Summarizing Data and Deducing Probabilities

By Janani Ravi
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.
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Course info
Level
Intermediate
Updated
Jun 20, 2019
Duration
2h 49m
Table of contents
Course Overview
Course Overview 2m
Understanding Descriptive Statistics for Data Analysis
Module Overview 1m Prerequisites and Course Outline 2m Understanding Descriptive Statistics 7m Measures of Central Tendency and Dispersion 5m Understanding Variance 3m Measuring Relationships Using Covariance 3m Covariance Matrices and Correlation Matrices 5m
Performing Exploratory Data Analysis in Spreadsheets
Module Overview 1m Working with Excel Workbooks 3m Descriptive Statistics for Univariate Data 7m Visualizing Univariate Statistics 3m Using Pivot Tables for Summary Statistics 4m Performing Analysis Using Bucketing and Pivot Charts 6m Visualizing Bivariate Relationships 3m Performing Regression Analysis on Bivariate Data 4m Covariance and Correlation Matrices for Multivariate Data 4m Visualizing Multivariate Data Using Pivot Charts 3m Regression Analysis with Multivariate Data 3m Module Summary 1m
Summarizing Data and Deducing Probabilities Using Python
Module Overview 2m Getting Started with Azure Notebooks 2m Calculating Descriptive Statistics Using Python 6m Calculating Descriptive Statistics Using Python Libraries 7m Calculating Skewness Kurtosis and Simple Visualizations 3m Bivariate Analysis 4m Simple Regression on Bivariate Data Using Scipy 3m Regression on Multivariate Data Using Statsmodels and scikit-learn 6m Module Summary 1m
Understanding and Applying Bayes' Rule
Module Overview 1m Intuition behind Bayes' Theorem 5m A Priori and Conditional Probabilities to Classify Data 5m Applying Bayes' Theorem to Make Predictions 7m Calculating a Priori Probabilities of Survival on the Titanic 7m Applying Bayes' Rule Using the Naive Bayes Classifier 3m Module Summary 1m
Visualizing Probabilistic and Statistical Data Using Seaborn
Module Overview 1m Understanding Kernel Density Estimation 4m Histograms, KDE Plots, and Rug Plots for Univariate Analysis 5m Scatter Plots, Joint Plots, Hexbin Plots for Univariate Analysis 5m Regression Analysis on Bivariate Data 5m Representing Pairwise Relationships Using the Pairplot and Pairgrid 4m Visualizing Categorical Data Using Strip Plots and Swarm Plots 3m Visualizing Data Using Box Plots and Violin Plots 4m Visualizing Categorical Data Using Bar Plots, Point Plots, and Cat Plots 4m Summary and Further Study 2m
Description
Course info
Level
Intermediate
Updated
Jun 20, 2019
Duration
2h 49m
Description

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.

About the author
Janani Ravi
About the author

A problem solver at heart, 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.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, my name is Janani Ravi, and welcome to this course on Summarizing Data and Deducing Probabilities. A little about myself. I have a masters degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real-time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. 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's really important that you know your data well. In this course, you will gain the ability to summarize your data using univariate, bivariate, and multivariate statistics is a range of technologies. First, you'll 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 those constructs can be generalized to multiple variables using covariances and correlation matrices. Then 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're 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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