At the start of 2017 I decided to take on learning web development. I finished the course work in 9 months but after that 9 months I was still on the fence about what I wanted to do with this new skill. I had to sit down and figure this out. Time was ticking and I wasn’t getting any younger. I attended a hackathon, meetups and workshops just to hear about other ideas and options in this technology industry.
First thing I had to do was figure out what I was good at. I’ve always been hard on myself when it came to things I’m good at because I pride myself in knowing how to do a lot of things but what I did WELL was another thing. While attending Kim Crayton’s workshop “Taming The Wild West” in August 2017, she asked us to write down this very thing but then she asked us to tell her out loud one thing we wrote down. Cue every single ounce of my anxiety!! After all, my list was kind of short. I told her I was good at analyzing things and she wanted specifics so I had to drill down until I got to data analysis. It’s primarily what my current job consists of although that’s not my job title. This was my first “ah ha” into a data driven path in technology. I admit I didn’t do much with this information until months later where data analysis and data science came up again in a few of my networks.
I wanted to explore this path so I attended an online data science conference put on by Metis. There I listened to some powerful speakers. A few of the speakers had the exact same educational background that I had, economics degree and an MBA. A lot of them moved on to attain Ph.Ds but that was never a goal of mine. The conference also pointed me to some resources to learn more about data science and the programming languages involved. This is where Datacamp surfaced. Although I preferred in person classes, that wasn’t in my budget and since Datacamp offered holiday special discounts I decided to take advantage. I signed up for the Python Programmer track which is 10 courses that get you started with python language to use for data science.
The format of the courses is appealing. There’s video lessons where you the instructor is visibly seen during the slide presentation…but not in a YouTube fashion….but more like they are a part of the slide presentation….. and within the video lessons are practice problem using ipython shell. You have the chance to run the code using the skills you just learned in the video lesson and you can submit your code to immediately see if there are any errors. With the completion of the video lessons and the practice exercises you can earn XP points. I’ll be honest, I didn’t know exactly what that was until recently but it’s nice to earn things as you go along. (XP points = Experience Points) After each of the course you receive a certificate of accomplishment. And because I’m celebrating my victories in 2018, I’ve saved and shared my certificates on social media.
Datacamp also has practice problems you can work on daily for each course. These are outside of the courses. It keeps track of how many days in a row you complete the practice problems and again you earn XP points. These are good to keep the info fresh in your mind. So far I’ve completed 4 courses and I do the practice problems for all of those courses. I now feel like I have understanding of the information from at least course 1. I like that the problems vary every day. There have only been a few repeats but they may be the problems that I need more help with which is fine.
The projects of Datacamp have recently been released. I haven’t started any of those but I look forward to taking on a project. These projects can be saved to GITHUB so it becomes a part of your online profile which it what hiring executives want to see.
I signed up for a year subscription to Datacamp. My plan is to take as many of their courses as possible in that time frame and stay involved in the community. If I can start off in data analytics and move into data science as a career field I will be very happy with that.
If you have any tips or tools or projects for a beginner in programming for data science please share below.