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- Follow a practical approach to learn Julia programming the easy way
- Get an extensive coverage of Julia s packages for statistical analysis
- This recipe-based approach will help you get familiar with the key concepts in Juli
Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation.
Later on, you ll see how to optimize data science programs with parallel computing and memory allocation. You ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform.
This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.
What you will learn
- Extract and handle your data with Julia
- Uncover the concepts of metaprogramming in Julia
- Conduct statistical analysis with StatsBase.jl and Distributions.jl
- Build your data science models
- Find out how to visualize your data with Gadfly
- Explore big data concepts in Julia
About the Author
Jalem Raj Rohit is an IIT Jodhpur graduate with a keen interest in machine learning, data science, data analysis, computational statistics, and natural language processing (NLP). Rohit currently works as a senior data scientist at Zomato, also having worked as the first data scientist at Kayako.
He is part of the Julia project, where he develops data science models and contributes to the codebase. Additionally, Raj is also a Mozilla contributor and volunteer, and he has interned at Scimergent Analytics.
Table of Contents
- Extracting and Handling Data
- Statistics with Julia
- Building Data Science Models
- Working with Visualizations
- Parallel Computing