

Data cleaning + preprocessing

Look for missing values and duplicates. Basic data manipulation & preliminary feature engineering.

Jun 7, 2025

03:59 AM UTC

Written proposal of modeling approach [Checkpoint]

Describe your planned modeling approach, based on the exploratory data analysis from the last two weeks (< 1 page, bullet points).

Jun 14, 2025

03:59 AM UTC

Preliminary Results

Results with visualizations and/or metrics. List of successes and pitfalls.

Jun 21, 2025

03:59 AM UTC

Clean your repository

Clean up your repository so that an outsider can easily follow your work. Convert notebooks into scripts where possible. Confirm that the whole pipeline from data ingestion all the way to prediction or inference works without fuss.

Jun 28, 2025

03:59 AM UTC

Final Projects Due

Final Projects must be submitted by this deadline in order to receive a certificate of completion.

©2017-2025 by The Erdős Institute.

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