The project is about random code written for practice. Description of the code is within the files as comments. - samalallover/Random-Scripts. Here is a great collection of eBooks written on the topics of Data Science, Business Analytics, Data Mining, Big Data, Machine Learning, Algorithms, Data. Nov 30, Unleash the power of Python and its robust data science yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls. Download Links.
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Editorial Reviews. Review. "A must-read resource for anyone who is serious about embracing Word Wise: Enabled; Enhanced Typesetting: Enabled; Page Flip: Enabled; Due to its large file size, this book may take longer to download. This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the data-analytic thinking. Data Science for Business, by Foster Provost and Tom Fawcett, is for those who need to understand data science as well as those who want to develop.
Note that while every book here is provided for free, consider purchasing the hard copy if you find any particularly helpful. In many cases you will find Amazon links to the printed version, but bear in mind that these are affiliate links, and purchasing through them will help support not only the authors of these books, but also LearnDataSci. Thank you for reading, and thank you in advance for helping support this website. Comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Learning and Intelligent Optimization LION is the combination of learning from data and optimization applied to solve complex and dynamic problems. Learn about increasing the automation level and connecting data directly to decisions and actions.
This book is designed to introduce students to programming and computational thinking through the lens of exploring data. You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet. This is a simple book to learn the Python programming language, it is for the programmers who are new to Python. This book describes Python, an open-source general-purpose interpreted programming language available for a broad range of operating systems.
This book describes primarily version 2, but does at times reference changes in version 3. The aim of this Wikibook is to be the place where anyone can share his or her knowledge and tricks on R.
It is supposed to be organized by task but not by discipline. We try to make a cross-disciplinary book, i. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. My intent is to present a relatively brief, non-jargony overview of how practicing epidemiologists can apply some of the extremely powerful spatial analytic tools that are easily available to them.
By taking you through the development of a real web application from beginning to end, this hands-on guide demonstrates the practical advantages of test-driven development TDD with Python.
Python: Real-World Data Science (ePUB)
An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. This hands-on guide takes you through Python a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design.
Updated to Python 3. This is an introduction to the basic concepts of linear algebra, along with an introduction to the techniques of formal mathematics.
Python: Real-World Data Science (ePUB) | BookFlare
It has numerous worked examples, exercises and complete proofs, ideal for independent study. This text gives a brisk and engaging introduction to the mathematics behind the recently established field of Applied Topology.
This text has been written in clear and accurate language that students can read and comprehend. The author has minimized the number of explicitly state theorems and definitions, in favor of dealing with concepts in a more conversational manner. This book is designed for an introductory probability course at the university level for sophomores, juniors, and seniors in mathematics, physical and social sciences, engineering, and computer science.
This book gives a self- contained treatment of linear algebra with many of its most important applications. It is very unusual if not unique in being an elementary book which does not neglect arbitrary fields of scalars and the proofs of the theorems. The probability and statistics cookbook is a succinct representation of various topics in probability theory and statistics. It provides a comprehensive mathematical reference reduced to its essence, rather than aiming for elaborate explanations.
Get started with O'Reilly's Graph Databases and discover how graph databases can help you manage and query highly connected data. Essentials of the MongoDB system. Starting with creating a MongoDB database, you'll learn how to make collections and interact with their data, how to build a console application to interact with binary and image collection data, and much more.
This tutorial will give you a quick start to SQL. It covers most of the topics required for a basic understanding of SQL and to get a feel of how it works. It retains some similarities with relational databases which, in my opinion, makes it a great choice for anyone who is approaching the NoSQL world. Suitable for either a service course for non-statistics graduate students or for statistics majors.
This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, and much more. This is a textbook aimed at junior to senior undergraduate students and first-year graduate students.
It presents artificial intelligence AI using a coherent framework to study the design of intelligent computational agents. The foundations for inference are provided using randomization and simulation methods.
Once a solid foundation is formed, a transition is made to traditional approaches, where the normal and t distributions are used for hypothesis testing and Probability is optional, inference is key, and we feature real data whenever possible. Files for the entire book are freely available at openintro. This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework.
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While the approach is statistical, the emphasis is on concepts rather than mathematics. Think Bayes is an introduction to Bayesian statistics using computational methods.
The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.
This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. Well, there you have it. Thousands of e-pages to read through. We hope there's a data science book here for everyone, no matter what level you're starting at.
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Mark Pilgrim is a developer advocate for open source and open standards. Albert Sweigart, is a software developer in San Francisco, California. Anand conducts Python training classes on a semi-regular basis in Bangalore, India.
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Abbott, Dean Are you looking for some of good case studies that highlight how large companies leverage Big Data for driving productivity? Check out these There are plenty of Big Data quotes on the web!
But do you know which are the most powerful and thought-provoking ones? Here is Posted on May 6, Nov 5, Author Guest. The news on my timeline consisted mostly Posted on Feb 6, Nov 30, Author Guest.
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