Julia for Data Science

İlker Arslan
  • ISBN 9781633438699
  • 275 pages (estimated)
  • printed in black & white
We regret that we will not be publishing this title.
Look inside
These hands-on projects will level-up your Julia skills for data science, machine learning, and more.

In Julia for Data Science you’ll take on challenging real-world projects that teach you core skills like:

  • Ingestion, analysis, and manipulation of data
  • Producing stunning data visualizations
  • Creating supervised and unsupervised learning algorithms
  • Developing deep learning algorithms
  • Deploying machine learning algorithms
  • Packaging code
  • Building web apps

Julia for Data Science tests and improves your Julia skills on the kind of tasks data scientists perform every day. These challenging projects will work out your Julia skills for importing, cleaning, manipulating, and visualizing data. As you read, you’ll learn to take advantage of Julia’s full potential, as you develop high-performance deep learning algorithms and tackle supervised and unsupervised learning.

about the technology

When you think “language flexibility,” think Julia. Designed to solve the “two-language problem”, Julia offers the best of both worlds: the simple and flexible syntax you need for data exploration, and the lightning-fast execution speeds demanded for production deployment. Plus, its growing ecosystem of data science libraries and ability to convert code to run on GPUs make Julia a real contender for building complex machine learning applications that don’t leave you waiting days to see results.

about the book

Julia for Data Science challenges you with real-world projects like reading song lyrics from multiple text files and converting them into a data table, preparing credit application data for model development, and more. You’ll dive into developing powerful deep learning algorithms, and learn how Julia streamlines machine learning deployment. You’ll even pick up some new general purpose programming skills that are incredibly useful as a data scientist, including creating packages, building web apps, and writing domain-specific languages.

about the reader

For data scientists who know the absolute basics of Julia and want to upgrade their skills.

about the author

İlker Arslan, Ph.D., is currently a Chief Information Officer at a finance firm. He has more than 20 years of experience in data science and analytics, has authored two books on data science and statistical computing, and has published various papers on economics.