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Data Science Boot Camp

Spring 2025

Jan 23, 2025

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Apr 30, 2025

This program is included with Spring 2025 Career Launch Cohort Enrollment and Erdős Institute Alumni Club Membership at no additional cost.
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Registration Deadlines

Jan 29, 2025

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All Erdős Spring 2025 Career Launch Cohort or Alumni Club members who are not participating in the UX Research nor Deep Learning Boot Camps

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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.

Our goal is to provide you with the skills and mentorship necessary to produce a portfolio worthy data science project.

We will learn the fundamentals of data science including: data collection, data cleaning, exploratory data analysis, inferential statistics, supervised and unsupervised machine learning techniques, and the basics of neural networks.

Each week of the course we will have a live lecture, a problem session, and an optional "math hour" and office hour.

In order to receive a Data Science certificate you must complete a portfolio worthy project in collaboration with a team of your peers.

Slack

Click here to be invited to the slack organization: The Erdős Institute

Click here to access the slack channel: #slack-channel

Organizers, Instructors, and Advisors

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Steven Gubkin, PhD

Lead Instructor

Office Hours:

W 11am - 12pm and by appt.

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 5

Predicting Problematic Internet Use

Daniel Visscher, Emilie Wiesner, Aaron Weinberg

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github URL

Internet use has been identified by researchers as having the potential to rise to the level of addiction, with associated increased rates of anxiety and depression. Identifying cases of problematic internet usage currently requires evaluation by an expert, however, which is a significant impediment to screening children and adolescents across society. One potential solution is to rely on data that is more easily and uniformly collected: the kind collected by a family physician, a simple survey, or by a smartwatch. The research question this project sets out to answer is: “Can we predict the level of problematic internet usage exhibited by children and adolescents, based on their physical activity and survey responses?”

TEAM 26

Continuous Glucose Monitoring

Daniel Visscher,Margaret Swerdloff,Noah Gillespie,S. C. Park,oladimeji olaluwoye

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github URL

The idea of the project is to predict high glucose spikes from continuous glucose data, smartwatch data, food logs, and glycemic index. The dataset consists of the following:
1) Tri-axial accelerometer data (movement in subject)
2) Blood volume pulse
3) Intestinal glucose concentration
4) Electrodermal activity
5) Heart rate
6) IBI (interbeat interval)
7) Skin temperature
8) Food log
Data is public in: https://physionet.org/content/big-ideas-glycemic-wearable/1.1.2/#files-panel

First Steps/Prerequisites

Computer Setup Day/First Steps
There are some computer set up steps you need to complete before the first lecture. We will meet on 01/23/2025 on Zoom from 2pm to 3:30pm ET to make sure that we have all done the following:
  1. Cloned the GitHub repo locally
  2. Installed the conda environment.
  3. Run a Jupyter Notebook using that conda environment.
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

DSBC Orientation

Live Lectures

Cohort Orientation.

Slides
Transcript
Code

Project/Homework Instructions

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Project/Team Formation
Project Submission
Projects README

Schedule

Click on any date for more details

DS Bootcamp computer setup day

Jan 23, 2025 at 07:00 PM UTC

EVENT

Office Hour 01

Jan 29, 2025 at 04:00 PM UTC

EVENT

Math Hour 02

Feb 5, 2025 at 03:00 PM UTC

EVENT

Lecture 03: Regression II

Feb 11, 2025 at 05:00 PM UTC

EVENT

Problem Session 03

Feb 13, 2025 at 07:00 PM UTC

EVENT

Math Hour 04

Feb 19, 2025 at 03:00 PM UTC

EVENT

Lecture 05: Inference I

Feb 25, 2025 at 05:00 PM UTC

EVENT

Problem Session 05

Feb 27, 2025 at 07:00 PM UTC

EVENT

Office Hour 06

Mar 5, 2025 at 04:00 PM UTC

EVENT

Math Hour 07

Mar 12, 2025 at 03:00 PM UTC

EVENT

Lecture 08: Classification I

Mar 18, 2025 at 05:00 PM UTC

EVENT

Problem Session 08

Mar 20, 2025 at 07:00 PM UTC

EVENT

Office Hour 09

Mar 26, 2025 at 04:00 PM UTC

EVENT

Math Hour 10

Apr 2, 2025 at 03:00 PM UTC

EVENT

Lecture 11: Ensemble Learning II

Apr 8, 2025 at 05:00 PM UTC

EVENT

Problem Session 11

Apr 10, 2025 at 07:00 PM UTC

EVENT

Office Hour 12

Apr 16, 2025 at 04:00 PM UTC

EVENT

Lecture 01: Introduction, Computer Setup, Q/A

Jan 28, 2025 at 05:00 PM UTC

EVENT

Problem Session 01

Jan 30, 2025 at 07:00 PM UTC

EVENT

Office Hour 02

Feb 5, 2025 at 04:00 PM UTC

EVENT

Math Hour 03

Feb 12, 2025 at 03:00 PM UTC

EVENT

Project Pitch Hour

Feb 17, 2025 at 10:00 PM UTC

EVENT

Office Hour 04

Feb 19, 2025 at 04:00 PM UTC

EVENT

Math Hour 05

Feb 26, 2025 at 03:00 PM UTC

EVENT

Lecture 06: Inference II

Mar 4, 2025 at 05:00 PM UTC

EVENT

Problem Session 06

Mar 6, 2025 at 07:00 PM UTC

EVENT

Office Hour 07

Mar 12, 2025 at 04:00 PM UTC

EVENT

Math Hour 08

Mar 19, 2025 at 03:00 PM UTC

EVENT

Lecture 09: Classification II

Mar 25, 2025 at 05:00 PM UTC

EVENT

Problem Session 09

Mar 27, 2025 at 07:00 PM UTC

EVENT

Office Hour 10

Apr 2, 2025 at 04:00 PM UTC

EVENT

Math Hour 11

Apr 9, 2025 at 03:00 PM UTC

EVENT

Lecture 12: Introduction to Neural Networks

Apr 15, 2025 at 05:00 PM UTC

EVENT

Problem Session 12

Apr 24, 2025 at 06:00 PM UTC

EVENT

Math Hour 01

Jan 29, 2025 at 03:00 PM UTC

EVENT

Lecture 02: Regression I

Feb 4, 2025 at 05:00 PM UTC

EVENT

Problem Session 02

Feb 6, 2025 at 07:00 PM UTC

EVENT

Office Hour 03

Feb 12, 2025 at 04:00 PM UTC

EVENT

Lecture 04: Regression III

Feb 18, 2025 at 05:00 PM UTC

EVENT

Problem Session 04

Feb 20, 2025 at 07:00 PM UTC

EVENT

Office Hour 05

Feb 26, 2025 at 04:00 PM UTC

EVENT

Math Hour 06

Mar 5, 2025 at 03:00 PM UTC

EVENT

Lecture 07: Time Series

Mar 11, 2025 at 05:00 PM UTC

EVENT

Problem Session 07

Mar 13, 2025 at 07:00 PM UTC

EVENT

Office Hour 08

Mar 19, 2025 at 04:00 PM UTC

EVENT

Math Hour 09

Mar 26, 2025 at 03:00 PM UTC

EVENT

Lecture 10: Ensemble Learning I

Apr 1, 2025 at 05:00 PM UTC

EVENT

Problem Session 10

Apr 3, 2025 at 07:00 PM UTC

EVENT

Office Hour 11

Apr 9, 2025 at 04:00 PM UTC

EVENT

Math Hour 12

Apr 16, 2025 at 03:00 PM UTC

EVENT

Commencement and Project Showcase

Apr 30, 2025 at 04:00 PM UTC

EVENT

Please check your registration email for program schedule and zoom links.

Project/Homework Deadlines

Feb 7, 2025

04:59 PM UTC

Watch video about Project Formation

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

Feb 7, 2025

04:59 PM UTC

Watch 3 Previous Top Projects

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

Feb 17, 2025

10:00 PM UTC

Project Pitch Hour

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

Feb 21, 2025

04:59 PM 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 21, 2025

04:59 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).

Mar 7, 2025

04:59 PM UTC

Exploratory data analysis + visualizations [Checkpoint]

Distributions of variables, looking for outliers, etc. Descriptive statistics.

Mar 7, 2025

04:59 PM UTC

Data cleaning + preprocessing

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

Mar 22, 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).

Mar 29, 2025

03:59 AM UTC

Machine learning models or equivalent [Checkpoint]

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

Apr 22, 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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