Tour of Data Science

Learn R and Python in Parallel

AvNailong Zhang

Häftad, Engelska, 2020

Del i serien Chapman & Hall/CRC Data Science Series

749 kr

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Beskrivning

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 parallelCover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandasProvides a concise and accessible presentationIncludes 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.

Produktinformation

Utgivningsdatum:2020-11-12
Mått:178 x 254 x 12 mm
Vikt:386 g
Format:Häftad
Språk:Engelska
Serie:Chapman & Hall/CRC Data Science Series
Antal sidor:216
Förlag:Taylor & Francis Ltd
ISBN:9780367895860

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Mer om författaren

Nailong Zhang is lead Data Scientist at Mass Mutual Life Insurance Company.

Innehållsförteckning

Assumptions about the reader’s backgroundBook overview Introduction to R/Python Programming Calculator Variable and TypeFunctions Control flowsSome built-in data structures Revisit of variables Object-oriented programming (OOP) in R/Python Miscellaneous More on R/Python Programming Work with R/Python scripts Debugging in R/Python Benchmarking Vectorization Embarrassingly parallelism in R/Python Evaluation strategySpeed up with C/C++ in R/PythonA first impression of functional programming Miscellaneous data.table and pandasSQL Get started with data.table and pandas Indexing & selecting data Add/Remove/UpdateGroup by Join Random Variables, Distributions & Linear Regression A refresher on distributions Inversion sampling & rejection sampling Joint distribution & copula Fit a distribution Confidence intervalHypothesis testing Basics of linear regression Ridge regression Optimization in PracticeConvexity Gradient descent Root-finding General purpose minimization tools in R/Python Linear programming Miscellaneous Machine Learning - A gentle introduction Supervised learning Gradient boosting machine Unsupervised learning Reinforcement learning Deep Q-Networks Computational differentiation Miscellaneous