Essential Statistics for Non-STEM Data Analysts
Cód:
491_9781838984847
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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. Youll then get to grips with the fundamentals of statistical analysis and apply its concepts to real-world datasets. As you advance, youll 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 youve uncovered the working mechanism of data science algorithms, youll cover essential concepts for efficient data collection, cleaning, mining, visualization, and analysis. Finally, youll 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, youll 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
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