Data Science Boot Camp
Spring 2026
Jan 26, 2026
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May 1, 2026
Tu/Th 1:30pm - 3:00pm ET
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Lecture 00: Orientation / Computer Setup Day
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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.
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:
By appt. only
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 12
Guard-railing sensitive data
Ammar Eltigani,Aditya Priya,Erick Padilla,Padi Fuster Aguilera
This research project investigates methods for privacy-preserving data release, focusing on survey microdata, de-anonymisation risk, synthetic data generation, and statistical disclosure guarantees. Our goal is to enable open science while safeguarding individual privacy.
TEAM 7
AI Faithfulness in Scientific Reasoning
Awndre Gamache, Hayden Everett
This project asks whether we can predict when a vision language model (VLM) will fail a scientific chart-reasoning question. The data is the CharXiv benchmark which consists of 1,000 charts drawn from arXiv papers, each with a single reasoning question and a pre-graded record of whether a given VLM answered it correctly. For every question-chart pair, we produce a model-agnostic DeLeAn annotation with scores for twelve cognitive-demand dimensions that range from 0 to 5, produced by a verdict-blind process that never sees the correct answer. We predict failure for the GPT-4o, Sonnet 3.5, and GPT-4o random models, with a separate classifier for each.
First Steps/Prerequisites
- Base level familiarity with Python
- Differential calculus. Ideally you also know some multivariate differential calculus and linear algebra.
- Basic statistics and probability
Program Content
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Course materials are available on github through the following link:
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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!
Math Hour 05: Generalized Linear Models
Math Hour
Exponential Families, MLE estimates of model parameters for Generalized Linear Models with canonical link function.
Live Lecture 07: Inference II
Live Lectures
F-test for nested linear models, bootstrapping
Live Lecture 09: Time Series II
Live Lectures
Stationarity, autocovariance, moving average models, autoregressive models, SARIMA.
Math Hour 10: Kernel Regression
Math Hour
Development of Kernel Regression.
Live Lecture 06: Inference I
Live Lectures
Hypothesis testing framework (null vs. alternative, size, power, p-values, etc), confidence intervals, F-test for linear models.
Math Hour 07: Linear Regression Confidence Intervals
Math Hour
Confidence intervals for linear regression model parameters, conditional outcome means, conditional predictions.
Math Hour 09: The Kernel Trick
Math Hour
We motivate positive definite kernels in a machine learning context and prove some elementary facts about them.
Live Lecture 11: Ensembles II
Live Lectures
Adaboost, Gradient Boosting, XGBoost. Boosting is iteratively training models to correct the mistakes of the old model. Each individual model is a weak learner, but the ensemble is strong.
Math Hour 06: F-test for nested linear models
Math Hour
We give a geometrically motivated proof of the F-test for nested linear models.
Live Lecture 08: Time Series I
Live Lectures
Time Series splits, baseline models, rolling averages, exponential smoothing models.
Live Lecture 10: Ensemble I
Live Lectures
Decision Trees, CART algorithm, Random Forests, Bagging/Pasting, Voter Models.
Math Hour 11: Convex Optimization and SVC
Math Hour
Convex Optimization basics (lagrangians, lagrange duals, KKT conditions), good start on Support Vector Classifiers in the linearly separable case.
Project/Homework Instructions
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Schedule
Click on any date for more details
Orientation & Setup Week: Jan 26 - 30, 2026
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 for Certificate Holders: Mar 27 - May 1, 2026
Lecture 00: Orientation / Computer Setup Day
Jan 29, 2026 at 06:30 PM UTC
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Lecture 01: Supervised Learning
Feb 3, 2026 at 06:30 PM UTC
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Lecture 02: Model Evaluation
Feb 5, 2026 at 06:30 PM UTC
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Problem Session 02
Feb 9, 2026 at 06:30 PM UTC
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Problem Session 03
Feb 11, 2026 at 06:30 PM UTC
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Problem Session 04
Feb 16, 2026 at 06:30 PM UTC
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Problem Session 05
Feb 18, 2026 at 06:30 PM UTC
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Problem Session 06
Feb 23, 2026 at 06:30 PM UTC
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Problem Session 07
Feb 25, 2026 at 06:30 PM UTC
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Problem Session 08
Mar 2, 2026 at 06:30 PM UTC
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Problem Session 09
Mar 4, 2026 at 06:30 PM UTC
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Problem Session 10
Mar 9, 2026 at 05:30 PM UTC
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Problem Session 11
Mar 11, 2026 at 05:30 PM UTC
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Office Hour
Mar 17, 2026 at 05:30 PM UTC
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Office Hour
Mar 20, 2026 at 07:00 PM UTC
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Math Hour 00
Feb 2, 2026 at 03:00 PM UTC
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Math Hour 01
Feb 4, 2026 at 03:00 PM UTC
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Project Pitch Hour
Feb 6, 2026 at 09:00 PM UTC
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Lecture 03: Complexity Control
Feb 10, 2026 at 06:30 PM UTC
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Lecture 04: Linear Regression
Feb 12, 2026 at 06:30 PM UTC
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Lecture 05: Generalized Linear Models and Generalized Additive Models
Feb 17, 2026 at 06:30 PM UTC
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Lecture 06: Inference I
Feb 19, 2026 at 06:30 PM UTC
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Lecture 07: Inference II
Feb 24, 2026 at 06:30 PM UTC
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Lecture 08: Time Series I
Feb 26, 2026 at 06:30 PM UTC
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Lecture 09: Time Series II
Mar 3, 2026 at 06:30 PM UTC
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Lecture 10: Ensemble Learning I
Mar 5, 2026 at 06:30 PM UTC
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Lecture 11: Ensemble Learning II
Mar 10, 2026 at 05:30 PM UTC
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Lecture 12: Introduction to Neural Networks
Mar 12, 2026 at 05:30 PM UTC
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Office Hour
Mar 18, 2026 at 05:30 PM UTC
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Data Science Project Showcase
Apr 22, 2026 at 04:00 PM UTC
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Problem Session 00
Feb 2, 2026 at 06:30 PM UTC
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Problem Session 01
Feb 4, 2026 at 06:30 PM UTC
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Math Hour 02
Feb 9, 2026 at 03:00 PM UTC
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Math Hour 03
Feb 11, 2026 at 03:00 PM UTC
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Math Hour 04
Feb 16, 2026 at 03:00 PM UTC
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Math Hour 05
Feb 18, 2026 at 03:00 PM UTC
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Math Hour 06
Feb 23, 2026 at 03:00 PM UTC
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Math Hour 07
Feb 25, 2026 at 03:00 PM UTC
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Math Hour 08: Cancelled
Mar 2, 2026 at 03:00 PM UTC
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Math Hour 09
Mar 4, 2026 at 03:00 PM UTC
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Math Hour 10
Mar 9, 2026 at 02:00 PM UTC
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Math Hour 11
Mar 11, 2026 at 02:00 PM UTC
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Office Hour
Mar 16, 2026 at 05:30 PM UTC
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Office Hour
Mar 19, 2026 at 05:30 PM UTC
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Project/Homework Deadlines
Jan 30, 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 4, 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 4, 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 11, 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 11, 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 13, 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 20, 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 27, 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 6, 2026
04:59 AM UTC
Modeling and Preliminary Results
Results with visualizations and/or metrics. List of successes and pitfalls.
Mar 13, 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 22, 2026
03:59 AM UTC
Final Project Deadline
Submit your final project by this time.