Coverart for item
The Resource Data science with Java : practical methods for scientists and engineers, Michael R. Brzustowicz

Data science with Java : practical methods for scientists and engineers, Michael R. Brzustowicz

Label
Data science with Java : practical methods for scientists and engineers
Title
Data science with Java
Title remainder
practical methods for scientists and engineers
Statement of responsibility
Michael R. Brzustowicz
Creator
Author
Subject
Language
eng
Cataloging source
N$T
http://library.link/vocab/creatorName
Brzustowicz, Michael R
Dewey number
005.133
Illustrations
illustrations
Index
index present
LC call number
QA76.73.J39
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/subjectName
  • Java (Computer program language)
  • Data structures (Computer science)
  • COMPUTERS / Programming Languages / JavaScript
  • Data structures (Computer science)
  • Java (Computer program language)
Label
Data science with Java : practical methods for scientists and engineers, Michael R. Brzustowicz
Instantiates
Publication
Copyright
Note
Includes index
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Copyright; Table of Contents; Preface; Who Should Read This Book; Why I Wrote This Book; A Word on Data Science Today; Navigating This Book; Conventions Used in This Book; Using Code Examples; O'Reilly Safari; How to Contact Us; Acknowledgments; Chapter 1. Data I/O; What Is Data, Anyway?; Data Models; Univariate Arrays; Multivariate Arrays; Data Objects; Matrices and Vectors; JSON; Dealing with Real Data; Nulls; Blank Spaces; Parse Errors; Outliers; Managing Data Files; Understanding File Contents First; Reading from a Text File; Reading from a JSON File; Reading from an Image File
  • Writing to a Text FileMastering Database Operations; Command-Line Clients; Structured Query Language; Java Database Connectivity; Visualizing Data with Plots; Creating Simple Plots; Plotting Mixed Chart Types; Saving a Plot to a File; Chapter 2. Linear Algebra; Building Vectors and Matrices; Array Storage; Block Storage; Map Storage; Accessing Elements; Working with Submatrices; Randomization; Operating on Vectors and Matrices; Scaling; Transposing; Addition and Subtraction; Length; Distances; Multiplication; Inner Product; Outer Product; Entrywise Product; Compound Operations
  • Affine TransformationMapping a Function; Decomposing Matrices; Cholesky Decomposition; LU Decomposition; QR Decomposition; Singular Value Decomposition; Eigen Decomposition; Determinant; Inverse; Solving Linear Systems; Chapter 3. Statistics; The Probabilistic Origins of Data; Probability Density; Cumulative Probability; Statistical Moments; Entropy; Continuous Distributions; Discrete Distributions; Characterizing Datasets; Calculating Moments; Descriptive Statistics; Multivariate Statistics; Covariance and Correlation; Regression; Working with Large Datasets; Accumulating Statistics
  • Merging StatisticsRegression; Using Built-in Database Functions; Chapter 4. Data Operations; Transforming Text Data; Extracting Tokens from a Document; Utilizing Dictionaries; Vectorizing a Document; Scaling and Regularizing Numeric Data; Scaling Columns; Scaling Rows; Matrix Scaling Operator; Reducing Data to Principal Components; Covariance Method; SVD Method; Creating Training, Validation, and Test Sets; Index-Based Resampling; List-Based Resampling; Mini-Batches; Encoding Labels; A Generic Encoder; One-Hot Encoding; Chapter 5. Learning and Prediction; Learning Algorithms
  • Iterative Learning ProcedureGradient Descent Optimizer; Evaluating Learning Processes; Minimizing a Loss Function; Minimizing the Sum of Variances; Silhouette Coefficient; Log-Likelihood; Classifier Accuracy; Unsupervised Learning; k-Means Clustering; DBSCAN; Gaussian Mixtures; Supervised Learning; Naive Bayes; Linear Models; Deep Networks; Chapter 6. Hadoop MapReduce; Hadoop Distributed File System; MapReduce Architecture; Writing MapReduce Applications; Anatomy of a MapReduce Job; Hadoop Data Types; Mappers; Reducers; The Simplicity of a JSON String as Text; Deployment Wizardry
Control code
989872333
Dimensions
unknown
Extent
1 online resource (233 pages)
File format
unknown
Form of item
online
Isbn
9781491934067
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations
http://library.link/vocab/ext/overdrive/overdriveId
  • cl0500000866
  • e70235cc-7051-47ae-8547-92180873c56a
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)989872333
Label
Data science with Java : practical methods for scientists and engineers, Michael R. Brzustowicz
Publication
Copyright
Note
Includes index
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Copyright; Table of Contents; Preface; Who Should Read This Book; Why I Wrote This Book; A Word on Data Science Today; Navigating This Book; Conventions Used in This Book; Using Code Examples; O'Reilly Safari; How to Contact Us; Acknowledgments; Chapter 1. Data I/O; What Is Data, Anyway?; Data Models; Univariate Arrays; Multivariate Arrays; Data Objects; Matrices and Vectors; JSON; Dealing with Real Data; Nulls; Blank Spaces; Parse Errors; Outliers; Managing Data Files; Understanding File Contents First; Reading from a Text File; Reading from a JSON File; Reading from an Image File
  • Writing to a Text FileMastering Database Operations; Command-Line Clients; Structured Query Language; Java Database Connectivity; Visualizing Data with Plots; Creating Simple Plots; Plotting Mixed Chart Types; Saving a Plot to a File; Chapter 2. Linear Algebra; Building Vectors and Matrices; Array Storage; Block Storage; Map Storage; Accessing Elements; Working with Submatrices; Randomization; Operating on Vectors and Matrices; Scaling; Transposing; Addition and Subtraction; Length; Distances; Multiplication; Inner Product; Outer Product; Entrywise Product; Compound Operations
  • Affine TransformationMapping a Function; Decomposing Matrices; Cholesky Decomposition; LU Decomposition; QR Decomposition; Singular Value Decomposition; Eigen Decomposition; Determinant; Inverse; Solving Linear Systems; Chapter 3. Statistics; The Probabilistic Origins of Data; Probability Density; Cumulative Probability; Statistical Moments; Entropy; Continuous Distributions; Discrete Distributions; Characterizing Datasets; Calculating Moments; Descriptive Statistics; Multivariate Statistics; Covariance and Correlation; Regression; Working with Large Datasets; Accumulating Statistics
  • Merging StatisticsRegression; Using Built-in Database Functions; Chapter 4. Data Operations; Transforming Text Data; Extracting Tokens from a Document; Utilizing Dictionaries; Vectorizing a Document; Scaling and Regularizing Numeric Data; Scaling Columns; Scaling Rows; Matrix Scaling Operator; Reducing Data to Principal Components; Covariance Method; SVD Method; Creating Training, Validation, and Test Sets; Index-Based Resampling; List-Based Resampling; Mini-Batches; Encoding Labels; A Generic Encoder; One-Hot Encoding; Chapter 5. Learning and Prediction; Learning Algorithms
  • Iterative Learning ProcedureGradient Descent Optimizer; Evaluating Learning Processes; Minimizing a Loss Function; Minimizing the Sum of Variances; Silhouette Coefficient; Log-Likelihood; Classifier Accuracy; Unsupervised Learning; k-Means Clustering; DBSCAN; Gaussian Mixtures; Supervised Learning; Naive Bayes; Linear Models; Deep Networks; Chapter 6. Hadoop MapReduce; Hadoop Distributed File System; MapReduce Architecture; Writing MapReduce Applications; Anatomy of a MapReduce Job; Hadoop Data Types; Mappers; Reducers; The Simplicity of a JSON String as Text; Deployment Wizardry
Control code
989872333
Dimensions
unknown
Extent
1 online resource (233 pages)
File format
unknown
Form of item
online
Isbn
9781491934067
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations
http://library.link/vocab/ext/overdrive/overdriveId
  • cl0500000866
  • e70235cc-7051-47ae-8547-92180873c56a
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)989872333

Library Locations

    • Ellis LibraryBorrow it
      1020 Lowry Street, Columbia, MO, 65201, US
      38.944491 -92.326012
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      W2001 Lafferre Hall, Columbia, MO, 65211, US
      38.946102 -92.330125
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