Showing results for "james c mott"
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Statistics for Data Science
Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks
2017
EN
Get your statistics basics right before diving into the world of data science Key FeaturesNo need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs;Implement statistics in data science tasks such as data cleaning, mining, and analysisLearn all about probability, statistics, numerical computations, and more with the help of R programsBook DescriptionData sc...
Data Analysis with IBM SPSS Statistics
Implementing data modeling, descriptive statistics and ANOVA
2017
EN
Master data management & analysis techniques with IBM SPSS Statistics 24Key Features\[\*\]Leverage the power of IBM SPSS Statistics to perform efficient statistical analysis of your data\[\*\]Choose the right statistical technique to analyze different types of data and build efficient models from your data with ease\[\*\]Overcome any hurdle that you might come across while learning the different SPSS Statistics concepts with clear instructions,...
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Machine Learning with R
R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required – this book will take you methodically through every stage of applying machine learning.
2013
EN
Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain...
2014
EN
Explains the various techniques of PPM development, simulation and optimization. All the explanations are given with IT industry and usage of alternate techniques to build PPM to suit even smaller organizations. Application of Statistical, Probablistic and Simulation models are elaborated.
2011
EN
Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives.This book looks at both classical and ...
2009
EN
The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. ...
2017
EN
Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded second edition of Fundamentals of Predictive Analytics with JMP(R) bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics. Going beyond the theoretical foundation, this book gives you the technical k...
2016
EN
Big Data Analytics Made Easy is a must-read for everybody as it explains the power of Analytics in a simple and logical way along with an end to end code in R. Even if you are a novice in Big Data Analytics, you will still be able to understand the concepts explained in this book. If you are already working in Analytics and dealing with Big Data, you will still find this book useful, as it covers exhaustive Data Mining Techniques, which are considered to be Advanced topics. It covers Ma...
Data Analysis with STATA
Explore the big data field and learn how to perform data analytics and predictive modelling in STATA
2015
EN
Explore the big data field and learn how to perform data analytics and predictive modelling in STATA Key FeaturesBook DescriptionSTATA is an integrated software package that provides you with everything you need for data analysis, data management, and graphics. STATA also provides you with a platform to efficiently perform simulation, regression analysis (linear and multiple) \[and custom programming. This book covers data management, graphs visualizat...
Mastering Predictive Analytics with R
Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts
2015
EN
Key FeaturesBook DescriptionThis book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions,...
Introduction to R for Business Intelligence
Profit optimization using data mining, data analysis, and Business Intelligence
2016
EN
Learn how to leverage the power of R for Business IntelligenceKey Features\[\*\] Use this easy-to-follow guide to leverage the power of R analytics and make your business data more insightful.\[\*\] This highly practical guide teaches you how to develop dashboards that help you make informed decisions using R.\[\*\] Learn the A to Z of working with data for Business Intelligence with the help of this comprehensive guide.Book Descr...
Machine Learning for Hackers
Case Studies and Algorithms to Get You Started
2012
EN
If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classificat...











