Reinforce your understanding of data science and data analysis from a statistical perspective to extract meaningful insights from your data using Python programming
▶Book Description
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.
▶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 experiment design best practices
⦁Get to grips with resampling and bootstrapping in Python
⦁Delve into statistical tests with variance analysis, time series analysis, and A/B test examples
⦁Understand the statistics behind popular machine learning algorithms
⦁Answer questions on statistics for data scientist interviews
▶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
▶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.
▶What this book covers
⦁ Chapter 1, Fundamentals of Data Collection, Cleaning, and Preprocessing, introduces basic concepts in data collection, cleaning, and simple preprocessing.
⦁ Chapter 2, Essential Statistics for Data Assessment, talks about descriptive statistics, which are handy for the assessment of data quality and exploratory data analysis (EDA).
⦁ Chapter 3, Visualization with Statistical Graphs, introduces common graphs that suit different visualization scenarios.
⦁ Chapter 4, Sampling and Inferential Statistics, introduces the fundamental concepts and methodologies in sampling and the inference techniques associated with it.
⦁ Chapter 5, Common Probability Distributions, goes through the most common discrete and continuous distributions, which are the building blocks for more sophisticated reallife empirical distributions.
⦁ Chapter 6, Parametric Estimation, covers a classic and rich topic that solidifies your knowledge of statistics and probability by having you estimate parameters from accessible datasets.
⦁ Chapter 7, Statistical Hypothesis Testing, looks at a must-have skill for any data scientist or data analyst. We will cover the full life cycle of hypothesis testing, from assumptions to interpretation.
⦁ Chapter 8, Statistics for Regression, discusses statistics for regression problems, starting with simple linear regression.
⦁ Chapter 9, Statistics for Classification, explores statistics for classification problems, starting with logistic regression.
⦁ Chapter 10, Statistics for Tree-Based Methods, delves into statistics for tree-based methods, with a detailed walk through of building a decision tree from first principles.
⦁ Chapter 11, Statistics for Ensemble Methods, moves on to ensemble methods, which are meta-algorithms built on top of basic machine learning or statistical algorithms. This chapter is dedicated to methods such as bagging and boosting.
⦁ Chapter 12, Best Practice Collection, introduces several important practice tips based on the author's data science mentoring and practicing experience.
⦁ Chapter 13, Exercises and Projects, includes exercises and project suggestions grouped by chapter.
