The Journey Into Data Science
As of early April, of this year, I decided to change my career trajectory, leap into Data Science, and I haven’t looked back since.
Post High School I attempted college but ironically that didn’t work due to my English professor requesting a thirty page paper which, at the time, I felt I didn’t need to complete. Two weeks in I decided that it wasn’t for me and started working in a warehouse of a security integrations company. After ten months of warehouse duties and managing the vehicle fleet I decided to become an electrical apprentice. I learned the in’s and out’s of the industry, attended night classes, and eventually passed my C-6 electrical test after two years in the apprenticeship. From this point forward I always strived for more, eager to learn, eager to be successful, eager to see the world…
A little while after receiving my electrical license I decided it was time to leave my home town and see what this life has to offer. In the past ten years I have lived in Vermont, Florida, New York (the north country), and now I reside in Alaska. Throughout my career I have always been driven and continually grew within the companies I worked for.
At the start of my career I was pulling cable, running conduit, and completing service calls. By the time I left New York, I successfully managed $15 million dollars in total projects and had the opportunity to develop strong relationships with my customers. That is by far the best part of the hard work and dedication I put in while working in New York. To me, there is nothing more gratifying than building those relationships, upholding your commitments, and giving your customer a quality product that annihilates the competition.
Once I moved to Alaska I started looking into other career opportunities that required a high technical aptitude that fit my strengths. I was looking for a career which required strong communication skills along with programming knowledge. After discussions with a high school friend and a distant cousin, who are both in the industry, I decided Data Science was the right fit. They both highly recommended Flatiron School and GeneralAssemb.ly but with the warning “Data Science bootcamps are tough but they will set me up for success”.
After researching various schools I decided to move forward with Flatiron. Flatiron definitely wasn’t the cheapest course out there however I felt that the duration and their professional services out weighed other programs and would allow me to be the most successful.
Two weeks into the course and I completely understand their words of wisdom. This curriculum is fast moving and demanding. Looking back on it now, I cannot believe the amount of information I have learned in two weeks! It’s so easy to get caught up in the next chapter, the next problem, or the next phase but now, in the moment, the hard work is incredibly rewarding, motivational, and I’m filled with excitement!
As of the end of this week (weeks15[1]), we’re entering the phase one project. We have been tasked with analyzing movie datasets and provide business insights to a newly appointed department head.
Our team has jumped into the project & began our EDA on the various data sets. I have been tasked with cleaning & analyzing the IMDB data set. As of project kick off, I have successfully merged seven datasets, filtered the data based on top ten directors, top ten actors, top ten actresses, & identified a slightly positive correlation between the total run time of the movies and the overall ratings. Fun Fact #1: Movies tend to have lower ratings once they have crossed the 100 minute mark.
A few questions our team has brain stormed so far are:
1. What directors, writers, actors, & actresses have the highest average ratings by region?
2. Are there any correlations in this data set? I’ve identified <.6 correlation value between runtime and overall rating. Are any other useful correlations hiding in this data set?
3. Does a specific region have a higher average rating on their films?
So far this project has helped me implement the tools I’ve learned over the first two weeks and allows me to fine tune my base programming skills. I look forward to our final presentation, contributing to my teams results, and learning about the movie industry. Fun Fact #2: Documentaries have the highest average rating on IMDB.
Only being at weeks15[1], I’m trying not to look to the end of the road as of yet. I want to be fully present, immersed in the program, and fully prepared once I begin filling out job applications.
As a broad statement, I’m hoping that Data Science allows me to contribute something positive to society. For example, I was at my neighbors house the other night for dinner. He works with multiple global organizations within the environmental engineering field and has traveled around the world meeting with like minded individuals to solve negatively impacting global issues. [global_environmental_issue for global_environmental_issue in climate_change]
At the dinner table, he began explaining how aluminum for batteries is produced. He stated that aluminum is mined in Australia, shipped to Iceland, shipped to France, shipped back to Iceland, then shipped to China for manufacturing. This seems incredibly inefficient. How is this business savvy? How does this make sense? [global_environmental_aluminum for global_environmental_aluminum in climate_change[‘battery_manufacturing’]]
We discussed deeper and it turns out that shipping is incredibly cheap. But why? This is transportation on a global scale! It turns out that these ships run on a bi-product from the fuel industry. They run on this sludge (highly technical term) produced by oil refineries from the manufacturing of gasoline. If these ships didn’t utilize this sludge then the fuel industry would be forced to dispose of it which would be incredibly costly to them. Instead, this sludge allows for low fuel costs, making shipping cheap, and allows for the industry to operate at such a large scale.
I have not began to fact check this or dig deeper into the understand of the global shipping market. Instead I’d like to use this as an example of how Data Science is integrating its way into my life. Post boot camp, when I have spare time, I’d like to research this and see what kind of data I am able to gather to possibly give my neighbor some insight into his professional battles and, hopefully, give him some high caliber ammo to use in his future meetings with his counterparts.
To summarize, I’m excited for what Data Science has to offer me and look forward to the future challenges it presents. Hopefully, I’m able to accomplish some good in this world utilizing the skills that Flatiron prepares their students with.
Thank you for reading!
def teamwork(team_work):
follow_requests = []
for medium_user in team_work:
if medium_user[‘active’] == True:
follow_requests.append(medium_user)
return follow_requests
