Summarizing Data and Deducing Probabilities

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.
Course info
Level
Intermediate
Updated
Jun 20, 2019
Duration
2h 49m
Table of contents
Course Overview
Understanding Descriptive Statistics for Data Analysis
Performing Exploratory Data Analysis in Spreadsheets
Summarizing Data and Deducing Probabilities Using Python
Understanding and Applying Bayes' Rule
Visualizing Probabilistic and Statistical Data Using Seaborn
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
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.