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Head First Data Analysis

Head First Data Analysis
Author: Michael Milton
Publisher: "O'Reilly Media, Inc."
Total Pages: 486
Release: 2009-07-24
Genre: Business & Economics
ISBN: 0596153937

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A guide for data managers and analyzers. It shares guidelines for identifying patterns, predicting future outcomes, and presenting findings to others.


Head First Statistics

Head First Statistics
Author: Dawn Griffiths
Publisher: "O'Reilly Media, Inc."
Total Pages: 721
Release: 2008-08-26
Genre: Mathematics
ISBN: 059680086X

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A comprehensive introduction to statistics that teaches the fundamentals with real-life scenarios, and covers histograms, quartiles, probability, Bayes' theorem, predictions, approximations, random samples, and related topics.


Head First Object-Oriented Analysis and Design

Head First Object-Oriented Analysis and Design
Author: Brett McLaughlin
Publisher: "O'Reilly Media, Inc."
Total Pages: 634
Release: 2007
Genre: Computers
ISBN: 0596008678

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Provides information on analyzing, designing, and writing object-oriented software.


Head First Excel

Head First Excel
Author: Michael Milton
Publisher: "O'Reilly Media, Inc."
Total Pages: 438
Release: 2010-03-18
Genre: Computers
ISBN: 0596807694

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A brain friendly guide to Excel.


Head First SQL

Head First SQL
Author: Lynn Beighley
Publisher: "O'Reilly Media, Inc."
Total Pages: 607
Release: 2007-08-28
Genre: Computers
ISBN: 0596526849

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With its visually rich format designed for the way the brain works, this series of engaging narrative lessons that build on each other gives readers hands-on experience working with the SQL database language.


Head First Data Analysis

Head First Data Analysis
Author: Michael Milton
Publisher: "O'Reilly Media, Inc."
Total Pages: 488
Release: 2009-07-17
Genre: Business & Economics
ISBN: 1449392024

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A guide for data managers and analyzers. It shares guidelines for identifying patterns, predicting future outcomes, and presenting findings to others.


Data Smart

Data Smart
Author: John W. Foreman
Publisher: John Wiley & Sons
Total Pages: 432
Release: 2013-10-31
Genre: Business & Economics
ISBN: 1118839862

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Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.


Data Analysis for Social Science

Data Analysis for Social Science
Author: Elena Llaudet
Publisher: Princeton University Press
Total Pages: 256
Release: 2022-11-29
Genre: Computers
ISBN: 0691199434

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"Data analysis has become a necessary skill across the social sciences, and recent advancements in computing power have made knowledge of programming an essential component. Yet most data science books are intimidating and overwhelming to a non-specialist audience, including most undergraduates. This book will be a shorter, more focused and accessible version of Kosuke Imai's Quantitative Social Science book, which was published by Princeton in 2018 and has been adopted widely in graduate level courses of the same title. This book uses the same innovative approach as Quantitative Social Science , using real data and 'R' to answer a wide range of social science questions. It assumes no prior knowledge of statistics or coding. It starts with straightforward, simple data analysis and culminates with multivariate linear regression models, focusing more on the intuition of how the math works rather than the math itself. The book makes extensive use of data visualizations, diagrams, pictures, cartoons, etc., to help students understand and recall complex concepts, provides an easy to follow, step-by-step template of how to conduct data analysis from beginning to end, and will be accompanied by supplemental materials in the appendix and online for both students and instructors"--


Foundations of Data Science

Foundations of Data Science
Author: Avrim Blum
Publisher: Cambridge University Press
Total Pages: 433
Release: 2020-01-23
Genre: Computers
ISBN: 1108617360

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This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.


Head First C#

Head First C#
Author: Jennifer Greene
Publisher: "O'Reilly Media, Inc."
Total Pages: 1729
Release: 2013-08-23
Genre: Computers
ISBN: 144935887X

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Head First C# is a complete learning experience for learning how to program with C#, XAML, the .NET Framework, and Visual Studio. Fun and highly visual, this introduction to C# is designed to keep you engaged and entertained from first page to last. Updated for Windows 8.1 and Visual Studio 2013, and includes projects for all previous versions of Windows (included in the book, no additional downloading or printing required). You’ll build a fully functional video game in the opening chapter, and then learn how to use classes and object-oriented programming, draw graphics and animation, and query data with LINQ and serialize it to files. And you'll do it all by creating games, solving puzzles, and doing hands-on projects. By the time you're done, you'll be a solid C# programmer—and you'll have a great time along the way! Create a fun arcade game in the first chapter, and build games and other projects throughout the book Learn how to use XAML to design attractive and interactive pages and windows Build modern Windows Store apps using the latest Microsoft technology Learn WPF (Windows Presentation Foundation) using the downloadable WPF Learner's Guide Using the Model-View-ViewModel (MVVM) pattern to create robust architecture Build a bonus Windows Phone project and run it in the Visual Studio Windows Phone emulator Projects in the book work with all editions of Visual Studio, including the free Express editions.