10_python_data_intro.ipynb

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    "# Data science in Python\n",
    "\n",
    "<center><img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/c/c3/Python-logo-notext.svg/240px-Python-logo-notext.svg.png\" /></center>"
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    "## Aims\n",
    "\n",
    "This course will cover concepts and strategies for working with data **more effectively** in Python with the aim of: \n",
    "\n",
    "- Writing **reusable** code, using **functions** and **libraries**\n",
    "- Acquiring a working knowledge of **key concepts** which are prerequisites for advanced programming in Python like writing classes to build objects\n",
    "- During this course you will learn about:\n",
    "    - Using Python libraries to explore data in files\n",
    "    - Creating functions and modules to write reusable code\n",
    "    - Manipulating data using Pandas\n",
    "    - Visualising data with Matplotlib \n",
    "    - Working with biological data using BioPython"
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    "## Learning objectives\n",
    "- **Recall** the basic syntax, and the most common data structures in Python\n",
    "- **Explain** how to write conditions and loops in Python\n",
    "- **Practice** reading and writing files with Python\n",
    "- **Explain** how to write user-defined functions and modules in Python\n",
    "- **Use** existing in-built as well as third-party Python libraries\n",
    "- **Solve** more complex exercises using these concepts"
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    "## Course schedule - day 1\n",
    "\n",
    "- 09:30-09:45: [0:15] **Introduction**\n",
    "- 09:45-11:30: [1:45] **Session 1.1** - Starting with data and python\n",
    "- 11:30-11:45: [0:15] *break*\n",
    "- 11:45-12:30: [0:45] **Session 1.2** - Using existing python modules to explore data in files (part 1)\n",
    "- 12:30-13:30: [1:00] *lunch break*\n",
    "- 13:30-14:30: [1:00] **Session 1.2** - Using existing python modules to explore data in files (part 2)\n",
    "- 14:30-14:45: [0:15] *break*\n",
    "- 14:45-16:30: [1:45] **Session 1.3** - Creating functions and modules to write reusable code"
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    "## Course schedule - day 2\n",
    "\n",
    "\n",
    "- 09:30-09:45: [0:15] **Introduction**\n",
    "- 09:45-11:00: [1:15] **Session 2.1** - Manipulating data with Pandas\n",
    "- 11:00-11:15: [0:15] *break*\n",
    "- 11:15-12:30: [1:15] **Session 2.2** - Visualising data with Matplotlib\n",
    "- 12:30-13:30: [1:00] *lunch break*\n",
    "- 13:30-14:45: [1:15] **Session 2.3** - Working with biological data using BioPython\n",
    "- 14:45-15:00: [0:15] *break*\n",
    "- 15:00-16:15: [1:15] **Session 2.4** - Creating a data project report using Jupyter\n",
    "- 16:15-16:30: [0:15] **Conclusion**"
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    "## Course materials\n",
    "\n",
    "- The course materials is accessible on GitHub:\n",
    "    - https://github.com/pycam/python-data-science\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",
    "- If you have specific projects/problems that you think could be attempted using Python, we are happy to (try to) help during the exercises. Just let us know!\n"
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    "## Audience \n",
    "\n",
    "- This course is open to **everyone** with *some basic knowledge* of Python. \n",
    "- For today’s lesson, there is only one prerequisite: to **want to learn!**\n",
    "- We will start introducing the basics and building up from there. \n",
    "- You will set the pace and the amount of material we will cover."
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    "## Feedback\n",
    "\n",
    "- Questions are welcome at any point!\n",
    "- We use a two colour post-it system: \n",
    "  - **green** when exercise is done; and \n",
    "  - **red/pink** when you need help"
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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",
    "    - functional\n",
    "    - object-oriented\n",
    "- Python is widely used in bioinformatics and scientific computing, as well as many other academic areas and in industry too\n",
    "- Python is available on all popular operating systems\n",
    "    - Mac \n",
    "    - Windows\n",
    "    - Linux"
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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, [Matplotlib](http://matplotlib.org/) the Python plotting library and the [BioPython](http://biopython.org/) Library which provides tools for bioinformatics."
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    "## How to run *Python*?\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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   "source": [
    "## Next session\n",
    "\n",
    "Go to our next notebook: [Session 1.1: Starting with data and Python](11_python_data.ipynb)"
   ]
  }
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