This document provides guidance on finding low-cost or free data science resources. It categorizes resources for beginners, intermediates, and advanced learners. Beginner resources like KD Nuggets and YouTube aim for likes and clicks. Intermediate resources like Meetup and MOOCs want identity and participation. Advanced resources want trading relationships where value is exchanged. The document recommends specific free resources from Microsoft like Azure documentation and AI Business School. It emphasizes understanding the motivations behind "free" to build respectful relationships within the free economy.

1. How to Find
Low-Cost or Free
Data Science
Resources
Mark Tabladillo Ph.D.
June 30, 2020

2. Problems
with Fake
Free
• False or misleading content
• Second-quality content
• Outdated content
• Asking money for something free
• Risks

3. Main
Economic
Idea on
“Free”
• “Follow the Money”
• Think through what people want when they
offer something for free or at a reduced cost
• Understanding especially stated motivation
is the pathway to respect

4. Main Quality Ideas on “Free”
STAY CLOSE TO THE
SOURCE
FIND SOURCES WITH
STANDARDS
PRODUCERS AND THEIR
FRIENDS ARE THE
HIGHEST ASSURANCE
DEVELOP YOUR OWN MAP

5. Main Content Ideas on “Free”
Data
Business
Understanding
Data Engineering
Deployment
Modeling
Microsoft
Team Data Science Process
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/

6. Taxonomy of
Resources
• Beginner: anonymous
• Giver wants likes and clicks
• Intermediate: give information
• Giver wants identity, perhaps low-
fee
• Advanced: trade information
• Giver wants participation

7. Beginner
• KD Nuggets
• https://www.kdnuggets.com/2020/04/machine-
learning-data-science-books-courses-
quarantine.html
• Toward Data Science
• https://towardsdatascience.com/
• Github
• https://towardsdatascience.com/top-10-
popular-github-repositories-to-learn-about-
data-science-4acc7b99c44
• Stack Overflow https://stackoverflow.com/
• YouTube
• Deep Learning Book
https://www.deeplearningbook.org/

8. Beginner @
Microsoft
• Channel 9
• https://channel9.msdn.com/Series
/Intro-to-Python-Development
• Documentation
• https://docs.microsoft.com/en-
us/azure/machine-learning/team-
data-science-process/

9. Intermediate
• PASS
• Virtual Chapters
• SQL Saturday
• PASS Summit (comparatively low-cost)
• Meetup http://www.meetup.com/
• University (free)
• MIT / Columbia / Stanford / Harvard / Berkeley
• https://towardsdatascience.com/learn-data-science-
from-top-universities-for-free-ee3387ad88ac
• MOOC (Udacity, edX, Coursera)
• LinkedIn: producers
• Microsoft (1,200); Google (65); Amazon/AWS
(165); Databricks (55); Alibaba (1)

10. Intermediate @
Microsoft
• AI Business School
• https://www.microsoft.com/en-
us/ai/ai-business-school
• Learn
• https://docs.microsoft.com/en-
us/learn/

11. Advanced
• Producing
• KD Nuggets
• Towards Data Science
• PASS
• Github
• Stack Overflow
• Meetup
• Edge: clarifies what you have to trade

12. Advanced @
Microsoft
• MVP (Most Valuable
Professional)
• https://mvp.microsoft.com/

13. Main
Economic
Idea on
“Free”
• Think through what people want when they
offer something for free or at a reduced cost
• Likes or clicks -- Beginner
• Identity -- Intermediate
• Trading Relationship -- Advanced
• Understanding especially stated motivation
is the pathway to respect
• Write a thank you
• Refer
• Promote
• Become part of the free economy

14. Abstract
• There are many free or low-cost resources to become better trained
in data science. None of these options equals a formal degree: but
short of that scope, these other resources are helpful at least for
keeping up with technology. This presentation will provide specific
recommendations on free or low-cost resources based on the Team
Data Science Process framework (business understanding, data
engineering, modeling, deployment).