Books

Google engineers are regular expression masters. Do you want to become one, too? The Smartest Way to Learn Python Regex transforms you into a regular expression master. The book leverages an innovative learning approach: (1) read a chapter, (2) watch a course video, and (3) solve a code puzzle. It's fun!

Fear of missing out on data science and machine learning?This ​eBook ​gives you a fun way to start learning data science with Python. ​It gives you a thorough introduction Python's most important library for data science: NumPy. 100% Based on puzzle-based learning - scientifically proven to generate ​44% better learning retention and ​efficiency.​

Roger Peng and Hilary Parker started the Not So Standard Deviations podcast in 2015, a podcast dedicated to discussing the backstory and day to day life of data scientists in academia and industry. This book collects many of their conversations about data science and how it works (and sometimes doesn't work) in the real world.

This book describes the algorithms and procedures used to fit statistical models to data. The material covered is taught in the Advanced Statistical Computing course in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health.

Greenhouse gas emissions have caused considerable changes in climate, including increased surface air temperatures and rising sea levels. This e-textbook presents a series of laboratory exercises in R that teach the Earth science and statistical concepts needed for assessing climate-related risks. These exercises are intended for upper-level undergraduates, beginning graduate students, and professionals in other areas who wish to gain insight into academic climate risk analysis.

The hardest part of stats is understanding how it all fits together. If you want a book that takes you from the very basics all the way through to the most advanced concepts, this is for you!

We all use data every day, often without realizing it. The Data Mind reveals how paying attention to patterns, asking better questions, and thinking more consciously can change the way we make decisions, understand ourselves, and see the world around us.

Longitudinal Data Analysis Using R is a practical, end-to-end guide to longitudinal data analysis that helps you move from raw data to credible substantive conclusions. It tackles the real pain points researchers face in longitudinal analysis, such as data preparation, exploring change over time, model choice, missing data, and interpretation, using clear explanations, real-world data, and fully reproducible R code.

Is There a Six-Figure Freelance Developer in You?Leaving the Rat Race with Python shows you how to nurture, grow, and harness your work-from-home coding business online --- and live the good life!It's an insider's guide to freelance developing. Start your new thriving coding business now!

Are you an above-average Python coder? Prove it! Coffee Break Python - Mastery Workout helps you boost your Python skills and reach mastery level. The approach is simple: you solve 99 really hard Python puzzles that get harder as you progress with the book. A clear path to Python mastery!

Readers will learn discrete mathematical abstracts as well as its implementation in algorithm and data structures shown in various programming languages, such as C, C++, PHP, Java, C#, Python and Dart. This book combines two major components of Mathematics and Computer Science under one roof.

A series on writing effective, idiomatic pandas.

This book introduces the topic of Developing Data Products in R. A data product is the ideal output of a Data Science experiment. This book is based on the Coursera Class "Developing Data Products" as part of the Data Science Specialization. Particular emphasis is paid to developing Shiny apps and interactive graphics.

Η πρώτη πλήρης βιβλιομετρική αποτίμηση κάθε ελληνικού πανεπιστημιακού Τμήματος και των μόνιμων μελών ΔΕΠ: 23 πανεπιστήμια, 339 Τμήματα και 8.809 μέλη, σε τρεις βιβλιομετρικές πηγές.

The first complete bibliometric evaluation of every Greek university department and its tenured faculty: 23 universities, 339 departments and 8,809 faculty members, ranked across three bibliometric sources.