Introduction_to_python_week_1_introduction.ipynb

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   "source": [
    "# An introduction to solving biological problems with Python"
   ]
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   "source": [
    "## Presenters for September 2018\n",
    "- Maria Doyle, Research Computing Facility\n",
    "- Richard Lupat, Bioinformatics Core\n",
    "- Miriam Yeung, Dawson Labs"
   ]
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   "source": [
    "## Learning objectives\n",
    "- **Recall** how to print, create variables and save Python code in files\n",
    "- **List** the most common data types in Python\n",
    "- **Explain** how to write conditions and loops in Python\n",
    "- **Use and compare** these concepts in different code examples \n",
    "- **Propose and create** solutions using these concepts in different exercises"
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    "## Course schedule - Week 1\n",
    "\n",
    "- 09:00-11:00: [2h00] **Session 1** - Setup, Introduction, Simple data types\n",
    "- 11:00-11:30: *Break*\n",
    "- 11:30-13:00: [1h30] **Session 2** - Lists and Dictionaries"
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    "## Course materials\n",
    "\n",
    "- There is a course webpage with links to the materials, example solutions to the exercises etc.:\n",
    "    - https://pmacdasci.github.io/python-intro/\n",
    "- All course material is available on GitHub https://github.com/pmacdasci/python-intro\n",
    "- We’d like you to follow along with the example code as we go through the material, and attempt the exercises to practice what you’ve learned\n",
    "- Questions are welcome at any point!\n",
    "- If you have specific projects/problems you like to use Python for we are happy to (try to) help during the exercises\n"
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    "## What is *Python*?\n",
    "\n",
    "- Python is a *dynamic, interpreted* general purpose programming language initially created by Guido van Rossum in 1991\n",
    "- It is a powerful language that supports several popular programming paradigms:\n",
    "    - procedural\n",
    "    - object-oriented\n",
    "    - functional\n",
    "- Python is widely used in bioinformatics and scientific computing, as well as many other fields and in industry\n",
    "- Python is available on all popular operating systems\n",
    "    - Macs\n",
    "    - Windows\n",
    "    - Linux"
   ]
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   "source": [
    "## Why learn Python?\n",
    "\n",
    "- R and Python are the two most popular programming languages used by data analysts and data scientists. Both are free and and open source, and were developed in the early 1990s—[R for statistical analysis and Python as a general-purpose programming language](https://qz.com/1063071/the-great-r-versus-python-for-data-science-debate/)\n",
    "- Since 2017 [Python is overtaking R for Data Science and Machine Learning](https://www.datasciencecentral.com/profiles/blogs/python-overtakes-r-for-data-science-and-machine-learning)\n",
    "- Python is the [No.1 programming language of 2018](https://spectrum.ieee.org/static/interactive-the-top-programming-languages-2018)\n",
    "\n",
    "<img src=\"img/Stack_Overflow_Python_vs_R.png\">"
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    "## The Python programming language\n",
    "\n",
    "- Python is considered to come with \"batteries included\" and the <a href=\"https://docs.python.org/3.5/library/\">standard library</a> (some of which we will see in this course) provides built-in support for lots of common tasks:\n",
    "    - numerical & mathematical functions \n",
    "    - interacting with files and the operating system\n",
    "    - ...\n",
    "\n",
    "- There is also a wide range of external libraries for areas not covered in the standard library, such as [Pandas](http://pandas.pydata.org/) the Python Data Analysis Library and the [BioPython](http://biopython.org/) Library which provides tools for bioinformatics."
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    "## Python 2 vs Python 3\n",
    "\n",
    "- Some currently used bioinformatics programs and scripts use Python 2.7.\n",
    "- But Python 2.7 will not be maintained past <a href=\"https://python3statement.org/\">January 1, 2020</a>, so it has <a href=\"https://pythonclock.org/\">less than 1.5 years of life left</a>.\n",
    "- Python 3 is the future and we will be using that in this course."
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    "## Getting started\n",
    "\n",
    "- Python is an *interpreted* language, this means that your computer does not run Python code natively, but instead we run our code using the Python interpreter\n",
    "- There are three ways in which you can run Python code:\n",
    "    - Directly typing **commands into the interpreter**: *Good for experimenting with the language, and for some interactive work*\n",
    "    - Using a **Jupyter notebook**: *Great for experimenting with the language, and for sharing and learning*\n",
    "    - Typing code **into a file** and then telling the interpreter to run the code from this file: *Good for larger programs, and when you want to run the same code repeatedly*\n"
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    "## What is a Jupyter notebook?\n",
    "\n",
    "<img src=\"http://jupyter.org/assets/nav_logo.svg\">\n",
    "\n",
    "- The [Jupyter Notebook](http://jupyter.org/) is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. \n",
    "\n",
    "- Jupyter provides a rich architecture for interactive data science and scientific computing with: \n",
    "    - Over 40 programming languages such as Python, R, Julia and Scala.\n",
    "    - A browser-based notebook with support for code, rich text, math expressions, plots and other rich media.\n",
    "    - Support for interactive data visualization.\n",
    "    - Easy to use tools for parallel computing."
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    "## How to install Jupyter on your own computer?\n",
    "\n",
    "<img src=\"http://jupyter.org/assets/nav_logo.svg\">\n",
    "\n",
    "- [See Installing Jupyter Notebook](https://jupyter.readthedocs.io/en/latest/install.html)\n",
    "\n",
    "- For new users, we recommend [installing Anaconda](https://www.continuum.io/downloads). Anaconda conveniently installs Python, the Jupyter Notebook, and other commonly used packages for scientific computing and data science.\n",
    "\n",
    "- Start the notebook server from the command line:\n",
    "```\n",
    "jupyter notebook\n",
    "```\n",
    "- You should see the notebook home page open in your web browser.\n"
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    "## How to run python in a Jupyter notebook?\n",
    "\n",
    "<img src=\"http://jupyter.org/assets/nav_logo.svg\">\n",
    "\n",
    "- See [Jupyter Notebook Basics](http://nbviewer.jupyter.org/github/jupyter/notebook/blob/master/docs/source/examples/Notebook/Notebook%20Basics.ipynb)\n",
    "\n",
    "## Jupyter shortcuts\n",
    "\n",
    "- Juptyer has two modes, edit mode (green cell border) and command mode (blue cell border)\n",
    "- The two most important shortcuts are `Enter` for edit mode (or click into a cell) and `Esc` for command mode\n",
    "- In edit mode, most of the keyboard is dedicated to typing into the cell's editor. Thus, in edit mode there are relatively few shortcuts. In command mode, the entire keyboard is available for shortcuts, so there are many more. \n",
    "- The Help->Keyboard Shortcuts dialog lists the available shortcuts (or type h in Command mode).\n",
    "- Recommended shortcuts to learn in command mode are:\n",
    "    - Basic navigation: enter, shift-enter, up/k, down/j\n",
    "    - Saving the notebook: s\n",
    "    - Change Cell types: y, m, 1-6, t\n",
    "    - Cell creation: a, b\n",
    "    - Cell editing: x, c, v, d, z"
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    "\n",
    "Go to our notebook for the first session: [Introduction_to_python_week_1_session_1](Introduction_to_python_week_1_session_1.ipynb)"
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