Advancing into Analytics: From Excel to Python and R

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 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 Colophon