Summarizing Data and Deducing Probabilities

سرفصل های دوره

This course covers the most important aspects of exploratory data analysis using different univariate, bivariate, and multivariate statistics from Excel and Python, including the use of Naive Bayes' classifiers and Seaborn to visualize relationships.

1. Course Overview

1. Course Overview

2. Understanding Descriptive Statistics for Data Analysis

1. Version Check

2. Module Overview

3. Prerequisites and Course Outline

4. Understanding Descriptive Statistics

5. Measures of Central Tendency and Dispersion

6. Understanding Variance

7. Measuring Relationships Using Covariance

8. Covariance Matrices and Correlation Matrices

3. Performing Exploratory Data Analysis in Spreadsheets

01. Module Overview

02. Working with Excel Workbooks

03. Descriptive Statistics for Univariate Data

04. Visualizing Univariate Statistics

05. Using Pivot Tables for Summary Statistics

06. Performing Analysis Using Bucketing and Pivot Charts

07. Visualizing Bivariate Relationships

08. Performing Regression Analysis on Bivariate Data

09. Covariance and Correlation Matrices for Multivariate Data

10. Visualizing Multivariate Data Using Pivot Charts

11. Regression Analysis with Multivariate Data

12. Module Summary

4. Summarizing Data and Deducing Probabilities Using Python

1. Module Overview

2. Getting Started with Azure Notebooks

3. Calculating Descriptive Statistics Using Python

4. Calculating Descriptive Statistics Using Python Libraries

5. Calculating Skewness Kurtosis and Simple Visualizations

6. Bivariate Analysis

7. Simple Regression on Bivariate Data Using Scipy

8. Regression on Multivariate Data Using Statsmodels and scikit-lea

9. Module Summary

5. Understanding and Applying Bayes Rule

1. Module Overview

2. Intuition behind Bayes Theorem

3. A Priori and Conditional Probabilities to Classify Data

4. Applying Bayes Theorem to Make Predictions

5. Calculating a Priori Probabilities of Survival on the Titanic

6. Applying Bayes Rule Using the Naive Bayes Classifier

7. Module Summary

6. Visualizing Probabilistic and Statistical Data Using Seaborn

01. Module Overview

02. Understanding Kernel Density Estimation

03. Histograms, KDE Plots, and Rug Plots for Univariate Analys

04. Scatter Plots, Joint Plots, Hexbin Plots for Univariate An

05. Regression Analysis on Bivariate Data

06. Representing Pairwise Relationships Using the Pairplot and

07. Visualizing Categorical Data Using Strip Plots and Swarm P

08. Visualizing Data Using Box Plots and Violin Plots

09. Visualizing Categorical Data Using Bar Plots, Point Plots,

10. Summary and Further Study

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تولید کننده: PluralSight