10. Bivariate N2N Statistics
10.4 Practice
Applied Python for Data Analytics: An Introduction
Preface
Introduction
1. Working with Variables
1.2 Type casting
1.3 Methods
1.4 Date & Time Variables
1.5 Type Hinting Variables
1.6 Project
1.7 Practice
2. Controlling Python's Flow
2.1 If Statements
2.2 Validation and Error Handling
2.3 Advanced Flow Control
2.4 Project
2.5 Practice
3. Working with Loops
3.1 Controlling Loops
3.2 Nested Loops
3.3 Generative Loops
3.4 Project
3.5 Practice
4. Functions
4.1 Parameters
4.2 Returns
4.3 Type Hinting
4.4 Project
4.5 Practice
5. Classes
Class Inheritance
project.html
practice.html
6. DataFrame Basics
6.1 Reading and Modifying Data
6.2 Filtering and Sorting DataFrames
6.3 Reading and Writing Data
6.4 Project
6.5 Practice
7. Advanced DataFrames
7.1 Replacing Values in a DataFrame
7.2 Using the
.apply()
Method
7.3 Dates and Times in DataFrames
7.4 Grouping and Aggregation
7.5 Project
7.4 Practice
8. Univariate Statistics
8.1 EDA Basics
8.2 Normality—Standard Dev., Skew, Kurtosis
8.3 Project
practice.html
9. Univariate Visualizations
9.1 Anatomy of a Visualization
9.2 Histograms
9.3 Box Plots
9.4 Multi Plots
9.5 Project
9.6 Practice
10. Bivariate N2N Statistics
10.1 P-Values and Statistical Significance
10.2 The Pearson R in Python
10.3 Project
10.4 Practice
11. Bivariate N2N Visualizations
11.1 Line of Best Fit
11.2 Multiple Dimension Visualizations
11.3 Scatter Plots and More
Project
Practice
12. Bivariate C2N Statistics
12.1 T-Tests
12.2 ANOVA
12.3 Tukey’s HSD Test
12.4 Project
12.5 Practice
13. Bivariate C2N Visualizations
13.1 Bar Plots
13.2 Visualizing Multiple Dimensions
13.2 Project
13.3 Practice
Appendix
Appendix A: VS Code
Appendix B: Git
Appendix C: UV for Python Management
10. Bivariate N2N Statistics
10.4 Practice
10.4 Practice
Bivariate Statistics
10.3 Project
11. Bivariate N2N Visualizations