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.

1. An Obligatory Introduction to Data Science

2. Wesley Eldridge
Rebellious Labs
Software Engineer
University of Alabama
BSBA - Applied Economics

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. Before there was data science

6. Big Data

7. Data Scientist

8. What is a data scientist?

9. “...data scientists do three fundamentally different things:
math, code (and engineer systems), and communicate.”
- Hilary Mason

10. Math
&
Stats
Engineering
Communication
Data Scientist
nerd
nerd
nerd

11. What do data scientists do?

13. How to use data in your
products and organization

14. Collect all the data!

15. “Data by itself is useless. Data is only useful if you apply it.”
- Todd Park

17. Finding or building data

18. “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

19. Using data to understand
the question

20. Metrics to define the
success of the model

21. Presenting the data to
stakeholders or users

22. 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

23. Building products
and processes for
better data

25. Do do not worry about the tools.
Hire smart people and they will bring the tools with them.

26. How do I become a
data scientist?

27. 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

28. 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

29. Questions?
@weseldridge