Python Teaching Series:

FCC Wireless Data Analysis with Python Pandas

Thursday, March 14 6:00-8:00 at the ATX hackerspace

$80.00 per session for non members and $40 per session for ATX members, pay through eventbrite. To get the member discount contact dean@atxhackerspace.org for the discount code.

Eventbrite

Class Description

Data analysis is becoming more important in our everyday work. Data is everywhere and it can be difficult to handle, clean and derive insight from. In this lab we will download a slice of FCC wireless licensing data and work with it to practice data cleaning, analysis and plotting with Pandas, python’s powerful data analysis package.

Data Description - FCC wireless Data Analysis

Access to specific radio bands is becoming more important. The allocation of the rights to use these bands is regulated by the FCC and and publically announced in by the Universal Licensing System. In the image below you can see the allocation of specific bands across the radio spectrum as of 2016. In this lab we will download a slice of this data.

<img style="padding: 0 15px; float: right ;" src="https://upload.wikimedia.org/wikipedia/commons/c/c7/United_States_Frequency_Allocations_Chart_2016_-_The_Radio_Spectrum.pdf" width="500"/ >

This is the fourth in a series intended to teach you the essential basics of programming using Python and how to apply these basics to **make cool things**. The course can be taken a-la-carte based on the topic or can be attended regularly to become a well rounded python coder. I will cover all aspects of python language over the series.

In the FCC Data Analysis class you will:

  • Handle a large data set from a government source

  • Load and parse the data

  • Conduct quaility control and exploration

  • Communicate your findings with visulaization

  • Come prepared to learn by doing

Set-up

The class will be taught on your laptop by leveraging jupyter notebooks and the Anaconda Package manager. You must bring a laptop to take this class. If you are able to install anaconda and and run a jupyter notebook before you come to class we will be able to save some time. Anaconda is here. Be sure to allow the installer to set your PATH when you are given the option (it is not the default).

About Python

Python is an open source language that will run on a variety of platforms. With this class you’ll be able to write programs for Raspberry Pi, the web, Windows PCs, and many other applications. The basics taught in this class will relate to many other programming languages. Let this class be a stepping stone to help you with your own projects or even a career in programming.

Your instructor

<img style="padding: 0 15px; float: left ;" src="https://avatars1.githubusercontent.com/u/10604824?s=400&u=f7a03aa1e2abbb8e32a1ab0c84d2f7953da4bf16&v=4" width="100"/ >

Gunnar Kleemann will be teaching this class. Gunnar holds a PhD in genetics and a Masters in Data science. Gunnar is a faculty member UC Berkeley’s Masters in Data Science (MIDS) and has been teaching Python for data science the last 3 years and. He currently runs Austin Capital Data from the ATX Hackerspace and is the a Principal Data Scientist with Berkeley Data Science Group.

Details

This is a two hour course held every other Thursday (600-800PM)

Past Classes

  • 1/31 Class 1 - Computer Generated Art with Python
  • 2/14 Class 2 - Fundamentals: Built-in objects and types
  • 2/28 Class 3 - Search strategies and the virtual rover

Upcoming Classes

  • 3/14 Class 4 - Data Analysis on FCC Wireless Data
  • 3/28 Class 5 - Dasboarding Your Data with Dash
  • 4/11 Class 6 - TBD
  • 4/25 Class 7 - TBD
  • 5/09 Class 8 - TBD

Class Size Min 2 Max 15

Contact

gunnarkl@gmail.com

Python for Makers Website

Getting to the hackerspace

The hackerspace is located at 3701 Dessau Rd. Bldg 3 - dirve to the back of the lot and make a right, the hackerspace main entrance is on the right.


Curriculim developement was made possible by resources from: Berkeley Data Science Group and Austin Capital Data

  • image credit: image reference: https://upload.wikimedia.org/wikipedia/commons/c/c7/United_States_Frequency_Allocations_Chart_2016_-_The_Radio_Spectrum.pdf