Put your data science knowledge to work with this practical guide to statistics. You'll understand the working mechanism of each method used and find out how data science algorithms function. This book will help you learn the statistical techniques required for key model building and functioning using Python.
Über den Autor
Rongpeng Li is a data science instructor and a senior data scientist at Galvanize, Inc. He has previously been a research programmer at Information Sciences Institute, working on knowledge graphs and artificial intelligence. He has also been the host and organizer of the Data Analysis Workshop Designed for Non-STEM Busy Professionals at LA.
Klappentext
Reinforce your understanding of data science and data analysis from a statistical perspective to extract meaningful insights from your data using Python programmingnnnKey featuresWork your way through the entire data analysis pipeline with statistics concerns in mind to make reasonable decisionsnUnderstand how various data science algorithms functionnBuild a solid foundation in statistics for data science and machine learning using Python-based examplesnnnnnBook DeillegalscriptionnStatistics remain the backbone of modern analysis tasks, helping you to interpret the results produced by data science pipelines. This book is a detailed guide covering the math and various statistical methods required for undertaking data science tasks.nnnThe book starts by showing you how to preprocess data and inspect distributions and correlations from a statistical perspective. You'll then get to grips with the fundamentals of statistical analysis and apply its concepts to real-world datasets. As you advance, you'll find out how statistical concepts emerge from different stages of data science pipelines, understand the summary of datasets in the language of statistics, and use it to build a solid foundation for robust data products such as explanatory models and predictive models. Once you've uncovered the working mechanism of data science algorithms, you'll cover essential concepts for efficient data collection, cleaning, mining, visualization, and analysis. Finally, you'll implement statistical methods in key machine learning tasks such as classification, regression, tree-based methods, and ensemble learning.nnnBy the end of this Essential Statistics for Non-STEM Data Analysts book, you'll have learned how to build and present a self-contained, statistics-backed data product to meet your business goals.nnnWhat you will learnFind out how to grab and load data into an analysis environmentnPerform deillegalscriptive analysis to extract meaningful summaries from datanDiscover probability, parameter estimation, hypothesis tests, and experiment design best practicesnGet to grips with resampling and bootstrapping in PythonnDelve into statistical tests with variance analysis, time series analysis, and A/B test examplesnUnderstand the statistics behind popular machine learning algorithmsnAnswer questions on statistics for data scientist interviewsnnnnnWho this book is fornThis book is an entry-level guide for data science enthusiasts, data analysts, and anyone starting out in the field of data science and looking to learn the essential statistical concepts with the help of simple explanations and examples. If you're a developer or student with a non-mathematical background, you'll find this book useful. Working knowledge of the Python programming language is required.
