Summarizing Data and Deducing Probabilities

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Janani Ravi

2:48:26

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1.Course Overview

01.Course Overview.mp4

01:59

2.Understanding Descriptive Statistics for Data Analysis

02.Module Overview.mp4

01:14

03.Prerequisites and Course Outline.mp4

01:31

04.Understanding Descriptive Statistics.mp4

06:57

05.Measures of Central Tendency and Dispersion.mp4

04:53

06.Understanding Variance.mp4

02:44

07.Measuring Relationships Using Covariance.mp4

03:20

08.Covariance Matrices and Correlation Matrices.mp4

04:47

3.Performing Exploratory Data Analysis in Spreadsheets

09.Module Overview.mp4

01:08

10.Working with Excel Workbooks.mp4

03:04

11.Descriptive Statistics for Univariate Data.mp4

06:49

12.Visualizing Univariate Statistics.mp4

03:04

13.Using Pivot Tables for Summary Statistics.mp4

03:45

14.Performing Analysis Using Bucketing and Pivot Charts.mp4

05:59

15.Visualizing Bivariate Relationships.mp4

02:51

16.Performing Regression Analysis on Bivariate Data.mp4

04:06

17.Covariance and Correlation Matrices for Multivariate Data.mp4

04:07

18.Visualizing Multivariate Data Using Pivot Charts.mp4

03:23

19.Regression Analysis with Multivariate Data.mp4

02:36

20.Module Summary.mp4

01:25

4.Summarizing Data and Deducing Probabilities Using Python

21.Module Overview.mp4

01:34

22.Getting Started with Azure Notebooks.mp4

02:10

23.Calculating Descriptive Statistics Using Python.mp4

05:44

24.Calculating Descriptive Statistics Using Python Libraries.mp4

06:33

25.Calculating Skewness Kurtosis and Simple Visualizations .mp4

03:08

26. Bivariate Analysis .mp4

03:30

27.Simple Regression on Bivariate Data Using Scipy.mp4

02:50

28.Regression on Multivariate Data Using Statsmodels and scikit-learn.mp4

05:31

29.Module Summary.mp4

01:09

5.Understanding and Applying Bayes' Rule

30.Module Overview.mp4

01:08

31.Intuition behind Bayes Theorem.mp4

04:34

32.A Priori and Conditional Probabilities to Classify Data.mp4

04:43

33.Applying Bayes Theorem to Make Predictions.mp4

06:51

34.Calculating a Priori Probabilities of Survival on the Titanic.mp4

07:19

35.Applying Bayes Rule Using the Naive Bayes Classifier.mp4

03:06

36.Module Summary.mp4

01:17

6.Visualizing Probabilistic and Statistical Data Using Seaborn

37.Module Overview.mp4

01:05

38.Understanding Kernel Density Estimation.mp4

03:58

39.Histograms, KDE Plots, and Rug Plots for Univariate Analysis.mp4

04:50

40.Scatter Plots, Joint Plots, Hexbin Plots for Univariate Analysis .mp4

05:27

41.Regression Analysis on Bivariate Data.mp4

04:52

42.Representing Pairwise Relationships Using the Pairplot and Pairgrid.mp4

04:27

43.Visualizing Categorical Data Using Strip Plots and Swarm Plots.mp4

03:20

44.Visualizing Data Using Box Plots and Violin Plots.mp4

03:49

45.Visualizing Categorical Data Using Bar Plots, Point Plots, and Cat Plots.mp4

04:11

46.Summary and Further Study.mp4

01:38

Description

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.

What You'll Learn?

Data science and data modeling are fast emerging as crucial capabilities that every enterprise and every technologist must possess these days. Increasingly, different organizations are using the same models and the same modeling tools, so what differs is how those models are applied to the data. So, it is really important that you know your data well.

In this course, Summarizing Data and Deducing Probabilities, you will gain the ability to summarize your data using univariate, bivariate, and multivariate statistics in a range of technologies.

First, you will learn how measures of mean and central tendency can be calculated in Microsoft Excel and Python. Next, you will discover how to use correlations and covariances to explore pairwise relationships. You will then see how those constructs can be generalized to multiple variables using covariance and correlation matrices.

You will understand and apply Bayes' Theorem, one of the most powerful and widely-used results in probability, to build a robust classifier.

Finally, you will use Seaborn, a visualization library, to represent statistics visually.

When you are finished with this course, you will have the skills and knowledge to use univariate, bivariate, and multivariate descriptive statistics from Excel and Python in order to find relationships and calculate probabilities.

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Python

Data Science

Excel

Janani Ravi

Instructor's Courses

Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing high-quality content for technical skill development. Loonycorn is working on developing an engine (patent filed) to automate animations for presentations and educational content.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.