If you're interested in distributed computing, you may find my project minimalcluster-py worth checking out. It's a "minimal" framework written with plain Python and its standard libraries. Even if it's "minimal", it can help build up a small-scale cluster quickly with commodity machines & LAN cable (or even Wifi), in order to tackle "embarrasing parallel" problems.

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Python is also my choice for Apache Spark (PySpark). You may be interested in my tutorials for PySpark below.

R Language

I'm the author & maintainer of CRAN R package ECharts2Shiny, which helps embed interactive charts into R Shiny web applications.

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You can install it by running


or check it on Github.

Resource List


  • Image Classifier Using MXNet for Deep Learning & Flask for Web
  • Using Parallel Computing to Speed-up Python & R Codes
  • Apache Spark (PySpark) Practice on Real Data
  • How to Build a Mac Application from Python Script
  • R Language

  • How to Achieve Faster R Coding
  • File and Folder Manipulation with R Language
  • How to Enter from Keyboard in Non-Interactive Mode in R
  • R Course Given in Singapore Polytechnic: Data Visualization with R
  • More

  • Practice with 'Real' SQL Problems
  • How to Do-It-Yourself A Cluster for Spark & Hadoop
  • Issue & Solution When Connecting to MySQL Remotely