Interpreting Data Using Descriptive Statistics with Python

This course covers measures of central tendency and dispersion needed to identify key insights in data. It also covers: correlation, covariance, skewness, kurtosis, and implementations in Python libraries such as Pandas, SciPy, and StatsModels.
Course info
Rating
(25)
Level
Intermediate
Updated
Nov 8, 2019
Duration
2h 20m
Table of contents
Description
Course info
Rating
(25)
Level
Intermediate
Updated
Nov 8, 2019
Duration
2h 20m
Description

The tools of machine learning - algorithms, solution techniques, and even neural network architectures, are becoming commoditized. Everyone is using the same tools these days, so your edge needs to come from how well you adapt those tools to your data.

In this course, Interpreting Data using Descriptive Statistics with Python, you will gain the ability to identify the important statistical properties of your dataset and understand their implications.

First, you will explore how important measures of central tendency, the arithmetic mean, the mode, and the median, each summarize our data in different ways. Next, you will discover how measures of dispersion such as standard deviation provide clues about variation in a single variable.

Later, you will learn how your data is distributed using skewness and kurtosis and understand bivariate measures of dispersion and co-movement like correlation and covariance.

Finally, you will round out your knowledge by implementing these measures using different libraries available in Python, like Pandas, SciPy, and StatsModels.

When you are finished with this course, you will have the skills and knowledge to summarize key statistical properties of your dataset using Python.

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 Interpreting Data Using Descriptive Statistics with Python. 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 start up Loonycorn, a studio for high quality video content. The tools of machine learning, algorithms, solution techniques, and even neural network architectures are becoming commoditized. Everyone is using the same tools these days so your edge needs to come from how well you adapt those tools to your data. Today, more than ever, it's important that really know your data well. In this course, you will gain the ability to identify important statistical properties of your dataset and understand their implications. First, you will learn how important measures of central tendency, the arithmetic mean, the mode, and the median each summarizes our data in different ways. Next, you will discover how measures of this dispersion, such as standard deviation, provide clues about variation in a single variable. You will learn how your data is distributed using skewness and kurtosis, and you will then understand bivariate measures of dispersion and core movement, such as correlation and covariance. Finally, we'll round out your knowledge by implementing these measures using different libraries available in Python, such as Pandas, SciPy, and stats models. When you're finished with this course, you will have the skills and knowledge to summarize key statistical properties of your data set using Python.