# Getting started

- Review of syllabus
    - People and office hours
    - Learning objectives
    - Course content
    - Grading
    - Homework policy
    - Expectations
- Using Jupyter on Docker
- Using GitHub
- Local installation
    - Optional: [Anaconda Python 3.5](https://www.continuum.io/downloads)
    - Optional: [Rodeo IDE for Python](http://rodeo.yhat.com)
- Using Jupyter
    - Navigation
    - Help
    - Markdown cells
    - Code cells
    - Keyboard shortcuts
    - Literate programming
- Programming in Python
    - Calculator
    - Basic types
    - Data structures
    - Flow control
    - Built-in functions
- Worksheet 1
    - Practice coding
- Homework
    - Sample
    - Homework 01 (Due 4 PM Wed 18, 2017)
- Reference books (optional)
    - Python Data Science Handbook: Essential Tools for Working with Data By Jake VanderPlas
    - Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2nd edition) By Wes McKinney (not published yet)
    - High Performance Python: Practical Performant Programming for Humans By Micha Gorelick, Ian Ozsvald
    - Discovering Modern C++: An Intensive Course for Scientists, Engineers, and Programmers by Peter Gottschling
    - [Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference by Cameron Davidson-Pilon](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)

Add outline for first lecture
Add outline for first lecture
