I remember the first day I heard about data science. I was a sophomore in college and had already switched my major twice, but still had no idea what I wanted to do after graduation. My university had recently partnered with a coding bootcamp to let students learn web development. During an information session, the instructors mentioned data science, and I was immediately curious about the field. I approached an admissions counselor to learn more and heard my first real explanation of data science. I was hooked when the counselor described data scientists as naturally curious people who love puzzles and technology.
My Failed First Attempt at Learning Data Science Online
Data science is an intimidating field because there’s so much to learn and so many specialties to pursue. The variety of options has always been the main attraction for me, but it has also seemed like the learning equivalent of climbing Mount Everest, especially for someone like me, who had no programming experience, had taken only one statistics course, and was enrolled at a liberal arts college. My only choice of educational pursuit was the internet, but online learning is hard and takes a significant amount of dedication.
I was so overwhelmed with the amount of information that I didn’t know what to focus on, had difficulty retaining concepts, and was left with too many unanswered questions. I decided to ditch the online learning for a bit and enrolled in an in-person Ruby programming class, which was the best decision for me because I got my questions answered and built my programming foundation. Halfway through the class, I decided to give online learning a second shot, because I still knew I wanted to pursue data science.
Why I Chose Springboard
I already knew learning online was a challenge for me, so I sought a structured program with a detailed lesson plan. I stumbled upon Springboard’s Introduction to Data Science course, which had everything I was looking for in a program: self-paced, data science heavy, and mentor-led. As soon as I enrolled and started the curriculum, I felt relieved and confident that I was learning the right topics. I tried to take my time working through the course to retain as much information as possible, which was a good decision because I took in-depth notes that I still consult today.
The capstone project, a hands-on experience that’s the best way to learn data science, gave me the opportunity to develop a professional-style project from beginning to end. My weekly mentor calls were as impactful as the curriculum, and since I had known about data science for almost a year but had never talked to a real data scientist, I had many questions about the industry and career paths. Every week I looked forward to my meetings with my mentor because they gave me the opportunity to get feedback on my work and gain insight into what a career in data science entails.
Get To Know Other Data Science Students
Lou Zhang
Data Scientist at MachineMetrics
Isabel Van Zijl
Lead Data Analyst at Kinship
Jonas Cuadrado
Senior Data Scientist at Feedzai
After I Graduated From Springboard
In early June 2017, I started my internship as a data integrity intern. Using the knowledge I learned at Springboard, I automated my entire process and finished a summer-long project in under two weeks. I spent the rest of the summer continuing with automation and proposing data-quality solutions, which made a positive impact on the company.
At the end of the summer, I returned to school for my senior year and continued to learn about data science by picking up more personal projects. In December, I decided to return to Springboard for the more advanced Data Science Career Track because I wanted to advance my skills and take advantage of the mentorship program and the career resources offered in the Career Track.
Advice to Prospective Students
Now that I have completed my data science education at Springboard, I can reflect on the project and offer several pieces of advice to prospective students.
- Consider what you want to get out of the program. For the Intro course, I wanted to learn as many topics as possible, but for the Career Track, I wanted to focus on machine learning and statistics, which took up most of my mentor calls. If you would like to specialize or dive deeper into data science, the career track may be a better option than the Intro course, which provides a general overview of the industry.
- Take notes! Take notes! Take notes! When learning a new language, you won’t realize how many times you forget the same code or syntax. It’s best to take detailed, organized notes to help you remember and refer back to important information.
- Pick a capstone project with real-world data. The capstone project is the biggest learning opportunity and can be a great way to interest employers. By selecting a project with real-world data, you’ll set yourself up for success in two ways. First, you’ll have a smoother experience when cleaning and analyzing the data as part of the project, and second, you’ll gain experience and strengthen the skills as a data scientist you’ll need when tackling similar problems in the real world.
Learning online can be a challenging experience, but Springboard makes it easy to feel connected to others through a dedicated peer community and a passionate mentor while curating the best information available to help you develop strong skills and a solid foundation of knowledge.
Since you’re here…
Thinking about a career in data science? Enroll in our Data Science Bootcamp, and we’ll get you hired in 6 months. If you’re just getting started, take a peek at our foundational Data Science Course, and don’t forget to peep our student reviews. The data’s on our side.
This post was written by David Gibson. David is an alum of Springboard’s Data Science Career Track.
