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 TwainThis 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 SampleProbability Sampling TechniquesSimple Random SamplingSystematic SamplingStratified SamplingClustered SamplingNon-probability Sampling TechniqueConvenience SamplingQuota SamplingJudgemental SamplingSnowball SamplingWhat is Statistics?Importance of Statistics In Data-science and Machine LearningTypes of StatisticsMeasure of Central TendencyArithmetic MeanWeighted MeanMean of Categorical DataGeometric MeanHarmonic MeanMedianModeMeasure of Spread/DispersionQuantilesPercentile in DetailRangeInterquartile Range(IQR)Box and Whisker PlotVarianceStandard DeviationDegree of FreedomMean DeviationCoefficient of VariationSkewnessKurtosisCovariance and CorrelationMomentsStandard ErrorConfidence IntervalStem and Leaf diagramDot PlotsFrequency DistributionRelative FrequencyCumulative Relative FrequencyAbout 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:Google Colab. Github.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.