book

Data Science with Java

by Michael R. Brzustowicz

June 2017

Beginner to intermediate

233 pages

5h 57m

English

O'Reilly Media, Inc.

Read now

Unlock full access

Who Should Read This BookWhy I Wrote This BookA Word on Data Science TodayNavigating This BookConventions Used in This BookUsing Code ExamplesO’Reilly SafariHow to Contact UsAcknowledgments
What Is Data, Anyway?Data ModelsUnivariate ArraysMultivariate ArraysData ObjectsMatrices and VectorsJSONDealing with Real DataNullsBlank SpacesParse ErrorsOutliersManaging Data FilesUnderstanding File Contents FirstReading from a Text FileReading from a JSON FileReading from an Image FileWriting to a Text FileMastering Database OperationsCommand-Line ClientsStructured Query LanguageJava Database ConnectivityVisualizing Data with PlotsCreating Simple PlotsPlotting Mixed Chart TypesSaving a Plot to a File
Building Vectors and MatricesArray StorageBlock StorageMap StorageAccessing ElementsWorking with SubmatricesRandomizationOperating on Vectors and MatricesScalingTransposingAddition and SubtractionLengthDistancesMultiplicationInner ProductOuter ProductEntrywise ProductCompound OperationsAffine TransformationMapping a FunctionDecomposing MatricesCholesky DecompositionLU DecompositionQR DecompositionSingular Value DecompositionEigen DecompositionDeterminantInverseSolving Linear Systems
The Probabilistic Origins of DataProbability DensityCumulative ProbabilityStatistical MomentsEntropyContinuous DistributionsDiscrete DistributionsCharacterizing DatasetsCalculating MomentsDescriptive StatisticsMultivariate StatisticsCovariance and CorrelationRegressionWorking with Large DatasetsAccumulating StatisticsMerging StatisticsRegressionUsing Built-in Database Functions
Transforming Text DataExtracting Tokens from a DocumentUtilizing DictionariesVectorizing a DocumentScaling and Regularizing Numeric DataScaling ColumnsScaling RowsMatrix Scaling OperatorReducing Data to Principal ComponentsCovariance MethodSVD MethodCreating Training, Validation, and Test SetsIndex-Based ResamplingList-Based ResamplingMini-BatchesEncoding LabelsA Generic EncoderOne-Hot Encoding
Learning AlgorithmsIterative Learning ProcedureGradient Descent OptimizerEvaluating Learning ProcessesMinimizing a Loss FunctionMinimizing the Sum of VariancesSilhouette CoefficientLog-LikelihoodClassifier AccuracyUnsupervised Learningk-Means ClusteringDBSCANGaussian MixturesSupervised LearningNaive BayesLinear ModelsDeep Networks
Hadoop Distributed File SystemMapReduce ArchitectureWriting MapReduce ApplicationsAnatomy of a MapReduce JobHadoop Data TypesMappersReducersThe Simplicity of a JSON String as TextDeployment WizardryMapReduce ExamplesWord CountCustom Word CountSparse Linear Algebra
Anscombe’s QuartetSentimentGaussian MixturesIrisMNIST

Overview

Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today’s data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java.

You’ll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you’ll find code examples you can use in your applications.

Examine methods for obtaining, cleaning, and arranging data into its purest form
Understand the matrix structure that your data should take
Learn basic concepts for testing the origin and validity of data
Transform your data into stable and usable numerical values
Understand supervised and unsupervised learning algorithms, and methods for evaluating their success
Get up and running with MapReduce, using customized components suitable for data science algorithms