Julia Programming Projects

A step-by-step guide that demonstrates how to build simple-to-advanced applications through examples in Julia Lang 1.x using modern tools
Key Features
Work with powerful open-source libraries for data wrangling, analysis, and visualization

Develop full-featured, full-stack web applications

Learn to perform supervised and unsupervised machine learning and time series analysis with Julia


Book Description
Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing.

After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI.

Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting.


We'll close with package development, documenting, testing and benchmarking.


By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia.
What you will learn
Leverage Julia's strengths, its top packages, and main IDE options

Analyze and manipulate datasets using Julia and DataFrames

Write complex code while building real-life Julia applications

Develop and run a web app using Julia and the HTTP package

Build a recommender system using supervised machine learning

Perform exploratory data analysis

Apply unsupervised machine learning algorithms

Perform time series data analysis, visualization, and forecasting
Who this book is for
Data scientists, statisticians, business analysts, and developers who are interested in learning how to use Julia to crunch numbers, analyze data and build apps will find this book useful. A basic knowledge of programming is assumed.