book

Data Science with Java

by Michael R. Brzustowicz

June 2017

Beginner to intermediate

233 pages

5h 57m

English

O'Reilly Media, Inc.

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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

Content preview from Data Science with Java

Chapter 2. Linear Algebra

Now that we have spent a whole chapter acquiring data in some format or another, we will most likely end up viewing the data (in our minds) in the form of spreadsheet. It is natural to envision the names of each column going across from left to right (age, address, ID number, etc.), with each row representing a unique record or data point. Much of data science comes down to this exact formulation. What we are seeking to find is a relationship between any number of columns of interest (which we will call variables) and any number of columns that indicate a measurable outcome (which we will call responses).

Typically, we use the letter to denote the variables, and for the responses. Likewise, the responses can be designated by a matrix Y that has a number of columns and must have the same number of rows as X does. Note that in many cases, there is only one dimension of response variable such that . However, it helps to generalize linear algebra problems to arbitrary ...