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117 results for "learning analytics"
Handbook of Learning Analytics 2017 By Charles Lang et al.
Paperback: $20.00
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The Handbook of Learning Analytics is designed to meet the needs of a new and growing field. It aims to balance rigor, quality, open access and breadth of appeal and was devised to be an introduction... More > to the current state of research. The Handbook is a snapshot of the field in 2017 and features a range of prominent authors from the learning analytics and educational data mining research communities. The chapters have been peer reviewed by committed members of these fields and are being published with the endorsement of both the Society for Learning Analytics Research and the International Society for Educational Data Mining. We hope you will find the Handbook of Learning Analytics a useful and informative resource.< Less
Data Analytics, Bioinformatics, and Machine Learning By Stephen Winters-Hilt
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In this book I describe how to use computational, algorithmic, statistical, and informatics methods to analyze any data that is captured in digital form, whether it be text, sequential data in... More > general (such as experimental observations over time, or stock market and econometric histories), or symbolic data such as genome and transcriptome data.< Less
Data Analytics, Bioinformatics, and Machine Learning By Stephen Winters-Hilt
Hardcover: List Price: $137.00 $68.50 | You Save: 50%
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In this book I describe how to use computational, algorithmic, statistical, and informatics methods to analyze any data that is captured in digital form, whether it be text, sequential data in... More > general (such as experimental observations over time, or stock market and econometric histories) or symbolic data, especially genomes and transcriptomes.< Less
Predictive Analytics using R By Jeffrey Strickland
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This book is about predictive analytics. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this a survey of predictive modeling. A predictive model is a... More > statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, one should have a basic understanding of mathematical statistics — it is an advanced book. Some theoretical foundations are laid out but not proven, but references are provided for additional coverage. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. You may download R, from a preferred CRAN mirror at http://www.r-project.org/. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. One could use this a textbook with problem solving in R—but there are no “by-hand” exercises.< Less
An Introduction to Business Analytics By Ger Koole
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Business Analytics (BA) is about turning data into decisions. This book covers the full range of BA topics, including statistics, machine learning and optimization, in a way that makes them... More > accessible to a broader audience. Decision makers will gain enough insight into the subject to have meaningful discussions with machine learning specialists, and those starting out as data scientists will benefit from an overview of the field and take their first steps as business analytics specialist. Through this book and the various exercises included, you will be equipped with an understanding of BA, while learning R, a popular tool for statistics and machine learning.< Less
Predictive Modeling and Analytics By Jeffrey Strickland
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This book is about predictive modeling. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this as a survey of predictive models, both statistical and... More > machine learning. We define A predictive model as a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, the reader should have a basic understanding of statistics (statistical inference, models, tests, etc.)—this is an advanced book. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. One could use this as a textbook with problem solving in R (there are no “by-hand” exercises).< Less
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Data Analytics Using Open-Source Tools By Jeffrey Strickland
Hardcover: List Price: $60.45 $36.27 | You Save: 40%
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This book is about Data Analytics. In that respect, it is like others. What distinguishes it from the rest is the variety of open-source tool applications. This book incorporates the use of R Studio,... More > Python, SAS Studio (University Edition), and KNIME. This book is also about manipulating Big Data. Apache Hadoop on Hortonworks Sandbox is introduced and we manage, move, handle, and transform data using Apache Hive, Apache Spark, MapReduce and TEZ, with terminal shell commands and Ambari. We show you how to set up a virtual machine in Microsoft Azure. We then use the data in later chapters for modeling. We cover Descriptive Modeling and Predictive. The content includes Support Vector Machines, Decision Tree learning, Random Forests, Naïve and Empirical Bayes, Gradient Boosting, Cluster Modeling, Generalized Linear Models, Logistic Regression, and Artificial Neural Networks. Every chapter includes completely worked examples using one or more open-source tools.< Less
Data Analytics Using Open-Source Tools By Jeffrey Strickland
Paperback: List Price: $46.45 $27.87 | You Save: 40%
Prints in 3-5 business days
This book is about Data Analytics. In that respect, it is like others. What distinguishes it from the rest is the variety of open-source tool applications. This book incorporates the use of R Studio,... More > Python, SAS Studio (University Edition), and KNIME. This book is also about manipulating Big Data. Apache Hadoop on Hortonworks Sandbox is introduced and we manage, move, handle, and transform data using Apache Hive, Apache Spark, MapReduce and TEZ, with terminal shell commands and Ambari. We show you how to set up a virtual machine in Microsoft Azure. We then use the data in later chapters for modeling. We cover Descriptive Modeling and Predictive. The content includes Support Vector Machines, Decision Tree learning, Random Forests, Naïve and Empirical Bayes, Gradient Boosting, Cluster Modeling, Generalized Linear Models, Logistic Regression, and Artificial Neural Networks. Every chapter includes completely worked examples using one or more open-source tools.< Less
Energy and Analytics: Big Data & Technology Integration By John J. “Jack” McGowan, CEM
eBook (ePub): $99.99
The goal of this book is to provide an energy manager's master reference for learning how to leverage "big data" style analytics to manage and coordinate the key issues in both energy... More > supply and demand for a facility - and thereby to offer a more holistic approach for optimizing energy-related facility performance. The author provides the reader with a detailed explanation of the underlying systems technologies which enable big data in buildings, and how these technologies can be utilized to provide added cost benefit from energy efficiency, onsite solar and electricity markets. The book was written to serve as a primer on building automation systems (BAS) standards, web services, and electricity markets and programs, providing a complete tutorial on energy analytics hardware, software and internet-enabled offerings which successful energy managers must understand today.< Less

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