Getting into Data Science

Data Science is certainly a booming sector at the moment, helped by Harvard famously declaring “Data Scientist: The Sexiest Job of the 21st Century”. Many prospective students attracted to the high starting salaries, job security, applicability in multiple industries and perhaps most important of all, the intellectually stimulating and diverse work.

I am an engineer by training, first in Civil Engineering then Petroleum Engineering. Oil & Gas is an amazing industry to work in, fast paced, complex and huge amounts of money being spent on projects across the globe. You can personally influence millions of dollars’ worth of investment with analysis you are presenting to management. A lot of the skills that a petroleum engineer needs, overlap with the skill set a data scientist requires, but one of the most important skills of all is the ability to communicate complex engineering solutions to a non-technical audience. This journey of evaluating data, repackaging it in a way that is easy to understand for management and have your ideas supported with investment is a very rewarding experience. Data science skills are becoming more and more valued in not only Oil & Gas but in many other industries, I see learning these skills as a great way to futureproof my value in my industry but also a way to transition into other sectors. The Oil and Gas industry has always been data rich great potential and now is as exciting a time as ever to equip myself with Data Science skills and utilise them in an industry that is undergoing a digital transformation.

Bootcamp vs Degree (why Flatiron?)

I opted for Flatiron in particular because the curriculum in the Part Time Online Course did not differ from the Full Time Online Course, it was simply taken across 10 months instead of 5months. This appealed to me because I had explored other Part Time options but they tended to be watered down versions of full time, immersive offerings. I wanted the full curriculum — after all, I will be competing for jobs against full time graduates — but being in full time employment this was not an option. Part Time Online at Flatiron was perfect for me. Before committing, of course I had to do some due diligence and LinkedIn is the perfect platform to do so. I found Flatiron Alumni to be extremely open to me reaching out to them and asking for information on how they found the bootcamp and whether or not they would recommend — all of the feedback I received was extremely positive but came with a warning, this is going to be hard.

Phase 1

The first module of the course covers a lot of material; command terminal, gitbash set up, basic python functions, dictionaries, lists, variables, loops, pandas, numPy, data visualization, APIs and JSON, SQL and more. The little experience I had certainly helped but there was many a night spent I felt like I hit a brick wall, but when you make a breakthrough it’s extremely rewarding — a piece of code you’ve been stuck on for a couple of hours suddenly working after scrolling through all the material and also post after post on Stackoverflow definitely results in celebrations. (one thing that is embraced at Flatiron is the skill of googling (and it is a skill), in the real world you will be out there and will need to find answers yourself, you won’t have a friendly instructor in your new job with all the answers, they want to prepare you for that).

At the end of phase 1 you put all of what you’ve learned together in a project, and this was probably the first time I started to feel like I knew the material that I had learned up to that point. The scenario in the project was that Microsoft was looking to enter the movie making business and wanted to know where they should best direct their efforts and investment. Looking at data from IMDB, Rotten Tomatoes, Box Office Mojo, The Numbers and figuring out what type of budget Microsoft would need for a movie, what genre, is there a good time to release a movie? If you spend more do you get bigger profits? Does good review on Rotten Tomatoes have an impact? This was my first experience of exploratory data analysis and what I found to be the most difficult aspect was not the coding or the writing up or the presenting, I learned that one question always leads to another and it is also a skill in knowing when to stop, as a data scientist working on real projects with real deadlines you will not have all the time in the world to satisfy your every curiosity — time management in a project is invaluable.

Oil and Gas professional who is passionate about Data Science