English | 2020 | ISBN-13 : 978-1838984847 | 392 Pages | True (PDF, EPUB, MOBI) + Code | 58.33 MB

Statistics remain the backbone of modern analysis tasks, helping you to interpret the results produced by data science pipelines.

Reinforce your understanding of data science and data analysis from a statistical perspective to extract meaningful insights from your data using Python programming

Key Features

Work your way through the entire data analysis pipeline with statistics concerns in mind to make reasonable decisions

Understand how various data science algorithms function

Build a solid foundation in statistics for data science and machine learning using Python-based examples

Book Description

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 learn

Find out how to grab and load data into an analysis environment

Perform descriptive analysis to extract meaningful summaries from data

Discover probability, parameter estimation, hypothesis tests, and expent design best practices

Get to grips with resampling and bootstrapping in Python

Delve into statistical tests with variance analysis, series analysis, and A/B test examples

Understand the statistics behind popular machine learning algorithms

Answer questions on statistics for data scientist interviews

Who this book is for

This 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.

Table of Contents

Fundamentals of Data Collection, Cleaning and Preprocessing

Essential Statistics for Data Assessment

Visualization with Statistical Graphs

Sampling and Inferential Statistics

Common Probability Distributions

Parametric Estimation

Statistical Hypothesis Testing

Statistics for Regression

Statistics for Classification

Statistics for Tree-based Methods

Statistics for Ensemble Method

A Collection of Best Practices

Exercises and Projects



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