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You're reading from Julia 1.0 Programming Cookbook Over 100 numerical and distributed computing recipes for your daily data science work?ow

Product type Paperback

Published in Nov 2018

Publisher Packt

ISBN-13 9781788998369

Length 460 pages

Edition 1st Edition

Languages

Julia

Tools

Boost

Concepts

Data Science

Authors (2):

Przemysław Szufel

Bogumił Kamiński

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Table of Contents (12) Chapters

Preface

1. Installing and Setting Up Julia

2. Data Structures and Algorithms FREE CHAPTER

3. Data Engineering in Julia

4. Numerical Computing with Julia

5. Variables, Types, and Functions

6. Metaprogramming and Advanced Typing

7. Handling Analytical Data

8. Julia Workflow

9. Data Science

10. Distributed Computing

11. Other Books You May Enjoy

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Data Science

In this chapter, we present the following recipes:

Working with databases
Optimization using JuMP
Estimation using maximum likelihood
Complex plotting with the Plots.jl package
Building machine learning models with the ScikitLearn.jl package

The rest of the chapter is locked

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Authors (2)

Kamiński

Bogumi Kamiski (GitHub username: bkamins) is an associate professor and head of the Decision Support and Analysis Unit at the SGH Warsaw School of Economics, as well as adjunct professor at the data science laboratory, Ryerson University, Toronto. He is coeditor of the Central European Journal of Economic Modeling and Econometrics, and of the Multiple Criteria Decision Making journal. His scientific interests center on operational research and computational social science. He has authored over 50 research articles on simulation, optimization, and prediction methods. He also has 15+ years' experience in the deployment of large-scale advanced analytics solutions for industry and public administration.

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Szufel

Przemysaw Szufel (GitHub username: pszufe) is an assistant professor in the Decision Support and Analysis Unit at the SGH Warsaw School of Economics. His current research focuses on distributed systems and methods for the execution of large-scale simulations for numerical experiments and optimization. He is working on asynchronous algorithms for the parallel execution of large-scale computations in the cloud and distributed computational environments. He has authored, and co-authored, several open source tools for high-performance and numerical simulation.

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