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A Tour of Data Science: Learn R and Python in Parallel

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ISBN: 9780367895860
Author/Editor: Nailong Zhang

Publisher: CRC Press

Year: 2020

1 in stock (can be backordered)

SKU: ABD-CRC-5504 Category:

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.

Additional information

Weight 0.4 kg

Product Properties

Year of Publication

2020
Table of Contents

Assumptions about the reader's background Book overview Introduction to R/Python Programming Calculator Variable and Type Functions Control flows Some 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 strategy Speed up with C/C++ in R/Python A first impression of functional programming Miscellaneous data.table and pandas SQL Get started with data.table and pandas Indexing & selecting data Add/Remove/Update Group by Join Random Variables, Distributions & Linear Regression A refresher on distributions Inversion sampling & rejection sampling Joint distribution & copula Fit a distribution Confidence interval Hypothesis testing Basics of linear regression Ridge regression Optimization in Practice Convexity 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
Author

Nailong Zhang
ISBN/ISSN

9780367895860
Binding

Paperback
Edition

1
Publisher

CRC Press

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