R Programming Data Analyst Learning Path, is a tour through the most important parts of R, the statistical programming language, from the very basics to complex modeling. It covers reading data, programming basics, visualization, data munging, regression, classification, clustering, modern machine learning, network analysis, web graphics, and techniques for dealing with large data, both in memory and in databases.
Unleash the powerful capabilities of R to work effectively with data.
Over 100 hands-on tasks to help you effectively solve real-world data problems using the most popular R packages and techniques
This video provides a brief introduction to using Julia for data science. Zack explains the benefits of Julia including most importantly the simplicity of coding without the sacrificing of performance. Learn how Julia compares to other languages including Python, Fortran, and C. Learn about the packages available in Julia and the pre-requisites to quickly picking up the language, including the importance of at least some knowledge of object-oriented (OO) paradigms.