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A tour of data science : learn R and Python in parallel / Nailong Zhang.

By: Zhang, Nailong [author.]Material type: Text

TextSeries: Publisher: Abingdon, Oxon ; Boca Raton, FL : CRC Press, 2021Edition: First editionDescription: 1 online resource (x, 206 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781003020646; 100302064X; 9781000215274; 100021527X; 9781000215236; 1000215237; 9781000215199; 1000215199Subject(s): COMPUTERS / Programming Languages / Python | COMPUTERS / Programming Languages / General | COMPUTERS / Computer Graphics / Game Programming & Design | Data mining | R (Computer program language) | Python (Computer program language)DDC classification: 006.3/12 LOC classification: QA76.9.D343 | Z458 2021ebOnline resources: Taylor & Francis | OCLC metadata license agreement

Contents:

Assumptions about the readers backgroundBook overviewIntroduction to R/Python ProgrammingCalculatorVariable and TypeFunctionsControl flowsSome built-in data structuresRevisit of variablesObject-oriented programming (OOP) in R/PythonMiscellaneousMore on R/Python ProgrammingWork with R/Python scriptsDebugging in R/PythonBenchmarkingVectorizationEmbarrassingly parallelism in R/PythonEvaluation strategySpeed up with C/C++ in R/PythonA first impression of functional programming Miscellaneousdata.table and pandasSQLGet started with data.table and pandasIndexing & selecting dataAdd/Remove/UpdateGroup byJoinRandom Variables, Distributions & Linear RegressionA refresher on distributionsInversion sampling & rejection samplingJoint distribution & copulaFit a distributionConfidence intervalHypothesis testingBasics of linear regressionRidge regressionOptimization in PracticeConvexityGradient descentRoot-findingGeneral purpose minimization tools in R/PythonLinear programmingMiscellaneousMachine Learning - A gentle introductionSupervised learningGradient boosting machineUnsupervised learningReinforcement learningDeep Q-NetworksComputational differentiationMiscellaneous

Summary: A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.Summary: "A Tour of Data Science : Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source"-- Provided by publisher.

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A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.

"A Tour of Data Science : Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source"-- Provided by publisher.

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