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


Time series are a very common type of data—they can be used to represent key business metrics such as financial prices, resource usage (energy, water, raw materials, and so on), weather patterns, or macroeconomic trends—and the list could go on and on. The particularity of time series is that the data has to be collected at regular intervals, and the key aspect of time series analysis is exploring ways that allow us to understand past values so that we can predict future ones.

One powerful approach is to decompose a time series into a combination of trend, cycle, seasonality, and irregular (also called error or noise). We learned how to do this in this chapter while we analysed the EU's unemployment data. We started by learning to compute the trend component by means of moving averages. Then, we applied multiplicative series decomposition formulas to calculate seasonality and error, and we also applied basic forecasting methods to predict future values. In the process, we learned...