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.

01. Course Overview

01. Course Overview

02. Understanding Descriptive Statistics for Data Analysis

02. Module Overview

03. Prerequisites and Course Outline

04. Understanding Descriptive Statistics

05. Measures of Central Tendency and Dispersion

06. Understanding Variance

07. Measuring Relationships Using Covariance

08. Covariance Matrices and Correlation Matrices

03. Performing Exploratory Data Analysis in Spreadsheets

09. Module Overview

10. Working with Excel Workbooks

11. Descriptive Statistics for Univariate Data

12. Visualizing Univariate Statistics

13. Using Pivot Tables for Summary Statistics

14. Performing Analysis Using Bucketing and Pivot Charts

15. Visualizing Bivariate Relationships

16. Performing Regression Analysis on Bivariate Data

17. Covariance and Correlation Matrices for Multivariate Data

18. Visualizing Multivariate Data Using Pivot Charts

19. Regression Analysis with Multivariate Data

20. Module Summary

04. Summarizing Data and Deducing Probabilities Using Python

21. Module Overview

22. Getting Started with Azure Notebooks

23. Calculating Descriptive Statistics Using Python

24. Calculating Descriptive Statistics Using Python Libraries

25. Calculating Skewness Kurtosis and Simple Visualizations

26. Bivariate Analysis

27. Simple Regression on Bivariate Data Using Scipy

28. Regression on Multivariate Data Using Statsmodels and scikit-learn

29. Module Summary

05. Understanding and Applying Bayes Rule

30. Module Overview

31. Intuition behind Bayes Theorem

32. A Priori and Conditional Probabilities to Classify Data

33. Applying Bayes Theorem to Make Predictions

34. Calculating a Priori Probabilities of Survival on the Titanic

35. Applying Bayes Rule Using the Naive Bayes Classifier

36. Module Summary

06. Visualizing Probabilistic and Statistical Data Using Seaborn

37. Module Overview

38. Understanding Kernel Density Estimation

39. Histograms KDE Plots and Rug Plots for Univariate Analysis

40. Scatter Plots Joint Plots Hexbin Plots for Univariate Analysis

41. Regression Analysis on Bivariate Data

42. Representing Pairwise Relationships Using the Pairplot and Pairgrid

43. Visualizing Categorical Data Using Strip Plots and Swarm Plots

44. Visualizing Data Using Box Plots and Violin Plots

45. Visualizing Categorical Data Using Bar Plots Point Plots and Cat Plots

46. Summary and Further Study

439,000 تومان

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تولید کننده: PluralSight