Data is collected constantly: how far we travel, who we interact with online and where we spend our money. Every bit of data has a story to tell but isolated, these morsels of information lie dormant and useless, like separated Lego blocks in the closet.
Written by the author of Amazon Best Seller Machine Learning for Absolute Beginners, this book guides beginners through the fundamentals of inferential and descriptive statistics with a mix of practical demonstrations, visual examples, historical origins, and plain English explanations. As a resource for beginners, this book won't teach you how to beat the market or predict the next U.S. election but provides a concise and simple-to-understand suplement to a standard textbook.
The book includes an introduction to important techniques used to infer predictions from data, such as hypothesis testing, linear regression analysis, confidence intervals, probability theory, and data distribution. Descriptive statistics techniques such as central tendency measures and standard deviation are also covered in this book.
Full Overview of Book Themes
Historical Development of Statistics
Data Sampling
Central Tendency Measures
Measures Of Spread
Measures Of Position
Designing Hypothesis Tests
Probability & Bayes Theory
Regression Analysis
Clustering Analysis
As the launch pad to quantitative research, business optimization or a promising career in data science, it's never been a better time to brush up on statistics or learn these concepts for the very first time.