Book Image

Julia Programming Projects

By : Adrian Salceanu
Book Image

Julia Programming Projects

By: Adrian Salceanu

Overview of this book

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.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt

Working with relational databases

Our web crawler is quite performant—using CSS selectors is very efficient. But, as it is right now, if we end up with the same Wikipedia article in different game sessions, we'll have to fetch it, parse it, and extract its contents multiple times. This is a time-consuming and resource-expensive operation—and, more importantly, one we can easily eliminate if we just store the article information once we fetch it the first time.

We could use Julia's serialization features, which we've already seen, but since we're building a fairly complex game, we would benefit from adding a database backend. Besides storing articles' data, we could also persist information about players, scores, preferences, and whatnot.

We have already seen how to interact with MongoDB. In this case, though, a relational database is the better choice, as we'll work with a series of related entities: articles, games (referencing articles), players (referencing games), and more.

Julia's package...