Update: This workshop has been rescheduled from April 5th to April 19th.
Data Community DC and District Data Labs are excited to be offering a Building Data Apps with Python workshop on April 5th, 2014.
Python is one of the most popular programming languages for data analysis. Therefore, it is important to have a basic working knowledge of the language in order to access more complex topics in data science and natural language processing. The purpose of this one-day course is to introduce the development process in python using a project-based, hands-on approach.
Note: This course is focused purely on Python development in a data context for those who aren’t familiar with Python. Other courses like Python Data Analysis focus on data analytics with Python, not necessarily on Python development itself.
The main workshop will run from 11am - 6pm with an hour break for lunch around 1pm. For those that are new to programming, there will be an optional introductory session from 9am - 11am aimed at getting you comfortable enough with Python development to follow along in the main session.
The price per attendee is $220.
Introductory Session: Python for New Programmers (9am - 11am)
The morning session will teach the fundamentals of Python to those who are new to programming. Learners would be grouped with a TA to ensure their success in the second session. The goal of this session is to ensure that students can demonstrate basic concepts in a classroom environment through successful completion of hands-on exercises. This beginning session will cover the following basic topics and exercises:
Topics:
Variables
Expressions
Conditionality
Loops
Executing Programs
Object Oriented Programming
Functions
Classes
Exercises:
Write a function to determine if input is even or odd
Read data from a file
Count the words/lines in a file
At the end of this session, students should be familiar enough with programming concepts in Python to be able to follow along in the second session. They will have acquired a learning cohort in their classmates and instructors to help them learn Python more thoroughly in the future, and they will have observed Python development in action.
Main Session: Building a Python Application (11am - 6pm)
The afternoon session will focus on python application development for those who already know how to program and are familiar with Python. In particular, we’ll build a data application from beginning to end in a workshop fashion. This course would be a prerequisite for all other DDL courses offered that use python.
The following topics will be covered:
Basic project structure
virtualenv & virtualenvwrapper
Building requirements outside the stdlib
Testing with nose
Ingesting data with request.py
Munging data into SQLite Databases
Some simple computations in Python
Reporting data with JSON
Data visualization with Jinja2 and Highcharts
We will build a Python application using the data science workflow: using Python to ingest, munge, compute, report, and even visualize. This is a basic, standard workflow that is repeatable and paves the way for more advanced courses using numerical and statistical packages in Python like Pandas and NumPy. In particular, we’ll use and fetch data from Data.gov, transform it and store it in a SQLite database, then do some simple computation. Then we will use Python to push our analyses out in JSON format and provide a simple reporting technique with Jinja2 and charting using Highcharts.
Prerequisites
Although this is an introductory course, some prerequisites are required. You should have Python installed and some basic familiarity with it in addition to familiarity with the command line. You should also create a Github account if you don't already have one.
The following are suggested tasks to perform before this course:
Installing Python: https://wiki.python.org/moin/BeginnersGuide/Download
Install virtualenv and virtualenvwrapper: http://docs.python-guide.org/en/latest/dev/virtualenvs/
Getting familiar with Python:
Python Hello World: http://www.learnpython.org/en/Hello,_World!
Python Basics: http://www.codecademy.com/tracks/python
Getting familiar with the command line:
Using the terminal: http://cli.learncodethehardway.org/book/
Get a Github account: https://github.com/
Instructors
Benjamin Bengfort
Benjamin is an experienced Data Scientist and Python developer who has worked in military, industry, and academia for the past eight years. He is currently pursuing his PhD in Computer Science at The University of Maryland, College Park, doing research in Metacognition and Active Logic. He is also a Data Scientist at Cobrain Company in Bethesda, MD where he builds data products including recommender systems and classifier models. He holds a Masters degree from North Dakota State University where he taught undergraduate Computer Science courses. He is also adjunct faculty at Georgetown University where he teaches Data Science and Analytics.
Sarah Kelley
Sarah is a Junior Engineer at Cobrain company in Bethesda, Maryland where she works on the data ingestion pipeline. She is a former math teacher with an MA in Education from Seattle University and an aspiring data scientist who seeks to inspire people of diverse, traditionally non-technical backgrounds to learn how to program.
Other Info
District Data Labs is comprised of several Data Community DC members focused on providing data science educational offerings to help others in our community enhance and expand their existing technical and analytical skills.
For those that are driving, the best parking option we have found in the area is the garage behind the SunTrust building on the Southeast corner of Glebe Rd. and Fairfax Dr.
Without a doubt, the best $220 I have spent in a long time. The class was very well thought out. It provided both an opportunity for anyone across the spectrum to glean something meaningful from the class, which in my humble opinion, is a borderline miracle.
The intro by Sarah was comprehensive enough to help those familiar with Python, but simply in need of a brush up, and for those who are relatively new to it.
Ben's portion was particularly helpful in that it allowed, at least for me, to make connections between all of the data science information I have been ingesting the past 2-3 years. Each aspect by themselves can be deep water. This class allowed me to view data science from a holistic perspective, to make connections. It was an Aha moment for me.
Thank you Data Community DC. I look forward to learning and growing more in this field. Hopefully, I can be a productive member of the Incubator program, and ultimately a successful data scientist. Job well done!
3 · April 20
This was a solid class, very broad in scope and I think pulled off rather well, especially considering this was the first time it was tried. The soup-to-nuts coverage of building a realistic data app in one day provided a welcome challenge for the students (and instructors) in what could only be called an immersive experience. I appreciated that Ben did not skim the material, which would have been the easy way to go. He covered the essentials robustly while emphasizing some very practical aspects of doing data science in the real world.
4 · April 20
Really great class- great questions, and everyone kept up despite my high rate of speed- really enjoyed you all!
5 · April 20
Ambitious class, was both well organized and delivered. Great process-oriented introduction to the data science pipeline.
3 · April 20
Feel free to add me to google hangout if you have any questions.
[masked]
Tweet about the class with the hash tag:
#DataApps
3 · April 19
Slides with more information can be found here:
https://docs.google.com/presenta...
April 19
If you want to take a look at the repository this morning over coffee, you can find it here: https://github.com/DistrictDataL...
April 19
There is construction around the main entrance of the building, so we will need to use the side entrance. If you're facing the building, go around to your right and you'll see a sign on the door by the loading docks.
1 · April 19
Are there any spaces left or is the event full? I have a friend who would be interested in joining if there was space.
April 16
We will probably offer this again later this year, as this is going to be a prerequisite for some of the more advanced courses we will offer.
April 19
I usually park in the parking garage behind the SunTrust building on the Southeast corner of Glebe Rd. and Fairfax Dr.
April 19
Hey Guys- I don't really want to get into a Python 2.7 vs 3.2 discussion - we can have that conversation over beers after the class (I just came back from PyCon in Montreal, so I've been discussing it with Python core developers). However, let's suffice it to say that the code I'm writing will definitely work on Python 2.7.3+ and I am going to use Numpy - and some other third party libraries that make no claims to be Python 3 compatible (though they mostly are). I've managed to avoid scipy and pandas for exactly this reason. So the code *may work on Python 3.2, and if you code in 3.2, that's fine with me- but my stuff will be in 2.7.5
April 18
Hi, can you clarify what needs to be installed before class? I downloaded Python version 3 from https://wiki.python.org/moin/Beg.... Do I need to download the virtual environment?
April 18
If you have a Mac- you have Python; you just need to get virtualenv and virtualenv wrapper ... which should be as simple as pip install virtualenv and pip install virtualenvwrapper
April 18
I'm here at 4601 N Fairfax Dr at 9 am. Building is locked tight and no one is around.
April 12
the main session is not for newbies. someone who needs the introductory session is highly unlikely to get the main session.
March 11
Workshops are only a single day- but the folks you meet will help inform and guide you; programming is a team sport. Although it may be a bit overwhelming, our workshop is a form of immersion. Therefore I think newbies will gain a lot from both sessions.
March 12
Refunds offered if:
Payments you make go to the organizer, not to Meetup. You must make refund requests to the organizer.
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I just added notes for setting up your development environment on Windows to the intro-to-python repository. You can read the notes at https://github.com/DistrictDataL...