“But don’t I need a degree to get a job as a data scientist?” As a non-traditional educational model, we hear this concern a lot. The answer is yes and no. Yes, because most jobs in data science require some form of higher education. No, because that degree doesn’t have to be in something related to data science – data scientists have degrees in many different areas of study! So if the concern that lacking a piece of paper will prevent you from accelerating your career, fear no more.
While a Master’s Degree provides a lot of value, it isn’t the most direct path for a job. With the exponential growth in data generation and the race to keep up with storing and processing that data, data science no longer sits at the fringe of an ultra-specialized workforce. Companies now need much larger teams to analyze, model, and leverage the data they’ve collected. So while the field of data science might have once only been available to those skilled in highly academic algorithm development, it’s now a playground for those with some Python skills who know how to find valuable insights in a mess of data.
Now let’s get a bit more specific. If modern roles in data science demand more practical skills, why is a bootcamp a better path?
#1: Responsive curriculum: Barely 10 years ago nobody had heard of data science. But in that short amount of time, the tools and technologies in the field have grown exponentially. Each year sees the introduction of new packages, visualization tools, and cutting edge technologies. With such a rapidly evolving landscape, it’s hard for traditional learning environments to keep pace. With our ears tuned directly to employers, we’re able to adapt quickly and ensure we’re teaching what hiring managers need.
#2 Hands-on Project-Based Learning: Have you ever watched Top Chef or The Great British Bake Off? You were probably pretty entertained, but how did that seared Ahi Tuna with orange mint avocado salsa and balsamic vinegar reduced amuse-bouche turn out? The sad reality is, watching experts do their thing doesn’t make you an expert. Nor does listening to lectures. Our program is built around the concept of praxis, which is essentially the practical application of theory, or the blending of theory and practice. Half of your 670 program hours are spent actually writing code, so you develop the muscle memory and experience of programming. A career in data science is like an old-time trade, like becoming a blacksmith: you have to learn from masters and practice, practice, practice.
#3 Progressive Curriculum Structure: In a traditional degree, students study by taking several classes at a time. You may begin with data structures and algorithms, then move to SQL, then take Python, and so on. But real-world data science doesn’t work so neatly. You will never face a project where you’re only working with one of those tools, so this pedagogical method is misaligned with career demands. Our program focuses on real-world deliverables at every step of the journey, while exposing you to increasingly complex problems and projects. You start off applying basic tools to simple challenges. Then, we begin varying the data sets, the way you access that data, the type of methodology you use, and the deliverable you’re responsible for. To put it simply, the structure of a traditional degree teaches you how to use a hammer, a saw, and a chisel. Codeup teaches you how to build a stool, a birdbox, and a sculpture with those tools, and when to use which.
#4 Job Placement Services: If education is your goal, stop reading now. If a career is your goal, then you’re in the right place. Most graduate institutions have career service offices where you can get advice on your resume and attend job fairs. But Codeup makes you a promise: get a job after graduation or get 100% of your money back. There are no two ways about that: our singular focus is your outcome. Unlike traditional institutions, we sell jobs, not education.
#5 Messy Data: This is probably the most important difference between us and traditional degrees. We use real, messy, misleading, broken data so you learn how to draw insights from the real thing. Unfortunately, that is not the norm. Because of the segmented class structure, traditional degrees have to focus on using data that teach one specific skill. At Codeup, you’re always applying your tools to a real deliverable, so we’re able to use real data sets that intersect the challenges of multiple skills.
Lastly, we encourage you to think about the return on your investment in your education.
Most importantly, the opportunity cost of pursuing a master’s degree is equal to 13-19 months of employment. At a median salary of $67,500 from Codeup, that’s between $67,500-$101,250 in foregone earnings.
So, you want a career in data science? A career accelerator like Codeup is the path for you. Still not convinced? We’re here to hear your concerns – contact us and let’s talk it through.