Tag Archives: julia

Julia for Data Science Book

Today brings us a very welcome guest post by Zacharias Voulgaris, author of Julia for Data Science. This is an excellent new book about the Julia language. By reading it you will learn about:

  • IDEs for using Julia
  • Basics of the Julia language
  • Accessing and exploring data
  • Machine learning
  • Advanced data science techniques with Julia (cross-validation, clustering, PCA, and more)

The book has a nice flow for someone starting out with Julia and the topics are well explained. Enjoy the post, and hopefully you get a chance to check out the book.

Introducing Julia for Data Science (Technics Publications), a Great Resource for Anyone Interested in Data Science.

Over the past couple of years, there have been several books on the Julia language, a relatively new and versatile tool for computationally-heavy applications. Julia has been adopted extensively by the scientific community as it provided a great alternative to MATLAB and R, while its high-level programming style made it easy for people who were not adept programmers. Also, lately it has attracted the attention of computer science professionals (including Python programmers) as well as data scientists. These people who were already very effective coders, decided to learn this language as well, since it provided undeniable benefits in terms of performance and rapid prototype development, esp. when it came to numeric applications. In addition, the fact that Julia was and is still being developed by a few top MIT graduates goes on to show that this is not a novelty doomed to fade away soon, but instead it is a serious effort that’s bound to linger for many years to come.

However, this post is not about Julia per se, since there are many other people who have made its many merits known to the world since the language was first released in 2012. Instead, we aim to talk about the lesser-known aspects of the language, namely its abundant applications in the fascinating field of data science. Although there are already some reliable resources out there pinpointing the fact that Julia is undoubtedly ready for data science, this book is the first and most complete resource on this topic. Without assuming any prior knowledge of the language, it guides you step-by-step to the mastery of the Julia essentials, helping you get comfortable enough to use it for a variety data science applications. It may not make you an expert in the language, but data scientists rarely care about the esoteric aspects of the programming tools they use, since this level of know-how is not required for getting stuff done. However, the reader is given enough information to be able to investigate those aspects on his own.

The Julia for Data Science book has been in development for about a year and is heavily focused on the applications part, with lots of code snippets, examples, and even questions and exercises, in every chapter. Also, it makes use of a couple of datasets that closely resemble the real-world ones that data scientists encounter in their everyday work. On top of that, it provides you with some theory on the data science process (there is a whole chapter of it dedicated to this, although other books usually devote a couple of pages to it). Although the book is not a complete guide to data science, it provides you with enough information to have a sense of perspective and understand how everything fits together. It is by no means a recipe book, though you can use it as reference one, once you have finished reading it.

The Julia for Data Science book is available at the publisher’s website, as well as on Amazon, in both paperback and eBook formats. We encourage you to give it a read and experience first-hand how Julia can enrich your data science toolbox!

Considering Julia? Here Are Some Resources

Julia is a new programming language that is quickly gaining traction in the statistics and data science world. It is a high-level language, yet the speed is comparable with lower-level languages like C and fortran. Below are some resources for various types of people.

Do you love the academic paper?

Julia: A Fresh Approach to Numerical Computing – An academic paper that includes a brief overview of the language as well as a description of the Julia architecture, benchmarks and much more.

You want to start with the Official Language Docs

The Julia Language is well-documented. There is even a style guide in the docs.

Forget the paper and docs, You want to try it Now!

JuliaBox – Web-hosted implementation of IJulia. IJulia is a collaboration with IPython to provide an interface for writing Julia code in a web browser. Use this option if you want to start right now and figure the details out later.

Slow down, Try some tutorials first

Forio Julia Tutorials – The tutorials expect you to install Julia Studio IDE, but JuliaBox above should be sufficient. The tutorials contain enough code to get you started. Then the tutorials advance to larger and more complex problems.

You prefer to see someone use Julia first

5 Free Programming Languages for Data Science

  1. R There is a package for nearly any algorithm you will ever need. That is where R really excels. It is widely used and has a strong community. The only slight downfall (in my opinion) is the cumbersome syntax.
  2. Python A very good language for beginning programmers. The syntax is quite readable and intuitive. With the NumPy and SciPy packages, python has many of the tools/algorithms necessary to do data science.
  3. Octave Octave was created to be very similar to the commercial product, Matlab. Octave is used and highly recommended in Dr. Andrew Ng’s Coursera machine learning course.
  4. Java While I don’t read a lot about people using Java for quickly testing new statistical models, a couple of the larger open-source data science products are built with Java, Hadoop and Storm to name a couple. Plus, Java does have libraries for just about everything, and it has proved itself to be a fairly descent production environment.
  5. Julia This is the newcomer on the list. Julia claims to have really great performance along with built-in support for parallelism and cloud computing. I am not too familiar with Julia, but it will be interesting to see how the Julia community grows over the coming months and years. Julia is currently lacking some of the libraries/algorithms that the others on the list support.