04: Development Environment

In Data Science, everyone more-or-less is a developer. Whether you write simple scripts in Bash, submit jobs to a queuing system on an HPC infrastructure, or develop advanced software and web applications in any programming language, an integrated development environment (IDE) makes your daily work easier. Among useful features, there are built-in browsing of the file system, autocomplete of syntax, a preview of available attributes, error detection, customized formatting, and even source code rendering (e.g., markdown or HTML). Even if you’re only using open-source pipelines, it’s worth familiarizing yourself with common editors such as Atom or Jupyter to get the most out of them.

Table of contents

1. Integrated & Interactive Development Environment (IDE)

• 1.1 Visual Studio Code: Multi-language Integrated DE
• 1.2 Jupyter: Interactive Web-Based Multi-Kernel DE
  • 1.2.1 Getting Started with JupyterLab on a local machine
  • 1.2.2 Getting Started with Jupyter Notebook on HPC systems
  • 1.2.3 Sharing Jupyter-based reproducible pipline via MyBinder

2. Python programming environment(s)

• 2.1 Online Console: simple Python code in a browser (without installation)
• 2.2 Online Notebook: interactive Python code on Try-jupyter and Colab (without installation)
• 2.3 Python Setup on your computing machine (installation needed)
  • 2.3.1 Shell & IDLE: create Python code in a terminal or simple IDE (beginner)
  • 2.3.2 Text editors: create Python code in terminal text files (intermediate)
  • 2.3.3 Jupyter Lab: create an interactive Python notebook (advanced)
  • 2.3.4 PyCharm: IDE for Professional Python Developers (professional)

3. R programming environment(s)

• 3.1 RStudio: Integrated Environment for R Programming
  • 3.1.1 Setting Up RStudio

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