top of page
Data Science Boot Camp

Fall 2023

Sep 7, 2023

-

Dec 7, 2023

I'm a paragraph. Click here to add your own text and edit me. It's easy.

erdosOspin.gif

Checking your registration status...

To access the program content, you must first create an account and member profile and be logged in.

Register

You are registered for this program.

Registration Deadlines

Oct 1, 2023

-

Academics from Member Institutions/Departments

Sep 30, 2023

-

Academics from Non-Member Institutions paying the $500 cohort membership fee

Sep 30, 2023

-

Academics from Non-Member Institutions applying for Corporate Sponsored Fellowships

Category

Launch

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. Due to its popularity, we now offer our boot camp online twice per year in two different formats: a 1-month long intensive boot camp each May and a semester long version each Fall.

Slack

#slack-channel

Organizers, Instructors, and Advisors

matt_osborne.png

Alec Clott, PhD

Head of Data Science Projects

Office Hours:

Wed. 12-12:30pm EST, and by appt.

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!

matt_osborne.png

Matthew Osborne, PhD

Lead Instructor, Senior Operations Analyst

Office Hours:

By appointment only

Email:

Preferred Contact:

Slack

Don't hesitate to contact me with any questions or concerns.

matt_osborne.png

Steven Gubkin, PhD

Lead Teaching Assistant

Office Hours:

Tu: 11am - 12pm ET

Email:

Preferred Contact:

Slack

Please feel free to message me on Slack with any questions! I will also be running a “math hour” every Wednesday from 11am - 12pm ET which will explore the mathematical underpinnings of the techniques covered in the previous lecture.

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

Aware NLP Project III

Mohammad Nooranidoost, Baian Liu, Craig Franze, Mustafa Anıl Tokmak, Himanshu Raj, Peter Williams

clear.png
Screen Shot 2022-06-03 at 11.31.35 AM.png
github URL

This project involves the investigation and evaluation of different methodologies for retrieval for use in RAG (Retrieval-Augmented Generation) systems. In particular, this project investigates retrieval quality for information downloaded from employee subreddits. We investigated the impacts of using clustering, multi-vector indexing, and multi-querying in advanced retrieval methodologies against baseline naive retrieval.

First Steps/Prerequisites

Participants should have a base-level familiarity with Python. Participants should also be familiar with some basic math concepts. Finally, you will also need to have your laptop or desktop computer set up for the course. If you are new to Python, need a quick math refresher, or if you need help setting up your computer, then please follow the link below.

Program Content

I'm a paragraph. Click here to add your own text and edit me. It's easy.

25231-github-cat-in-a-circle-icon-vector-icon-vector-eps.png
Program Content

Textbook/Notes

Welcome!

Introduction

In this video we welcome you to our data science content.

Slides
Code

Project/Homework Instructions

I'm a paragraph. Click here to add your own text and edit me. It's easy.

Project/Team Formation
Project Submission
Projects README

How To Form Projects

Instructional

This video should show you how to navigate the team formation process on the Erdos website.

Slides
Transcript

Schedule

Click on any date for more details

Lecture 1: Introduction

September 7, 2023 at 9:30:00 PM

EVENT

Office Hours 1

September 12, 2023 at 3:00:00 PM

EVENT

Problem Session 2

September 18, 2023 at 3:00:00 PM

EVENT

Lecture 3: Supervised Learning and Regression I

September 21, 2023 at 9:30:00 PM

EVENT

Math Hour 3

September 27, 2023 at 3:00:00 PM

EVENT

Office Hours 4

October 3, 2023 at 3:00:00 PM

EVENT

Problem Session 5

October 9, 2023 at 3:00:00 PM

EVENT

Lecture 6: Time Series II

October 12, 2023 at 9:30:00 PM

EVENT

Math Hour 6

October 18, 2023 at 3:00:00 PM

EVENT

Office Hours 7

October 24, 2023 at 3:00:00 PM

EVENT

Problem Session 8

October 30, 2023 at 3:00:00 PM

EVENT

Lecture 9: Classification II

November 2, 2023 at 9:30:00 PM

EVENT

Math Hour 9

November 8, 2023 at 4:00:00 PM

EVENT

Lecture 11: Ensemble Learning

November 16, 2023 at 10:30:00 PM

EVENT

Math Hour 11

November 29, 2023 at 4:00:00 PM

EVENT

Problem Session 1

September 11, 2023 at 3:00:00 PM

EVENT

Math Hour 1

September 13, 2023 at 3:00:00 PM

EVENT

Office Hours 2

September 19, 2023 at 3:00:00 PM

EVENT

Problem Session 3

September 25, 2023 at 3:00:00 PM

EVENT

Lecture 4: Regression II

September 28, 2023 at 9:30:00 PM

EVENT

Math Hour 4

October 4, 2023 at 3:00:00 PM

EVENT

Office Hours 5

October 10, 2023 at 3:00:00 PM

EVENT

Problem Session 6

October 16, 2023 at 3:00:00 PM

EVENT

Lecture 7: Time Series II

October 19, 2023 at 9:30:00 PM

EVENT

Math Hour 7

October 25, 2023 at 3:00:00 PM

EVENT

Office Hours 8

October 31, 2023 at 3:00:00 PM

EVENT

Problem Session 9

November 6, 2023 at 4:00:00 PM

EVENT

Lecture 10: Classification III

November 9, 2023 at 10:30:00 PM

EVENT

Problem Session 11

November 27, 2023 at 4:00:00 PM

EVENT

Lecture 12: Neural Networks

November 30, 2023 at 10:30:00 PM

EVENT

Study Group

September 12, 2023 at 3:00:00 PM

EVENT

Lecture 2: Data Collection

September 14, 2023 at 9:30:00 PM

EVENT

Math Hour 2

September 20, 2023 at 3:00:00 PM

EVENT

Office Hours 3

September 26, 2023 at 3:00:00 PM

EVENT

Problem Session 4

October 2, 2023 at 3:00:00 PM

EVENT

Lecture 5: Regression III & Time Series I

October 5, 2023 at 9:30:00 PM

EVENT

Math Hour 5

October 11, 2023 at 3:00:00 PM

EVENT

Office Hours 6

October 17, 2023 at 3:00:00 PM

EVENT

Problem Session 7

October 23, 2023 at 3:00:00 PM

EVENT

Lecture 8: Classification I

October 26, 2023 at 9:30:00 PM

EVENT

Math Hour 8

November 1, 2023 at 3:00:00 PM

EVENT

Office Hours 9

November 7, 2023 at 4:00:00 PM

EVENT

Problem Session 10

November 13, 2023 at 4:00:00 PM

EVENT

Office Hours 11

November 28, 2023 at 4:00:00 PM

EVENT

Project Showcase and Commencement

December 7, 2023 at 4:55:00 PM

EVENT

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

Project/Homework Deadlines

Sep 23, 2023

3:59 AM

Watch 3 Previous Top Projects

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

Oct 6, 2023

3:59 AM

Watch video about Project Formation

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

Oct 6, 2023

8:30 PM

Project Pitch Hour

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

Oct 12, 2023

3:59 AM

Submit Team Proposal or Idea to Project Formation Page

If you want to propose a project, or have an idea for a project, submit it by this date.

Oct 14, 2023

3:59 AM

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 21, 2023

3:59 AM

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 28, 2023

3:59 AM

Data cleaning + preprocessing

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

Nov 4, 2023

3:59 AM

Exploratory data analysis + visualizations [Checkpoint]

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

Nov 10, 2023

4:59 PM

Written proposal of modeling approach [Checkpoint]

Test linearity assumptions. Dimensionality reductions (if necessary). Describe your planned modeling approach, based on the exploratory data analysis from the last two weeks (< 1 page, bullet points).

Nov 16, 2023

4:59 AM

Machine learning models or equivalent [Checkpoint]

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

Dec 2, 2023

4:59 AM

Final project due

Please read the submission instructions on the link below.

bottom of page