Almost 50 people have signed up for this year's installment of the SGPE Python boot camp sponsored by SIRE. This represents almost a 500% increase in attendance from last year's event which is pretty exciting! In addition to SGPE MSc students, we are expecting PhD students from throughout Scotland and England as well as a few faculty members from various departments around the UK.

If you have note already done so please take a few minutes to fill out the course survey.

The course will be held in the following locations:

• Monday: Appleton Tower room M2b/M2c
• Tuesday: Appleton Tower room M2b/M2c
• Wednesday: Lecture Theatre 175, Old College
• Thursday: Lecture Theatre 175, Old College
• Friday: Appleton Tower room M2b/M2c (morning); Lecture Theatre 175, Old College (Afternoon).

Note that there are relatively few number of power sockets in LT 175 (we are working on a scheme to get more!). Please try to make sure that you fully charge your laptop Tuesday, and Wednesday night.

I have posted detailed instruction for downloading all of the required software (all of which is free!) on the Python boot camp Wiki. All participants are expected to have downloaded and installed the software prior to the start of class on Monday.

On Sunday afternoon there will be a help desk from 3-5pm in the basement computer lab of 30 Buccleuch place for those of you who would like some help installing the software.

I plan to take frequent official breaks (and you are free to take as many unofficial breaks as you wish). Unfortunately, you will be on your own for tea/coffee and lunch.

Curriculum for the 2nd annual "Python Boot Camp" held for the Scottish Graduate Programme in Economics at the University of Edinburgh, June 2-6 2014. The curriculum mainly follows Think Python by Allen Downey, and Quantitative Economics by Thomas Sargent and John Stachurski. Think Python is freely available on-line in both pdf and html. Solutions to exercises are also available. Code and additional documentation for Quantitative Economics can be forked from its github repository.

• Chapter 1: The way of the program
• Chapter 2: Variables, expressions and statements
• Chapter 3: Functions
• Chapter 4: Case Study on Interface Design

For the afternoon sessions, I will switch from discussing the basics of Python programming to more specialized topics. I will start by covering Part I: Programming in Python of Quantitative Economics.

• Chapter 5: Conditionals and recursion
• Chapter 6: Fruitful functions
• Chapter 7: Iteration

We will pick up where we left off with Part I: Programming in Python before moving on to Part II: The Scientific Libraries of Quantitative Economics.

• Chapter 8: Strings
• Chapter 9: Case Study on Word Play
• Chapter 10: Lists

We will pick up where we left off with Part II: The Scientific Libraries before moving on to Part III: Introductory applications of Quantitative Economics.

• Chapter 11: Dictionaries
• Chapter 12: Tuples
• Chapter 13: Case Study on Data Structures
• Chapter 14: Files

We will pick up where we left off with Part III: Introductory applications of Quantitative Economics.

• Chapter 15: Classes and Objects
• Chapter 16: Classes and Functions
• Chapter 17: Classes and Methods
• Chapter 18: Inheritance
• Chapter 19: Case Study on Tkinter

We will pick up where we left off with Part III: Introductory applications of Quantitative Economics and perhaps start on selected topics from Part IV: Advanced applications

Hopefully, by this point you will have fallen in love with Python programming and want to know where you can learn more...

I have found the following books interesting/useful:

• Think Complexity: Picking up where Think Python leaves off, this book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science. Available in both .html and .pdf formats.
• Think Stats: Introduction to Bayesian and Frequentist statistics for Python programmers. Available in both .html and .pdf formats.
• Programming Collective Intelligence: Introduction to statistical learning theory and machine learning techniques for Python programmers. Potential gold-mine of economics research applications. I maintain a repository of the code for the entire book.

If you really want to become a Python Jedi Master, then I suggest that you put yourself through MIT's legendary CS 6.00 (Spring, 2011): Introduction to Computer Science and Programming. The fall 2008 version of the course is still relevant (and the lecturer is more engaging). Both of these courses include video lectures and recitations as well as the usual course materials.