Learning Data Science

Introduction

• Exploring data science

1. What Is Data Science?

• A multidisciplinary practice with multiple meanings
• Using statistics and software
• Uncovering insights and creating knowledge

2. Working with Databases

• Making connections with relational databases
• Getting data into warehouses using ETL
• Letting go of the past with NoSQL
• Addressing a big data problem

3. Recognizing Different Data Types

• Keeping things simple with structured data
• Sharing semistructured data
• Collecting unstructured data
• Sifting through big garbage

4. Statistical Analysis

• Starting out with descriptive statistics
• Understanding probability
• Finding a correlation
• Correlation does not imply causation
• Combing techniques for predictive analytics

5. Critical Thinking

• Harness the power of questions
• Gold panning
• Focus on reasoning
• Test your reasoning

6. Encourage Questions

• Run question meetings
• Identify question types
• Organize your questions
• Create question trees
• Find new questions

7. Challenge Assumptions

• Clarify key terms
• Root out assumptions
• Find errors
• Challenge evidence
• See other causes
• Uncover misleading statistics
• Highlight missing data

8. Tell Stories

• Defining a story
• Spinning a story
• Weaving a story together
• Using story structure
• Introducing plot
• Presenting conflict

9. Engaging the Audience

• Defining details
• Reporting isn't telling
• Knowing your audience
• Believing what you say

10. Using Data Visuals

• Working with data
• Introducing visuals
• Eliminating distractions

11. Motivating Action

• Using metaphors
• Setting a vision
• Motivating the audience

Conclusion

• Next steps and additional resources