No degree? No problem. Whether you’re considering a career change or have taken a winding road to get here, the data science profession is welcoming to analytical minds that are equipped with the right skills. Read on to learn how to launch a data science career at any age.
Here’s what we’ll cover:
It’s never too late to start your data science journey. Although mid-career pivots can be daunting, it’s possible to become a data scientist at any age.
Contrary to popular belief, a degree in computer science or statistics—although useful—is not necessary to pursue a new career in data science; you can gain the practical knowledge and technical skills sought by hiring managers through independent study or online bootcamps.
Whether you’re looking to upskill or you’re starting from a place unrelated to tech, there is no shortage of courses and programs that will help you achieve your goals.
Data science revolves around using machine learning, statistics, and other computational skills to tease out meaningful and actionable insights from collected data. At their best, data scientists help organizations understand key findings from the information collected so that they can make informed, data-backed decisions instead of shooting in the dark.
Here are a few key data scientist responsibilities:
Data scientists also use machine learning algorithms to analyze and extract information from data. Machine learning helps data scientists make informed predictions and recommendations that an organization will find useful.
No. When filling a data science role, hiring managers look for evidence that a candidate has the skills to get the job done. This typically takes the form of a portfolio with case studies that highlight a candidate’s technical skills, problem-solving process, and ability to wrangle data sets to find actionable insights—all of which can be learned and fostered outside of a college setting.
While degrees related to data and computer science can be useful in laying a foundational understanding of statistics and coding, many online courses, bootcamps, and, to some extent, self-study, have proven effective at helping candidates pick up the skills and build on the experience needed to be job-ready.
Aspiring data scientists can acquire skills and experience outside of degree programs through independent study or virtual bootcamps. While teaching yourself allows you to move at whatever pace you choose, an online bootcamp will provide the structure and support you need to get hired as a data scientist.
Here is why data science bootcamps are so effective:
As long as you’ve got the right skills, you can become a data scientist at any age. Want proof? Fast Company highlighted the successful mid-career pivots of three women who moved from design and communications roles to data science.
Curious about people who have made mid-career pivots with the help of a data science bootcamp? Former reporter Chong Zi Liang reflects in a blog post on his transition from communications to data science at the age of 34. With no prior tech experience, Chong took several short coding courses before enrolling in a 12-week data science bootcamp.
After graduation, he landed a data science role at 99.co, a geospatial property search engine. Chong feels that his background in communications augments his ability to organize data-driven insights in a way that facilitates stakeholder decision-making.
Is data science the right career for you?
Springboard offers a comprehensive data science bootcamp. You’ll work with a one-on-one mentor to learn about data science, data wrangling, machine learning, and Python—and finish it all off with a portfolio-worthy capstone project.
Check out Springboard’s Data Science Career Track to see if you qualify.
Not quite ready to dive into a data science bootcamp?
Springboard now offers a Data Science Prep Course, where you can learn the foundational coding and statistics skills needed to start your career in data science.
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