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Transition to Tech Sean5

The structural change to the oil and gas industry and the transition to a digital economy is having a huge impact on people’s livelihoods and their lives. Career paths have been turned upside down and people must learn new skills to find jobs. Sean MacDonald is one of those people.  In this series of posts during the 12-week course, Sean chronicles the experience of a mid-career professional balancing family obligations and an intense tech training course offered though OCIF recipient Lighthouse Labs.

I have experienced a few significant milestones over the past couple of weeks.

First, I completed my course work for the Project Management Extension Certificate program through Mount Royal University, including a capstone project. This had been months in the making and I’m very proud for completing this program.

Subsequently, my application to the Project Management Institute for the official Project Management Professional certification has also been accepted. One requires a combination of project management experience and education to be approved prior to being able to take the exam. The final remaining task is to take and pass the certification exam, which will require some additional study time and review.

Secondly, I am officially halfway through the data science bootcamp program!

This was marked with the presentation of our midterm projects. This project had us work in teams of two.  Our mission: Analyze a dataset of commercial airline flights across the United States. The dataset spanned several years and had over 15 million rows. We were given just one week to complete the project.

The process required us to “clean the data”- make sure null values were removed or replaced, and then complete some exploratory data analysis. We needed to be able to accurately determine which carriers handled the most passengers, which airports handled the most flights, and predict whether delayed flights could make up time in the air. I added in daily weather data from each airport location to depict how precipitation, snow fall, or even cloud cover could potentially delay a flight.

The final part of the project was to create a model that would make predictions based on what was given and data that we added ourselves to improve the model, like weather measurements and average delays per airport and per carrier. My partner and I crafted separate models to split up the work and help each other and I was able to craft a model that would predict with 75 per cent accuracy if a flight would be cancelled or not. For my first time undertaking this complicated model, I am very proud with what I achieved in a relatively short period of time. 

Speaking of which, our instructors let us know that if this were a real-life scenario and we were presented with this task in the workplace, it would take about 3 months to complete effectively. Upon hearing this, I breathed for what felt like the first time that week. It certainly was a tall order for an emerging data scientist to be able to complete the project in the time allotted. What it demonstrated was that prioritizing tasks, splitting responsibilities, trusting your teammates, and acknowledging limitations (both computational and personal) allows one to showcase personal strengths. 

The midterm project has allowed the opportunity to take account of what I really understood and where I needed to focus on improvement. We have a lot of opportunity to apply what we learn through our homework assignments, but the midterm project was an important milestone for solidifying my confidence in my abilities and to see tangible progress.

Each week, I reflect on my knowledge retention since we are reviewing so many new concepts so quickly. At times, it can feel like topics are slipping through the cracks or that I’m not making the progress I should be. It can be difficult to gauge if I’m truly learning what I need to and am improving accordingly.

It reminds me of an illustrated book I made for my daughters last Christmas. It actually took all year to make, bit by bit and page by page, here and there, when I could. When I felt that I was nearly done, I looked back at some of the earlier pages and saw glaring mistakes and obvious errors in drawings that I previously deemed completed. My abilities had improved because I had been working on it so often and so consistently.

I actually ended up re-drawing the earlier pages and unsurprisingly, doing so took a lot less time and made the book better. There was never a “breakthrough moment” - I didn’t even realize I had improved until I looked back at where I started. 

Now, when I look back at the introductory modules and the first few weeks of the program, tasks that took me hours to figure out are now second nature. Even though I’m learning new things each week, I’m making actual progress on those topics in the following weeks as they are applied as part of a larger picture. Practice is certainly making perfect - or at least 75 per cent perfect for now. 

The progress may not be immediate, but it is definitely real.

Follow Sean on his 12- week journey from being laid off to diversifying his skill set for the technology sector. You can also connect with him on LinkedIn.  

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