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Data Science Boot Camp

Spring 2026

Jan 26, 2026

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May 1, 2026

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Registration Deadlines

Jan 21, 2026

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All Erdős Spring 2026 Career Launch Cohort or Alumni Club members who are not participating in another Launch bootcamp

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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 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.

TEAM 29

Who Regulates the Regulators?

Jared Able, Joshua Jackson, Zachary Brennan, Alexandria Wheeler, Nicholas Geiser

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github URL

With recent major cuts to governmental regulation agencies in the US, we investigate whether those cuts are justified. In particular, we analyze the efficacy of RGGI, a state-level cap-and-trade program designed to regulate CO2 emissions in power plants. By using synthetic controls, we answer the counterfactual question: "how would CO2 emissions look in a world where RGGI was never enacted?".

First Steps/Prerequisites

Course Orientation / Computer Setup Day
Our first meeting is Thursday, January 29th, 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
 
lectures/00_orientation/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!

(Prerecorded) L3E2: Regularization

Lecture 03: Complexity Control (prerecorded)

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

Slides
Transcript
Code

(Prerecorded) L4E1: Simple Linear Regression

Lecture 04: Regression I (prerecorded)

We introduce the simple linear regression model.

Slides
Code

(Prerecorded) L4E1: Categorical Variables and Interactions

Lecture 04: Regression I (prerecorded)

Adjusting our regression set up to accommodate categorical variables.

Slides
Code

(Prerecorded) L4E1: Basic Pipelines

Lecture 04: Regression I (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

(Prerecorded) L3E3: PCA

Lecture 03: Complexity Control (prerecorded)

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

Slides
Transcript
Code

(Prerecorded) L4E1: A First Predictive Modeling Project

Lecture 04: Regression I (prerecorded)

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

Slides
Code

(Prerecorded) L4E1: Polynomial Regression and Nonlinear Transformations

Lecture 04: Regression I (prerecorded)

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

Slides
Code

(Prerecorded) L8E1: What are Time Series and Forecasting

Lecture 08: Time Series I Control (prerecorded)

We introduce the notions of time series data and forecasting.

Slides
Code

(Prerecorded) L3E4: Hyperparameter Tuning

Lecture 03: Complexity Control (prerecorded)

Hyperparameter tuning and nested cross-validation.

Slides
Transcript
Code

(Prerecorded) L4E1: Multiple Linear Regression

Lecture 04: Regression I (prerecorded)

We introduce multiple linear regression.

Slides
Code

(Prerecorded) L4E1: Scaling Data

Lecture 04: Regression I (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

(Prerecorded) L8E2: Adjustments for Time Series Data

Lecture 08: Time Series I Control (prerecorded)

Since time series are ordered, we need a slight adjustment to our cross-validation strategy.

Slides
Code

Project/Homework Instructions

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Project/Team Formation
Project Submission
Projects README

Schedule

Click on any date for more details

Phase 1 - Instruction and Project Completion: Feb 02 - Mar 20, 2026
Project Review & Judging: Mar 23 - Mar 26, 2026
Phase 2 - Intense Interview Prep & Career Connections: Mar 27 - May 1, 2026

Lecture 00: Orientation / Computer Setup Day

Jan 29, 2026 at 06:30 PM UTC

EVENT

Lecture 01: Supervised Learning

Feb 3, 2026 at 06:30 PM UTC

EVENT

Lecture 02: Model Evaluation

Feb 5, 2026 at 06:30 PM UTC

EVENT

Problem Session 02

Feb 9, 2026 at 06:30 PM UTC

EVENT

Problem Session 03

Feb 11, 2026 at 06:30 PM UTC

EVENT

Problem Session 04

Feb 16, 2026 at 06:30 PM UTC

EVENT

Problem Session 05

Feb 18, 2026 at 06:30 PM UTC

EVENT

Problem Session 06

Feb 23, 2026 at 06:30 PM UTC

EVENT

Problem Session 07

Feb 25, 2026 at 06:30 PM UTC

EVENT

Problem Session 08

Mar 2, 2026 at 06:30 PM UTC

EVENT

Problem Session 09

Mar 4, 2026 at 06:30 PM UTC

EVENT

Problem Session 10

Mar 9, 2026 at 05:30 PM UTC

EVENT

Problem Session 11

Mar 11, 2026 at 05:30 PM UTC

EVENT

Math Hour 00

Feb 2, 2026 at 03:00 PM UTC

EVENT

Math Hour 01

Feb 4, 2026 at 03:00 PM UTC

EVENT

Project Pitch Hour

Feb 6, 2026 at 09:00 PM UTC

EVENT

Lecture 03: Complexity Control

Feb 10, 2026 at 06:30 PM UTC

EVENT

Lecture 04: Linear Regression

Feb 12, 2026 at 06:30 PM UTC

EVENT

Lecture 05: Generalized Linear Models and Generalized Additive Models

Feb 17, 2026 at 06:30 PM UTC

EVENT

Lecture 06: Inference I

Feb 19, 2026 at 06:30 PM UTC

EVENT

Lecture 07: Inference II

Feb 24, 2026 at 06:30 PM UTC

EVENT

Lecture 08: Time Series I

Feb 26, 2026 at 06:30 PM UTC

EVENT

Lecture 09: Time Series II

Mar 3, 2026 at 06:30 PM UTC

EVENT

Lecture 10: Ensemble Learning I

Mar 5, 2026 at 06:30 PM UTC

EVENT

Lecture 11: Ensemble Learning II

Mar 10, 2026 at 05:30 PM UTC

EVENT

Lecture 12: Introduction to Neural Networks

Mar 12, 2026 at 05:30 PM UTC

EVENT

Problem Session 00

Feb 2, 2026 at 06:30 PM UTC

EVENT

Problem Session 01

Feb 4, 2026 at 06:30 PM UTC

EVENT

Math Hour 02

Feb 9, 2026 at 03:00 PM UTC

EVENT

Math Hour 03

Feb 11, 2026 at 03:00 PM UTC

EVENT

Math Hour 04

Feb 16, 2026 at 03:00 PM UTC

EVENT

Math Hour 05

Feb 18, 2026 at 03:00 PM UTC

EVENT

Math Hour 06

Feb 23, 2026 at 03:00 PM UTC

EVENT

Math Hour 07

Feb 25, 2026 at 03:00 PM UTC

EVENT

Math Hour 08

Mar 2, 2026 at 03:00 PM UTC

EVENT

Math Hour 09

Mar 4, 2026 at 03:00 PM UTC

EVENT

Math Hour 10

Mar 9, 2026 at 02:00 PM UTC

EVENT

Math Hour 11

Mar 11, 2026 at 02:00 PM UTC

EVENT

Project/Homework Deadlines

Jan 31, 2026

04:59 AM UTC

Last chance to switch bootcamps

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

Feb 5, 2026

04:59 AM UTC

Watch video about Project Formation

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

Feb 5, 2026

04:59 AM UTC

Watch 3 Previous Top Projects

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

Feb 6, 2026

09:00 PM UTC

Project Pitch Hour

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

Feb 12, 2026

04:59 AM UTC

Last day to defer enrollment to a future cohort

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

Feb 12, 2026

04:59 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.

Feb 14, 2026

04:59 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).

Feb 21, 2026

04:59 AM UTC

Data cleaning + preprocessing + EDA

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

Feb 28, 2026

04:59 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).

Mar 7, 2026

04:59 AM UTC

Modeling and Preliminary Results

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

Mar 14, 2026

03:59 AM UTC

Clean your repository and start working on final presentation

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.

Mar 21, 2026

03:59 AM UTC

Final Project Deadline

Submit your final project by this time.

©2017-2025 by The Erdős Institute.

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