| Description |
1 online resource (x, 206 pages) |
| Series |
Chapman & Hall/CRC Data Science Series |
|
Chapman & Hall/CRC data science series.
|
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- 0.1 Assumptions About the Reader's Background -- 0.2 Book Overview -- Chapter 1 Introduction to R/Python Programming -- 1.1 Calculator -- 1.2 Variable and Type -- 1.3 Functions -- 1.4 Control Flows -- 1.5 Some Built-In Data Structures -- 1.6 Revisit of Variables -- 1.7 Object-Oriented Programming (OOP) In R/Python -- 1.8 Miscellaneous -- Chapter 2 More on R/Python Programming -- 2.1 Work with R/Python Scripts -- 2.2 Debugging in R/Python -- 2.3 Benchmarking -- 2.4 Vectorization |
|
2.5 Embarrassingly Parallelism in R/Python -- 2.6 Evaluation Strategy -- 2.7 Speed Up With C/C++ in R/Python -- 2.8 A First Impression of Functional Programming -- 2.9 Miscellaneous -- Chapter 3 data. table and pandas -- 3.1 SQL -- 3.2 Get Started with data.table and Pandas -- 3.3 Indexing & Selecting Data -- 3.4 Add/Remove/Update -- 3.5 Group by -- 3.6 Join -- Chapter 4 Random Variables, Distributions & Linear Regression -- 4.1 A Refresher on Distributions -- 4.2 Inversion Sampling & Rejection Sampling -- 4.3 Joint Distribution & Copula -- 4.4 Fit a Distribution -- 4.5 Confidence Interval |
|
4.6 Hypothesis Testing -- 4.7 Basics of Linear Regression -- 4.8 Ridge Regression -- Chapter 5 Optimization in Practice -- 5.1 Convexity -- 5.2 Gradient Descent -- 5.3 Root-Finding -- 5.4 General Purpose Minimization Tools in R/Python -- 5.5 Linear Programming -- 5.6 Miscellaneous -- Chapter 6 Machine Learning -- A gentle introduction -- 6.1 Supervised Learning -- 6.2 Gradient Boosting Machine -- 6.3 Unsupervised Learning -- 6.4 Reinforcement Learning -- 6.5 Deep Q-Networks -- 6.6 Computational Differentiation -- 6.7 Miscellaneous -- Bibliography -- Index |
| 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 |
| Notes |
Nailong Zhang is lead Data Scientist at Mass Mutual Life Insurance Company |
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Vendor-supplied metadata |
| Subject |
Data mining.
|
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R (Computer program language)
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Python (Computer program language)
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Data mining
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Python (Computer program language)
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R (Computer program language)
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| Form |
Electronic book
|
| ISBN |
9781003020646 |
|
100302064X |
|
9781000215274 |
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100021527X |
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9781000215236 |
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1000215237 |
|
9781000215199 |
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1000215199 |
|
