“Why did two marketers decide not to get married? Because they weren’t on the same landing page.”
At first glance, jumping from marketing to data science might seem absurd. Marketing’s all about promoting products and services to customers, while data science involves generating insights from data, right? But these careers overlap in several ways, so switching to data science isn’t as crazy as you think. In fact, it’s perfectly doable if you’re willing to develop a new skill set to complement the one you already have. We’re going to tell you how to become a data scientist if your background is in marketing.
How Are Marketing and Data Science Similar?
Marketing and data science have many similarities when it comes to data analytics. Think about it. You probably analyze data sets in your marketing job all the time. That might include:
- Using Google Analytics to track SEO campaigns
- Understanding consumer preferences with business intelligence tools
- Predicting how your target audience will react to your company’s products and services
Unsurprisingly, data science also involves lots of data analytics. These analytics are probably a little more advanced than you’re used to, but over time, you can certainly learn how to develop statistical algorithms and analyze complex algorithms if you enjoy working with marketing data. Willing to put the work in and enroll in a data science program? There’s no reason why you can’t land a rewarding, well-paid job in data science.
What Are the Differences Between Marketing and Data Science?
You can’t get a job in data science just because you know how to use Google Analytics. There’s a whole skill set to develop before starting your new career. One thing you’ll have to learn is coding, which data scientists use to create statistical models and algorithms. While data science requires less coding knowledge than data engineering, there’s still a steep learning curve. Grappling with Python or R might sound daunting, but knowing programming languages like these will further your career.
Another difference is how you analyze data in these two roles. As a marketer, you likely identify previous trends to learn more about marketing campaigns. Data science, however, requires an understanding of how data impacts an entire company. So you’ll be generating insights not just about marketing but about sales, financials, and day-to-day business processes too. Again, these are skills you can master.
The tools and technologies used in data science are also different than those in marketing, While you might use marketing automation software and a CRM system, data scientists rely on analytics engines like Apache Spark to do their jobs properly.
Why Become a Data Scientist Instead of a Marketer?
There are many reasons to become a data scientist. Perhaps you don’t feel fulfilled in your current marketing role or want to try a whole new career at this stage in your life. The good news is that data scientists earn, on average, a much higher salary than marketers. (The average salary for a marketing manager in the United States is $91,060; the average salary for a data scientist is $124,852.)
But there’s more to becoming a data scientist than the salary. Here are some of the other benefits of this role:
- Data scientists are in high demand right now, meaning it could be easy to find a job.
- Data science is a challenging position in which no two days are the same. You’ll be responsible for solving business challenges through data analysis and helping companies grow.
- All industries need data scientists, so you won’t have to confine yourself to one sector. You might become a data science professional in healthcare, finance, professional services, or another industry.
How to Become a Data Scientist
Transitioning from a marketing role to data science is achievable, but you’ll need education and experience. You might want to get a data science degree and kick-start your career that way or sign up for a shorter program that teaches you the fundamentals of the industry. Whatever route you take, you’ll need to build a data science portfolio over time that shows future employers your skills and expertise.
One of the best ways to become a data scientist with little experience is to enroll in a bootcamp that teaches the fundamentals of data science. Over a couple of months, you can learn how to complete basic coding tasks, work with data science tools, and decide whether this career change is right for you. Then you can move on to a longer and more specialized data science program that hones your skills and teaches you new ones. After graduating, you might have enough proficiency to get an entry-level job as a junior data scientist. Even this lower-level role might pay more than your current earnings as a marketer, with an average salary of $88,237 in the U.S.
From Marketer to Data Scientist: Final Word
Marketing and data science have differences but overlap in several ways, making a career change possible. If you want to solve complex business challenges and analyze statistical models and algorithms, you can transition from marketing to data science by enrolling in a program for data scientists, gaining some real-world experience, and slowly building your portfolio.
How The Data Incubator Can Help
Want to move from marketing to data science? The Data Incubator’s programs help you develop skills from industry experts. TDI’s programs let you develop skills from the country’s top instructors, paving the way for your future data science career. The Data Incubator can help you learn real-world data science skills regardless of your educational experience:
- New to data science? The Data Incubator’s Data Science Essentials course will teach you practical skills in only eight weeks. Register for Data Science Essentials
- Want to boost your chances of finding a high-paid position? Sign up for the Data Science Boot Camp and get real-world experience from some of the world’s best tutors and experts.
The Data Incubator is here to guide you through your data journey! Get in touch with the admissions team with any questions about applying for a data science program.