Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts in social media mining. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language.
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The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. Learn how to use a problem's "weight" against itself. Learn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at all. Its function is something like a traditional textbook — it will provide the detail and background theory to support the School of Data courses and challenges.
Create and publish your own interactive data visualization projects on the Web—even if you have little or no experience with data visualization or web development. Learn about Cloudera Impala--an open source project that's opening up the Apache Hadoop software stack to a wide audience of database analysts, users, and developers. MapReduce  is a programming model for expressing distributed computations on massive amounts of data and an execution framework for large-scale data processing on clusters of commodity servers. It was originally developed by Google It aims to make Hadoop knowledge accessible to a wider audience, not just to the highly technical.
Intro to Hadoop - An open-source framework for storing and processing big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines. This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop. In this in-depth report, data scientist DJ Patil explains the skills,perspectives, tools and processes that position data science teams for success.
The Data Science Handbook is a compilation of in-depth interviews with 25 remarkable data scientists, where they share their insights, stories, and advice. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you.
Useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. Practical programming for total beginners. In Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required.
This is a hands-on guide to Python 3 and its differences from Python 2. Each chapter starts with a real, complete code sample, picks it apart and explains the pieces, and then puts it all back together in a summary at the end. The first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know to analyze their own data using the R language. Each chapter gives you the complete source code for a new game and teaches the programming concepts from these examples.
I Dani started teaching the introductory statistics class for psychology students offered at the University of Adelaide, using the R statistical package as the primary tool. These are my own notes for the class which were trans-coded to book form.
First Steps In Research and Statistics
Introduction to computer science using the Python programming language. It covers the basics of computer programming in the first part while later chapters cover basic algorithms and data structures. This is a hands-on introduction to the Python programming language, written for people who have no experience with programming whatsoever.
After all, everybody has to start somewhere. This book is NOT introductory. The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied. If you need help writing programs in Python 3, or want to update older Python 2 code, this book is just the ticket. Packed with practical recipes written and tested with Python 3.
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For experienced Python developers. 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.
Individual Differences Practical Workbook: A Lecturer's Guide | Advance HE
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. 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. 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.
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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.