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Descriptive Statistics for Data-driven Decision Making with Python

“Data is like garbage! You’d better know what you’re going to do with it before you collect it.” ~ Mark Twain

This is not a typical reference book for descriptive statistics. Instead, we take an in-depth look at what methods you would be foolish not to use in data science or machine learning. In this book, we dive into descriptive statistics with Python and an overview of what is crucial to know to obtain the most advantage of what we show you. 

Descriptive statistics is essential for decision making based on data. Using descriptive statistics will give you a way to make straightforward decisions on your decision making without complex methodology. Descriptive statistics form the fundamental platform for every quantitative data analysis.

"Diving into Descriptive Statistics with Python" is a book by Pratik Shukla and Roberto Iriondo. Between us, we have worked together for the past year to create this material and prepare you for straightforward, data-driven decision making.

Please know that this is an experience-based, opinionated book. We only cover topics that will help you make the most out of using descriptive statistics for data-driven decision making.

Book Sample:

You can access a sample of this book through this article or this PDF. No email address needed.

Contents:

  • Population and Sample
  • Probability Sampling Techniques
  • Simple Random Sampling
  • Systematic Sampling
  • Stratified Sampling
  • Clustered Sampling
  • Non-probability Sampling Technique
  • Convenience Sampling
  • Quota Sampling
  • Judgemental Sampling
  • Snowball Sampling
  • What is Statistics?
  • Importance of Statistics In Data-science and Machine Learning
  • Types of Statistics
  • Measure of Central Tendency
  • Arithmetic Mean
  • Weighted Mean
  • Mean of Categorical Data
  • Geometric Mean
  • Harmonic Mean
  • Median
  • Mode
  • Measure of Spread/Dispersion
  • Quantiles
  • Percentile in Detail
  • Range
  • Interquartile Range(IQR)
  • Box and Whisker Plot
  • Variance
  • Standard Deviation
  • Degree of Freedom
  • Mean Deviation
  • Coefficient of Variation
  • Skewness
  • Kurtosis
  • Covariance and Correlation
  • Moments
  • Standard Error
  • Confidence Interval
  • Stem and Leaf diagram
  • Dot Plots
  • Frequency Distribution
  • Relative Frequency
  • Cumulative Relative Frequency

About the Authors:

Pratik Shukla is a machine learning engineer with Towards AI. He is pursuing his master's degree in computer science in the US starting in 2021. His current goals are motivated by the purpose of learning something new and remarkable every day. His research interests lie in machine learning and its applications, especially in astronomy and astrophysics. Previously, he received his B.Tech. from Gujarat Technological University, and his work has been featured in many places, from KDNuggets, Nightingale, and others.

Roberto Iriondo is the founder of Towards AI, a globally recognized publication and software company, and a front-end engineer at Carnegie Mellon University. As a builder and strategist by heart, his work has helped several companies to achieve their business goals and needs, from Anyscale, Superb AI, Determined AI, Lambda, Udacity, and many others.

FAQ:

What's the refund policy?

We thank you for supporting Towards AI. If what you see is not what you expected, please reply to the download email within 30 days, and you'll get a full refund.

Can I share this book with my team?

This version is for individual use only, but we are working on a team license to share with your team, class, or organization.

Resources:

DISCLAIMER: The views expressed in this book are those of the author(s) and do not represent the views of any company (directly or indirectly) associated with the author(s). This book does not intend to be a final product, yet rather a reflection of current thinking along with being a catalyst for discussion and improvement.

-Quantity 1+
  • You'll get a mobile improved PDF and future updates to this book.
  • Size4.87 MB
  • Length154 pages

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Descriptive Statistics for Data-driven Decision Making with Python

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