Claudia Gold, author of the Data Analysis Learning path on SlideRule, talks about why she wrote it and how to approach learning data science on your own. https://www.mysliderule.com/learning-paths/data-analysis/

1. How to teach yourself data science
by Claudia Gold

2. Background:
SlideRule Learning Path

3. SlideRule learning path:
The minimum you need
to be an entry-level data scientist

4. What do I need to know
1to be a data scientist?

5. Statistics
Areas of Data Science
Modeling
and
forecasting Machine
Learning
Data
Visualization
Data Engineering

6. Extract
and
manipulate data

7. Find trends and patterns
among noise

8. Visualize
and
communicate results

9. General computer knowledge:
Version control, the command
line, cron, etc

10. Designing sound experiments:
Research methodology

11. Business Sense
• Ask the right questions
• Focus on actionable insights
•Gain subject expertise

12. Math & Statistics
Knowledge
Source: Adapted from
Data Science Venn Diagram,
author Drew Conway,
Machine
Learning
Data
Science
Traditional
Research
posted to Wikimedia on Sep 30, 2010.
Hacking Skills
Danger
Zone!
Subject
Expertise

13. Learning data science takes months,
not hours.
(and that’s assuming you already know some math)

14. Data Scientist
or
Data Analyst?

15. What tools do
2data scientists use?

16. The essentials:
Python, R, and SQL

17. Data at scale:
Hadoop, Hive, MapReduce

18. Alternatives:
Julia, MongoDB, Java

19. Data visualization:
D3, R/Shiny

20. How these tools might fit together
Website data
SQL
database
Customer
support data
Event
tracking data
R or
Python
Website
Predictive
models
Dashboards
and reports
Analysis

21. What is it really like
to 3 be a data scientist?

22. Source: Programmer writing code with
unit tests, author Joonspoon, uploaded
to Wikipedia on November 4, 2014

23. Working with other teams
Data
Science
Marketing
Product
Business
decisions
Data
products
Operations

24. Balancing requests

25. Messy data

26. How you’ll spend your time
80%
20%
Analyzing data
Preparing to analyze data

27. Accuracy and completeness
vs
time constraints

28. Communicating results
to varied audiences

29. Advocating for data
within an organization

30. Balancing data with other priorities

31. Unlimited applications
Source: Burtch Works Study, Salaries of Data Scientists, pg 14

32. Always more to learn

33. Play a vital role within an organization

34. We’re hiring!
www.goodeggs.com/jobs