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An Obligatory Introduction to Data Science

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In this presentation, Wes Eldridge will provide a general overview on data science. The talk will cover a variety of topics, Wes will start with the dirty history of the field which will help add context. After learning about the history of data and data science Wes will discuss the common roles a data scientist holds in business and organizations. Next, he will talk about how to use data in your organization and products. Finally, he'll cover some tools to help you get started in data science. After the presentation, Wes will stick around for Q/A and data discussion.

Published in: Data & Analytics
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An Obligatory Introduction to Data Science

  1. 1. An Obligatory Introduction to Data Science
  2. 2. Wesley Eldridge Rebellious Labs Software Engineer University of Alabama BSBA - Applied Economics
  3. 3. Agenda ● The dirty history of data science ● Data scientist roles ● Using data in your product and business ● Tools and resources to get started
  4. 4. Before there was data science
  5. 5. Big Data
  6. 6. Data Scientist
  7. 7. What is a data scientist?
  8. 8. “...data scientists do three fundamentally different things: math, code (and engineer systems), and communicate.” - Hilary Mason
  9. 9. Math & Stats Engineering Communication Data Scientist nerd nerd nerd
  10. 10. What do data scientists do?
  11. 11. How to use data in your products and organization
  12. 12. Collect all the data!
  13. 13. “Data by itself is useless. Data is only useful if you apply it.” - Todd Park
  14. 14. Finding or building data
  15. 15. “Many social/digital scientists are reluctant to invest in making data because it’s much more costly and risky than analyzing data you already have available.” - Sean Taylor
  16. 16. Using data to understand the question
  17. 17. Metrics to define the success of the model
  18. 18. Presenting the data to stakeholders or users
  19. 19. 1. Ask a question 2. Finding or building data 3. Using that data to understand the question 4. Presenting the data to stakeholders or users
  20. 20. Building products and processes for better data
  21. 21. Do do not worry about the tools. Hire smart people and they will bring the tools with them.
  22. 22. How do I become a data scientist?
  23. 23. Start small Learn to code ● Python ● JavaScript Learn to move data ● SQL ● Mongo Learn to question everything ● No gut feelings ● Data-based decision making Find some data and play with it. ● Government/municipality data ● Social data ● Open data Online learning ● Kaggle.com ● Udacity.com ● Lynda.com Develop math skills ● Regression ● Error analysis ● Data distributions ● Linear Algebra
  24. 24. Then What? Mathematical Modeling ● Logistics regression ● One hot encoding ● Decision trees ● Correlation ● Model assumptions Data visualization ● D3.js ● Tell a story Help a non-profit/municipality ● Open up their data ● Tell their story ● Solve a problem bit.ly/2bxnQgb
  25. 25. Questions? @weseldridge

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