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Statistics for Data Science. Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

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Statistics for Data Science. Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks James D. Miller - okladka książki

Statistics for Data Science. Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks James D. Miller - okladka książki

Statistics for Data Science. Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks James D. Miller - audiobook MP3

Statistics for Data Science. Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks James D. Miller - audiobook CD

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Do przechowalni

Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on.

This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.

By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.

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O autorze książki

James D. Miller is an IBM certified expert, Master Consultant, Application/System Architect with +35 years of applications & system design/development experience across multiple platforms, technologies and data formats, including Big Data.

His experience includes IBM Planning Analytics, BI, Web architecture & design, systems analysis, GUI design & testing, Data modeling, design, and development of OLAP, Client/Server, Web & Mainframe applications and systems utilizing: Planning Analytics Workspace (PAW), IBM Watson Analytics, Cognos BI & TM1, Framework Manager, dynaSight/ArcPlan, ASP, DHTML, XML, MS Visual Basic, VBA, PERL, R, SPLUNK, MS SQL Server, ORACLE, etc.

He has authored numerous books, including Implementing Splunk - Second Edition; Mastering Splunk; Hands-On Machine Learning with IBM Watson; IBM Watson Projects; Statistics for Data Science; Mastering Predictive Analytics with R - Second Edition and others.

Project areas include those with Data Analytics, Planning Analytics, and FOPM projects, holding various roles from architect, developer, technical and project leader.

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