## Book description

Data analytics may seem daunting, but if you're an experienced Excel user, you have a unique head start. With this hands-on guide, intermediate Excel users will gain a solid understanding of analytics and the data stack. By the time you complete this book, you'll be able to conduct exploratory data analysis and hypothesis testing using a programming language.

Exploring and testing relationships are core to analytics. By using the tools and frameworks in this book, you'll be well positioned to continue learning more advanced data analysis techniques. Author George Mount, founder and CEO of Stringfest Analytics, demonstrates key statistical concepts with spreadsheets, then pivots your existing knowledge about data manipulation into R and Python programming.

This practical book guides you through:

• Foundations of analytics in Excel: Use Excel to test relationships between variables and build compelling demonstrations of important concepts in statistics and analytics
• From Excel to R: Cleanly transfer what you've learned about working with data from Excel to R
• From Excel to Python: Learn how to pivot your Excel data chops into Python and conduct a complete data analysis

## Publisher resources

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1. Preface
1. Learning Objective
2. Prerequisites
3. How I Got Here
5. The Instructional Benefits of Excel
6. Book Overview
7. End-of-Chapter Exercises
8. This Is Not a Laundry List
9. Donât Panic
10. Conventions Used in This Book
11. Using Code Examples
12. OâReilly Online Learning
14. Acknowledgments
2. I. Foundations of Analytics in Excel
3. 1. Foundations of Exploratory Data Analysis
1. What Is Exploratory Data Analysis?
2. Demonstration: Classifying Variables
3. Recap: Variable Types
4. Exploring Variables in Excel
5. Conclusion
6. Exercises
4. 2. Foundations of Probability
1. Probability and Randomness
2. Probability and Sample Space
3. Probability and Experiments
4. Unconditional and Conditional Probability
5. Probability Distributions
6. Conclusion
7. Exercises
5. 3. Foundations of Inferential Statistics
1. The Framework of Statistical Inference
2. Itâs Your Worldâ¦âthe Dataâs Only Living in It
3. Conclusion
4. Exercises
6. 4. Correlation and Regression
7. 5. The Data Analytics Stack
1. Statistics Versus Data Analytics Versus Data Science
2. The Importance of the Data Analytics Stack
3. Conclusion
4. Whatâs Next
5. Exercises
8. II. From Excel to R
9. 6. First Steps with R for Excel Users
10. 7. Data Structures in R
11. 8. Data Manipulation and Visualization in R
1. Data Manipulation with dplyr
2. Data Visualization with ggplot2
3. Conclusion
4. Exercises
12. 9. Capstone: R for Data Analytics
1. Exploratory Data Analysis
2. Hypothesis Testing
3. Conclusion
4. Exercises
13. III. From Excel to Python
14. 10. First Steps with Python for Excel Users
15. 11. Data Structures in Python
1. NumPy arrays
2. Indexing and Subsetting NumPy Arrays
3. Introducing Pandas DataFrames
4. Importing Data in Python
5. Exploring a DataFrame
6. Conclusion
7. Exercises
16. 12. Data Manipulation and Visualization in Python
17. 13. Capstone: Python for Data Analytics
1. Exploratory Data Analysis
2. Hypothesis Testing
3. Conclusion
4. Exercises
18. 14. Conclusion and Next Steps
19. Index