Advancing into Analytics

Table of contents

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