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
Author(s): George Mount
Edition: 1
Publisher: O'Reilly Media
Year: 2021
Language: English
Commentary: Vector PDF
Pages: 250
City: Sebastopol, CA
Tags: Machine Learning; Data Analysis; Python; R; Statistics; Excel; Linear Regression; Probability Theory; Statistical Inference; Elementary; Data Exploration
Cover
Copyright
Table of Contents
Preface
Learning Objective
Prerequisites
Technical Requirements
Technological Requirements
How I Got Here
“Excel Bad, Coding Good”
The Instructional Benefits of Excel
Book Overview
End-of-Chapter Exercises
This Is Not a Laundry List
Don’t Panic
Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us
Acknowledgments
Part I. Foundations of Analytics in Excel
Chapter 1. Foundations of Exploratory Data Analysis
What Is Exploratory Data Analysis?
Observations
Variables
Demonstration: Classifying Variables
Recap: Variable Types
Exploring Variables in Excel
Exploring Categorical Variables
Exploring Quantitative Variables
Conclusion
Exercises
Chapter 2. Foundations of Probability
Probability and Randomness
Probability and Sample Space
Probability and Experiments
Unconditional and Conditional Probability
Probability Distributions
Discrete Probability Distributions
Continuous Probability Distributions
Conclusion
Exercises
Chapter 3. Foundations of Inferential Statistics
The Framework of Statistical Inference
Collect a Representative Sample
State the Hypotheses
Formulate an Analysis Plan
Analyze the Data
Make a Decision
It’s Your World…the Data’s Only Living in It
Conclusion
Exercises
Chapter 4. Correlation and Regression
“Correlation Does Not Imply Causation”
Introducing Correlation
From Correlation to Regression
Linear Regression in Excel
Rethinking Our Results: Spurious Relationships
Conclusion
Advancing into Programming
Exercises
Chapter 5. The Data Analytics Stack
Statistics Versus Data Analytics Versus Data Science
Statistics
Data Analytics
Business Analytics
Data Science
Machine Learning
Distinct, but Not Exclusive
The Importance of the Data Analytics Stack
Spreadsheets
Databases
Business Intelligence Platforms
Data Programming Languages
Conclusion
What’s Next
Exercises
Part II. From Excel to R
Chapter 6. First Steps with R for Excel Users
Downloading R
Getting Started with RStudio
Packages in R
Upgrading R, RStudio, and R Packages
Conclusion
Exercises
Chapter 7. Data Structures in R
Vectors
Indexing and Subsetting Vectors
From Excel Tables to R Data Frames
Importing Data in R
Exploring a Data Frame
Indexing and Subsetting Data Frames
Writing Data Frames
Conclusion
Exercises
Chapter 8. Data Manipulation and Visualization in R
Data Manipulation with dplyr
Column-Wise Operations
Row-Wise Operations
Aggregating and Joining Data
dplyr and the Power of the Pipe (%>%)
Reshaping Data with tidyr
Data Visualization with ggplot2
Conclusion
Exercises
Chapter 9. Capstone: R for Data Analytics
Exploratory Data Analysis
Hypothesis Testing
Independent Samples t-test
Linear Regression
Train/Test Split and Validation
Conclusion
Exercises
Part III. From Excel to Python
Chapter 10. First Steps with Python for Excel Users
Downloading Python
Getting Started with Jupyter
Modules in Python
Upgrading Python, Anaconda, and Python packages
Conclusion
Exercises
Chapter 11. Data Structures in Python
NumPy arrays
Indexing and Subsetting NumPy Arrays
Introducing Pandas DataFrames
Importing Data in Python
Exploring a DataFrame
Indexing and Subsetting DataFrames
Writing DataFrames
Conclusion
Exercises
Chapter 12. Data Manipulation and Visualization in Python
Column-Wise Operations
Row-Wise Operations
Aggregating and Joining Data
Reshaping Data
Data Visualization
Conclusion
Exercises
Chapter 13. Capstone: Python for Data Analytics
Exploratory Data Analysis
Hypothesis Testing
Independent Samples T-test
Linear Regression
Train/Test Split and Validation
Conclusion
Exercises
Chapter 14. Conclusion and Next Steps
Further Slices of the Stack
Research Design and Business Experiments
Further Statistical Methods
Data Science and Machine Learning
Version Control
Ethics
Go Forth and Data How You Please
Parting Words
Index
About the Author
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