Finished Codecademy’s Python Course

I just wrapped up Codecademy‘s Python course.  I now have a Python knowledge base to build on.



Practice Really Does Make Perfect

I have been cruising through the Codecademy lessons (69% done) and have finished a whole set of practice problems.

I have noticed that when I am able to sit down a practice coding I remember the ins and outs of the Python syntax.  When I get stuck I resort to a Google search to jog my memory.

I have noticed that I have been developing some new habits.  I think I’m getting the hang of the colon instead of the curly braces.

I have noticed some bugs with the Codecademy platform.  I programmed my exercises in PyScripter (something I picked up from Khan Academy) and then would cut and paste the function into Codecademy interface.  My median function was working correctly on my machine but Codecademy said it was returning the wrong values.


Able to Continue with Udacity

I just wrapped up the 10th lesson of Codecademy‘s Python course.  I felt like I was getting the hang of it so I decided to test out my understanding by picking up the Udacity intro to data science course where I left off.

I am happy to announce I was able to complete the assignment and make some progress on that course.  I am starting to get the hang of Python.  I’m very excited about that.

I have also finished the Varian paper tonight.  I really liked his idea of using multiple prediction models to and then averaging their results to come up with a final prediction.  I also like his idea of disclosing the uncertainty of the model in economics, similar to the way hurricane landfall forecast disclose their uncertainty.  Economists need to be more honest about how much the really don’t know.


Progress with Python

I have just completed my 100th Codecademy Python exercise and earned this badge: Here are my thoughts on leaning Python so far.  I haven’t had too many problems learning the syntax.  Having learned PHP has helped a lot.  It has also left me with some ruts I have to break out of.  I do find the indenting a little strange and prefer my curly braces (meaning “{” and “}”). I am excited to keep plowing through the exercises.  Then I can move onto learning R and perhaps I can program a wrapper for the APIs.


Insight from Unexpected Places

I was working through Udacity’s intro to data science course and came to the first exercise which involves predicting if a passenger on the Titanic would be a survivor or not based on the characteristics of the passenger (age, sex, etc.).

Well, I got hung up because I don’t know enough Python syntax to do the programming (I guess I need to finish my Codecademy Python course first). So I decided to switch gears and explore how economists are using Big Data.

I was reading a working paper by Hal Varian, since he is partially responsible for getting me on this path, that was a high level overview of Big Data. It was in the 10 o’clock hour and my brain was spent so I stopped (I am a morning person). I was flipping ahead to see how many pages were left when I saw something I did not expect to see.

I saw a decision tree that showed how to predict if a person was a Titanic survivor based on some characteristics. You can get a hold of the paper on the resource page of this blog. It is funny where you can find insight. Who would of thought a paper, written for economists, could provide a model to predict Titanic survivors. There is a good lesson that Computer Scientists do not have a monopoly on this field.