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50 Essential Concepts 
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Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. Why exploratory data analysis is a key preliminary step in data science; How random sampling can reduce bias and yield a higher quality dataset, even with big data; How the principles of experimental design yield definitive answers to questions; How to use regression to estimate outcomes and detect anomalies; Key classification techniques for predicting which categories a record belongs to; Statistical machine learning methods that "learn" from data; Unsupervised learning methods for extracting meaning from unlabeled data. 4 5 170 Similar BooksPractical Statistics for Data Scientists, 2nd Edition
by Peter Bruce, Andrew Bruce, Peter Gedeck Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical meth... Price: $52.29 | Publisher: O'Reilly Media | Release: 2020
Clojure for Data Science
by Henry Garner The term "data science" has been widely used to define this new profession that is expected to interpret vast datasets and translate them to improved decision-making and performance. Clojure is a powerful language that combines the interactivity of a scripting language with the speed of a compiled language. Together with its ric... Price: $35.99 | Publisher: Packt Publishing | Release: 2015 Data Science with Java
by Michael Brzustowicz Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today's data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz... Price: $27.33 | Publisher: O'Reilly Media | Release: 2017 Practical Simulations for Machine Learning
by Paris Buttfield-Addison, Mars Buttfield-Addison, Tim Nugent, Jon Manning Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional mach... Price: $59.99 | Publisher: O'Reilly Media | Release: 2022 Essential Math for Data Science
by Thomas Nield Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain pract... Price: $44.67 | Publisher: O'Reilly Media | Release: 2022 Python for Data Analysis
by Wes McKinney Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and libraries you'll need to ... Price: $11.99 | Publisher: O'Reilly Media | Release: 2012 Learn R for Applied Statistics
by Goh Hui Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R's syntax, you will wo... Price: $29.23 | Publisher: Apress | Release: 2019 Feature Engineering for Machine Learning
by Alice Zheng, Amanda Casari Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you'll learn techniques for extracting and transforming features - the numeric representations of raw data - into formats for machine-learning models. Each chapter guides you through a single... Price: $29.93 | Publisher: O'Reilly Media | Release: 2018 |
Practical statistics for data scientists 2nd edition pdf github
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