Julia: Performing Statistical Computations
- 2.6
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Brief Introduction
Mould your programming skills by carrying out dynamic numerical computations with JuliaDescription
Julia is a high-performance dynamic programming language for numerical computing. This practical guide to programming with Julia will help you to work with data more efficiently.
This course begins with the important features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll explore utilizing the Julia language to identify, retrieve, and transform datasets so you can perform efficient data analysis and data manipulation.
You will then learn the concepts of metaprogramming and statistics in Julia.
Moving on, you will learn to build data science models by using several algorithms such as dimensionality reduction, linear discriminant analysis, and so on.
You’ll learn 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 course includes sections on identifying and classifying data science problems, data modelling, data analysis, data manipulation, multidimensional arrays, and parallel computing.
By the end of this course, you will acquire the skills to work more effectively with your data.
What am I going to get from this course?
Extract and manage your data efficiently with Julia
Explore the metaprogramming concepts in Julia
Perform 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
What’s special about this course?
We've spent the last decade working to help developers stay relevant. The structure of this course is a result of deep and intensive research into what real-world developers need to know in order to be job-ready. We don't spend too long on theory, and focus on practical results so that you can see for yourself how things work in action.
We have combined the best of the following Packt products:
- Julia Cookbook by Jalem Raj Rohit
- Julia Solutions by Jalem Raj Rohit
Meet your expert instructors:
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.
Meet your managing editor:
This course has been planned and designed for you by me, Shiny Poojary. I'm here to help you be successful every step of the way, and get maximum value out of your course purchase. If you have any questions along the way, you can reach out to me and our author group via the instructor contact feature on Udemy.
Requirements
- Requirements
- The software requirements assume you have any of the following OSes: Linux, Windows, or OS X
- There are no specific hardware requirements, except that you run and work all your code on a desktop, or a laptop preferably
Packt Publishing
2.6 Raiting
Julia: Performing Statistical Computations
- 2.6
-
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Mould your programming skills by carrying out dynamic numerical computations with Julia Julia is a high-performance dynamic programming language for numerical computing....
