Essential Statistics for Non-STEM Data Analysts (eBook)
392 Seiten
Packt Publishing (Verlag)
978-1-83898-756-5 (ISBN)
Statistics 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.
The 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.
By 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.
Reinforce your understanding of data science and data analysis from a statistical perspective to extract meaningful insights from your data using Python programmingKey FeaturesWork your way through the entire data analysis pipeline with statistics concerns in mind to make reasonable decisionsUnderstand how various data science algorithms functionBuild a solid foundation in statistics for data science and machine learning using Python-based examplesBook DescriptionStatistics 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. The 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. By 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.What you will learnFind out how to grab and load data into an analysis environmentPerform descriptive analysis to extract meaningful summaries from dataDiscover probability, parameter estimation, hypothesis tests, and experiment design best practicesGet to grips with resampling and bootstrapping in PythonDelve into statistical tests with variance analysis, time series analysis, and A/B test examplesUnderstand the statistics behind popular machine learning algorithmsAnswer questions on statistics for data scientist interviewsWho this book is forThis 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.
| Erscheint lt. Verlag | 12.11.2020 |
|---|---|
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| Mathematik / Informatik ► Informatik ► Web / Internet | |
| ISBN-10 | 1-83898-756-8 / 1838987568 |
| ISBN-13 | 978-1-83898-756-5 / 9781838987565 |
| Haben Sie eine Frage zum Produkt? |
EPUB (Ohne DRM)Digital Rights Management: ohne DRM
Dieses eBook enthält kein DRM oder Kopierschutz. Eine Weitergabe an Dritte ist jedoch rechtlich nicht zulässig, weil Sie beim Kauf nur die Rechte an der persönlichen Nutzung erwerben.
Dateiformat: EPUB (Electronic Publication)
EPUB ist ein offener Standard für eBooks und eignet sich besonders zur Darstellung von Belletristik und Sachbüchern. Der Fließtext wird dynamisch an die Display- und Schriftgröße angepasst. Auch für mobile Lesegeräte ist EPUB daher gut geeignet.
Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür die kostenlose Software Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür eine kostenlose App.
Geräteliste und zusätzliche Hinweise
Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.
aus dem Bereich
