Continuing Education in Software Engineering

Data Science, education, Learning, PyCon, Python, software engineering, Women in Tech

You’ve finished the classroom portion of your bootcamp.  Maybe you have a luxurious internship.  You’ve worked your butt off.  Time to sit back and relax… just kidding!

If you’re like me, the reason you got interested in software engineering is that the education on the subject is never ending.  There is always something new to learn, some path to go down, or maybe a mountain to climb.  The more I learn, the more I know of my Socratic ignorance.  In laymen’s terms, I am aware of the lack of my own knowledge.  Because of this, I strive to know everything I can about software engineering.

There are many ways to learn.  You can learn by listening, doing, seeing, and many more ways.  To learn by listening, you can listen to podcasts on your commute.  Find something that interests you so that you pay attention.  Software Engineering Daily, Code Newbie, and  Talk Python to Me are just a few that I listen to on my jog to work.  To learn by doing, you can use Coursera, EdX, Udacity, Udemy or even youtube.  Coursera and EdX are free, but you can find Udacity and Udemy courses for drastically discounted rates.  If you are more accountable when you put money into a course, they are great options.  If you’re a visual learner, join some local meetup groups.  They often have lightning talks to get you started on a subject, tutorials, and social gatherings.  In Seattle, I enjoy the PUPPY(Puget Sound Python Programming group, SeattleRb, She’s Coding Seattle, and Women in Data Science groups.  In addition to this, you can decide to go to and speak at conferences.  You can usually find a discount for local conferences through meetups or applying for a scholarship.  Being a speaker provides some perks (accommodation admission and sometimes more).  Another way to garner a free ticket is to volunteer at the conference.  The Tech Women Rising conference was extremely fulfilling.  I was able to meet astounding women and learn a lot.  Because of that I am volunteering at the Women in Tech Regatta.  I’m sure It’s going to be just as fantastic.  In May,  I’ll be attending PyCon in Cleveland.

There are always ways to learn and people that will help.  Good luck in your educational journey.

What is Selenium?

Learning, Python

Selenium is used to test software.  You can write the tests in many languages ( C#GroovyJavaPerlPHPPythonRuby and Scala), although I’m only familiar with writing them with Python.  It also works with Windows, MacOS, and Linux.  The tests are run against common web browsers like Internet Explorer, Chrome, and Mozilla Firefox.

Why am I excited about Selenium?  During my time at Ada Developers Academy, we weren’t given the option to test anything in the front end of our applications.  After a few Selenium tutorials, I am hooked.  You can use the language of your choice to write tests and perform actions in the DOM.  Somehow Python is able to do JavaScript-like things, such as click and add items into my virtual shopping cart.  I can’t wait to learn more about this fantastic tool.

Virtual Environment for Python

Learning, Python, Virtual Environment

In the coming weeks, I will be working on my capstone project for Ada Developers Academy.  I have decided to make a Python application using Django.  Python is a relatively new language to me, so I have spent my break doing a little research.  I know that the standard practice in creating a new project is setting up a virtual environment, but what is a virtual environment and why do I need to use it?

As I understand it, creating a virtual environment for Python will allow you to isolate packages and dependencies for that specific project.  If you have an updated version of Python on your system, it will not interfere with your work when you return to it because you will be using your virtual environment.  While working on my capstone, this will come in handy because I am working in a group.  We can decide which versions we want to use for Python and we will be able to independently work on the same project without fear of breaking it due to an unintentional upgrade.

Now for the difficult task, I have to figure out which way to set up my virtual environment.  For Python 3, I have been counseled that virtual environment wrapper is the tool for the job.  I’ve also seen that anaconda for Python comes with a way to set up virtual environments.  Also, Python 3 comes with pyvenv.  I have successfully created environments with Anaconda and virtual environment wrapper, but pyvenv is giving me some difficulties at the moment.

As with anything in the software engineering world, there are numerous ways to solve problems.  This is an opportunity to learn  how to research the correct tool for the task, which tools I enjoy working with, and which ones I will change next time.  I anticipate that this project will be full of challenges, but I know that I am ready to meet them and learn from the mistakes.

Intro to Data Science with Metis

Data Science, Documentation, Learning, Python

While searching through Meetup.com, I stumbled upon a free “One Day at Bootcamp” sponsored by Metis.   Since I am unfamiliar with data science and love any opportunity to learn something new, I signed up.  Within minutes, I had a welcome email from Metis letting me know of the things I should expect to learn in their class.  A few days later, I received a follow-up email reminding me that I should download Python 3 and Anaconda, if I didn’t already have it.  The correspondences that were sent from Metis were easy to follow and I found myself with the proper tools for the task ahead.

The day of the bootcamp, I wandered into the room and was greeted by a friendly person.  We started a bit late because of technical difficulties, but the teacher Roberto Reif, gave thorough explanations.  This class would have been accessible to a person of any skill level.  Throughout the course, Roberto was receptive to questions and interacted with the students.  We opened the Jupyter notebooks that were provided by Metis and began to work.  From what I understand, Jupyter notebook is a powerful prototyping tool.  It looks like a standard webpage or markdown file intermingled with mini-terminals for executing code.

First, we started with an intro to Python.  I haven’t written in Python code very much so I appreciated the intro.  We went through data types, indexing, loops, and functions.  I find it funny that Python has a data structure called a dictionary which is analogous to a Ruby hash.  Some new things I learned about were tuples and sets.  In Python, white space is extremely important.  I have been used to languages that call for an ‘end’ to a loop or function.  Python uses white space to mark which parts are or are not included in the function.  Apparently, the Python documentation isn’t very helpful due to it being open sourced, but there are some powerful modules available that I’d like to take some more time to research.

After the intro, we made our way to the next notebook on linear regression.  Linear regression is a tool to help us find trends in data.  Roberto showed us how to interact with data and make mock data with gaussian noise.  He said that what we were doing in this segment would be familiar to someone who uses MATLAB.

The Scikit learn api was the next subject that we looked at.  I will summarize Scikit by quoting the notebook.  “Basically, it’s an extraordinarily convenient way to start into machine learning and data mining.”  We use the SkLearn Api with three(ish) steps.

  1. Import and initialize the regression from SkLearn

2. Call the fit function of the module (learn from the data)

3. Predict/transform the data (predict outcome)

As an example of this model, we could see a prediction of which handwritten numbers were which numerical digits.  The results were surprisingly accurate.

Our final segment of the day was case study with Scikit learn and Pandas.  Pandas is a module for Python that helps you handle lots of data.  Our first example had us manipulate data from a CSV of weather and use Pandas to learn about our data.  In addition to data manipulation, we were able to visualize the data in a way that elucidated trends.  For the icing on the cake, we  built regression models in scikit-learn for housing in Ames, IA.  This was an excellent example because anyone could see how this model could be useful for predicting values.

Overall, I would say that my experience with Metis was fantastic and I learned a lot that day.  The staff was extremely helpful and I enjoyed the passion that everyone had for data science.  I would definitely attend another event at Metis.