ENGLISH

A Tour Of Data Science: Learn R And Python In Parallel

By Nailong Zhang

Book information

Publisher
CRC Press/Taylor & Francis Group
Year
2021
ISBN
0367897067, 9780367897062, 0367895862, 9780367895860, 100302064X, 9781003020646, 100021527X, 9781000215274, 1000215237, 9781000215236, 1000215199, 9781000215199
Language
english
Format
PDF
Filesize
7 MB (6849210 bytes)
Series
Chapman & Hall/CRC Big Data Series
Edition
1st Edition
Pages
217\217
Time added
2021-02-10 22:45:41
Data Mining; R (Computer Program Language); Python

Description

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. Cover......Page 1 Half Title......Page 2 Title Page......Page 4 Copyright Page......Page 5 Dedication......Page 6 Table of Contents......Page 8 0.2 Book Overview......Page 10 1.1 Calculator......Page 12 1.2 Variable and Type......Page 14 1.3 Functions......Page 15 1.4 Control Flows......Page 17 1.5 Some Built-In Data Structures......Page 20 1.6 Revisit of Variables......Page 32 1.7 Object-Oriented Programming (OOP) In R/Python......Page 41 1.8 Miscellaneous......Page 46 2.1 Work with R/Python Scripts......Page 48 2.2 Debugging in R/Python......Page 51 2.3 Benchmarking......Page 58 2.4 Vectorization......Page 60 2.5 Embarrassingly Parallelism in R/Python......Page 68 2.6 Evaluation Strategy......Page 72 2.7 Speed Up With C/C++ in R/Python......Page 74 2.8 A First Impression of Functional Programming......Page 76 2.9 Miscellaneous......Page 80 3.1 SQL......Page 82 3.2 Get Started with data.table and Pandas......Page 85 3.3 Indexing & Selecting Data......Page 87 3.4 Add/Remove/Update......Page 97 3.5 Group by......Page 102 3.6 Join......Page 104 4.1 A Refresher on Distributions......Page 110 4.2 Inversion Sampling & Rejection Sampling......Page 113 4.3 Joint Distribution & Copula......Page 117 4.4 Fit a Distribution......Page 121 4.5 Confidence Interval......Page 122 4.6 Hypothesis Testing......Page 125 4.7 Basics of Linear Regression......Page 133 4.8 Ridge Regression......Page 138 5.1 Convexity......Page 144 5.2 Gradient Descent......Page 145 5.3 Root-Finding......Page 152 5.4 General Purpose Minimization Tools in R/Python......Page 154 5.5 Linear Programming......Page 160 5.6 Miscellaneous......Page 164 6.1 Supervised Learning......Page 172 6.2 Gradient Boosting Machine......Page 180 6.3 Unsupervised Learning......Page 199 6.4 Reinforcement Learning......Page 206 6.5 Deep Q-Networks......Page 208 6.6 Computational Differentiation......Page 211 6.7 Miscellaneous......Page 213 Bibliography......Page 214 Index......Page 216

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