Data Science Boot Camp
Fall 2026
Sep 14, 2026
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Dec 18, 2026
Tu/Th 1:30pm - 3:00pm ET
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Lecture 00: Orientation / Computer Setup Day
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Registration Deadlines
Sep 14, 2026
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All Erdős Fall 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!
Live Lecture 05: Complexity Control
Live Lectures
Bias/Variance decomposition, regularization, PCA, hyperparameter tuning, nested CV
Math Hour 06: Confidence Intervals
Math Hours
Confidence intervals for linear regression model parameters, conditional outcome means, prediction intervals.
Lecture 08: Time Series I
Live Lectures
What time series are, time series splits, baseline forecasts, rolling averages, exponential smoothing.
(Prerecorded) L1E1: Workflow Overview
Lecture 01: Supervised Learning (Prerecorded)
Overview of the Data Science workflow (asking the right question, collecting/cleaning/exploring data, modeling, and deployment/reporting). A minimal example using concrete compressive strength.
Math Hour 05: F-test
Math Hours
Statement and proof of the F-test for nested linear models.
Live Lecture 07: Inference II
Live Lectures
Finished up Linear Regression Inference from last time (F-test, confidence intervals for model parameters and model predictions). The remainder of the time was spent discussing the bootstrap.
Math Hour 08: Kernel Regression
Math Hours
Development of kernelized version of linear regression.
(Prerecorded) L1E2: Data Collection
Lecture 01: Supervised Learning (Prerecorded)
A very short video which gives you pointers on where to find additional content on data collection. We do not cover this content in depth in the synchronous lectures.
Live Lecture 06: Inference I
Live Lectures
Hypothesis testing (null, alternative, size, power, p-value), confidence intervals, linear regression inference
Math Hour 07: Kernels
Math Hours
Pointwise Positive Definite Kernels, feature maps, producing new kernels out of old kernels.
Live Lecture 09: Time Series II
Live Lectures
autocorrelation, ARIMA
(Prerecorded) L1E3: Data Cleaning and EDA
Lecture 01: Supervised Learning (Prerecorded)
Overview of the basics of data cleaning and exploratory data analysis. Showcases some fundamental tools (pandas, matplotlib, plotly) as well as some more specialized tools (panderas, missingno).
Project/Homework Instructions
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Project Pitch Hour
Project Pitch Hour
Recording of Project Pitch Hour
Schedule
Click on any date for more details
Orientation & Setup Week: Sep 14 - 18, 2026
Phase 1 - Instruction and Project Completion: Sep 21 - Nov 06, 2026
Project Review & Judging: Nov 09 - Nov 12, 2026
Phase 2 - Intense Interview Prep & Career Connections for Certificate Holders: Nov 13 - Dec 18, 2026
Lecture 00: Orientation / Computer Setup Day
Sep 17, 2026 at 05:30 PM UTC
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Lecture 01: Supervised Learning
Sep 22, 2026 at 05:30 PM UTC
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Lecture 02: Model Evaluation
Sep 24, 2026 at 05:30 PM UTC
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Problem Session 02
Sep 28, 2026 at 05:30 PM UTC
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Problem Session 03
Sep 30, 2026 at 05:30 PM UTC
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Problem Session 04
Oct 5, 2026 at 05:30 PM UTC
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Problem Session 05
Oct 7, 2026 at 05:30 PM UTC
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Problem Session 06
Oct 12, 2026 at 05:30 PM UTC
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Problem Session 07
Oct 14, 2026 at 05:30 PM UTC
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Problem Session 08
Oct 19, 2026 at 05:30 PM UTC
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Problem Session 09
Oct 21, 2026 at 05:30 PM UTC
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Problem Session 10
Oct 26, 2026 at 05:30 PM UTC
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Problem Session 11
Oct 28, 2026 at 05:30 PM UTC
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Math Hour 00
Sep 21, 2026 at 02:00 PM UTC
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Math Hour 01
Sep 23, 2026 at 02:00 PM UTC
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Project Pitch Hour
Sep 25, 2026 at 08:00 PM UTC
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Lecture 03: Linear Regression
Sep 29, 2026 at 05:30 PM UTC
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Lecture 04: Generalized Linear Models
Oct 1, 2026 at 05:30 PM UTC
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Lecture 05: Complexity Control
Oct 6, 2026 at 05:30 PM UTC
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Lecture 06: Inference I
Oct 8, 2026 at 05:30 PM UTC
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Lecture 07: Inference II
Oct 13, 2026 at 05:30 PM UTC
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Lecture 08: Time Series I
Oct 15, 2026 at 05:30 PM UTC
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Lecture 09: Time Series II
Oct 20, 2026 at 05:30 PM UTC
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Lecture 10: Ensemble Learning I
Oct 22, 2026 at 05:30 PM UTC
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Lecture 11: Ensemble Learning II
Oct 27, 2026 at 05:30 PM UTC
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Lecture 12: Introduction to Neural Networks
Oct 29, 2026 at 05:30 PM UTC
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Problem Session 00
Sep 21, 2026 at 05:30 PM UTC
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Problem Session 01
Sep 23, 2026 at 05:30 PM UTC
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Math Hour 02
Sep 28, 2026 at 02:00 PM UTC
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Math Hour 03
Sep 30, 2026 at 02:00 PM UTC
EVENT
Math Hour 04
Oct 5, 2026 at 02:00 PM UTC
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Math Hour 05
Oct 7, 2026 at 02:00 PM UTC
EVENT
Math Hour 06
Oct 12, 2026 at 02:00 PM UTC
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Math Hour 07
Oct 14, 2026 at 02:00 PM UTC
EVENT
Math Hour 08
Oct 19, 2026 at 02:00 PM UTC
EVENT
Math Hour 09
Oct 21, 2026 at 02:00 PM UTC
EVENT
Math Hour 10
Oct 26, 2026 at 02:00 PM UTC
EVENT
Math Hour 11
Oct 28, 2026 at 02:00 PM UTC
EVENT
Project/Homework Deadlines
Sep 18, 2026
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.
Sep 23, 2026
03:59 AM UTC
Watch 3 Previous Top Projects
Consult the project database, and watch at least 3 previous top projects from Erdos Alumni.
Sep 23, 2026
03:59 AM UTC
Watch video about Project Formation
This should help answer any Q's you may have going into project formation
Sep 25, 2026
08:00 PM UTC
Project Pitch Hour
Opportunity to meet with other Erdős Fellows and form teams and propose topics.
Sep 30, 2026
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.
Oct 2, 2026
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).
Oct 2, 2026
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 from this cohort and defer to a future cohort.
Oct 9, 2026
03:59 AM UTC
Data cleaning + preprocessing + EDA
Look for missing values and duplicates. Basic data manipulation & preliminary feature engineering. Exploratory data analysis.
Oct 16, 2026
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).
Oct 23, 2026
03:59 AM UTC
Modeling and Preliminary Results
Results with visualizations and/or metrics. List of successes and pitfalls.
Oct 30, 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.
Nov 6, 2026
04:59 AM UTC
Final Project Deadline
Submit your final project by this time.