As a marketer, data science is your secret weapon.
Data science allows the marketer to find greater success with less money. This is because every effort can focus more accurately with data. It should then come as no surprise that the American Marketing Association names data science as an in-demand skill for today’s marketers.
[Image: KDnuggets]
For marketers looking to advance their careers, it’s important to understand the path of adding data science to their skills. Even for those who don’t have an interest in learning the STEM-driven field, understanding the background of the data scientist can help frame the work that they do.
In this article, we will investigate the career paths of the marketer who wants to learn data science. We’ll talk about the important traits for them to have, and discuss the pros and cons to each path of education. But we dive into how to become a data scientist, we need to answer an important question.
What Is a Data Scientist?
The answer to this will depend a lot on who you ask. For some, data scientist is a buzzword term for a data analyst. For others, the data scientist is the keystone of their business. While the traditional data analyst role still exists, more and more people are required to have a deeper understanding of data in order to help companies grow.
These needs have led to a role that we call “The Square Peg.” It exists in companies where data isn’t their primary focus, but they have elements that are data-driven. For instance, marketers at companies where data is not the product, or a data scientist who helps a manufacturing business understand their logistics metrics.
While anyone can get training, if their personality doesn’t match the job then they’ll never find success. So first we need to investigate two factors:
- The traits of the data scientist
- The skills that they should already have
The Traits of the Data Scientist
There has been an astonishing amount of articles written about what makes a great data scientist. While each of them provides some unique perspective, the important task is finding the points upon which they all agree. In this case, we’re not speaking about education, but rather the personality traits that make an ideal candidate for a career in data science.
It is interesting, but not surprising, that the traits you want in a data scientist align well with the traits you want to see in a marketing professional.
Curiosity
Data science is a field full of questions. The data scientist needs to be curious by nature. They should be the type of person who is always looking at a scenario and gathering new findings from it. The mark of a great data scientist is one who takes every bit of relevant information, and then asks for more.
Marketers have a natural penchant for figuring out why certain behaviors happen, and how they can influence those behaviors to their benefit. Adding data science to their set of skills allows the marketer to see these challenges from a different perspective, often leading to better results.
No Fear of Failure
A data scientist must be cognizant of what the “scientist” part of their title means. Science is a field of theories, exploration, and testing. Every one of these steps can lead to failure. The great data scientist embraces those failures and understands how to implement them into future scenarios.
For marketers, failure can be a path to success because each one is an opportunity to identify methods that work better. Split testing, for example, lets us narrow down what works by pinpointing what doesn’t.
Technical Prowess
Every aspect of the data scientist’s job involves technology, so it’s no place for luddites. Although we’re not yet into the technical side of the data scientist’s education, it’s important that they have an innate comfort with technology.
You’d be hard-pressed to find a marketer in today’s landscape that doesn’t use a bevy of technical tools. They run the gamut from dead simple to pivot-table nightmare. Each of them requires a certain level of comfort with technology.
Contextual Communicator
The importance of this trait is two-fold: Logic tells us that the data scientist will need to be able to present their findings in the most usable context. But more importantly, they need to first be able to ask the right questions to find out what information they need.
Ask any marketer what their best traits are, and it’s likely that they’ll tell you that they’re a great communicator. They understand the value of listening, and then showing their products as a solution to the context that’s been provided.
There are definitely other traits that are important to the data science marketer. Yet, these four are the ones that nearly every source says are critical.
The Skills of a Data Scientist
For the marketing professional who wants to add data science to their arsenal, this is where the paths diverge. There are some skills that are necessary in order to grasp data science. Most of them will be perfected during education. These are some basics that a marketer should have before taking the plunge.
The skills that you will need vary depending on the job or company that you hope to join. When referring to job listings that would qualify as a “Square Peg,” we get a better understanding of what is required for the positions:
- Basic Programming – R, Java, Python, Hadoop, and SQL are all frequently used.
- Statistics – At least an understanding of the basics, such as a p-value.
- Math – Multivariable calculus and linear algebra will help the data scientist understand which tests to run, and how to build analysis routines. Though less important for The Square Peg roles, these are still valuable skills for any data scientist.
- Software Development – This is one that will vary depending on the position. That said, the ability to write and deploy software is critical in many jobs, and a leg up in others.
Fortunately, even if you don’t have these skills today, there are resources available to help you get started.
How to Become a Data Scientist
Choosing Your Education
Even if a marketer meets all of the prerequisites, they still need formal data science training. There are three main options for pursuing an education in data science:
- Self-Study Programs
- Certification Boot Camps
- University Degrees
The education demands that you will see on job listings are often far different from the education shared by people who are actually working in data science positions. Further complicating matters, the role that a data scientist will fill guides the education that they will need. Although 44 percent of data scientists have a Master’s Degree as their highest level of education, not all of those degrees are in fields directly related to data science.
To begin the process of clarification, we will look at the three options, listing the points that you should know for each of them.
Self Study Programs
Self-study programs like Udacity and Coursera have risen in popularity in recent years, and for good reason. These courses are often available for significantly less money than boot camps or university programs. Further, the nature of self-study programs allows them to be completed as time permits.
[Source: Udacity]
While these programs only provide a certificate rather than a degree, that may not be a negative according to noted data scientist Edwin Chen:
“Just as people can teach themselves to be software engineers or mathematicians, a lot of people can teach themselves to be data scientists. After all, ‘data science’ still isn’t really something you learn in school, though more and more schools are offering data science programs. A lot of the best data scientists I know come from fields that aren’t the fields normally associated with data science like machine learning, statistics, and computer science.”
The appeal of a Master’s or PhD course might be strong. But it’s important to consider self-study programs as a viable path toward integrating data science with your marketing career.
Pros:
- Convenience: Self-study courses can be done at your own pace, and without travel.
- Affordable: Some courses are free, but even paid courses are inexpensive.
- Time Savings: Most courses can be completed in 8-18 months.
Cons:
- No degree path is offered.
- There is no peer or teacher guidance.
- Self-study programs do not offer job search assistance.
For the Marketer: Self-study programs can be an ideal answer if you’re the type of person who excels outside of a traditional classroom environment. You also have the ability to work around your existing schedule. Just bear in mind the lack of one-on-one instruction that is available.
Boot Camps
Somewhere between the self-study courses and a dedicated degree, you’ll find data science boot camps. These are intensive, in-person courses, taught by practicing data scientists, between six weeks and 3 months in length. They are pricier than self-study courses and require dedicated attendance, but they offer a level of hands-on instruction that self-study cannot.
[Image: Digital Inclusion]
Boot camp hosts run the gamut from startup incubators to traditional universities that are looking to expand their offerings. The beauty of this format comes in the choices that are available to students. For example, there are camps focused on specific areas, such as Data Application Lab’s program which focuses on marketing.
There are boot camp listings across the Internet, but Switchup has in-depth reviews of many of the offerings.
Pros:
- Speedy Education: Can be completed in 6 weeks to 3 months.
- Relative Affordability: Compared to getting a Master’s Degree, boot camps range from free – $16,000.
- Ideal for those looking to change careers quickly with intensive study.
- Many boot camps offer job search assistance.
Cons:
- The condensed learning schedule may prove difficult for some.
- Some programs require a time commitment that doesn’t allow for simultaneous full-time work.
For the Marketer: Boot camps can pose a challenge, because of their time demands. But the marketer who wants the fastest path to adding data science to their set of skills could be well rewarded for taking the plunge. It’s also hard to overstate the value of being taught by data scientists who are actively working in the field.
University Degrees
As the most traditionally-structured learning environment, the Master’s Degree also has the highest requirements. Unfortunately, it may leave graduates wanting. Third Nature Inc. President Mark Madsen notes that not all degree programs are providing their students with the real-life skills that they’ll need to find success.
“I have mixed feelings about the university programs. It seems to me that they’re more designed to capitalize on the fact that the demand is out there than they are in producing good data scientists. Often, they’re doing it by creating programs that emulate what they think people need to learn. And if you think about the early people who were doing this, they had a weird combination of math and programming and business problems. They all came from different areas. They grew themselves. The universities didn’t grow them.”
That said, it’s not uncommon to see Square Peg job listings, such as this one for Aetna, that note a strong preference for a Master’s degree or higher. According to Randy Bartlett, who holds two patents for predictive modeling, these companies may be doing themselves a disservice when it comes to hiring workers with real-world skills:
“You’d think the master’s degree would be better, but I don’t think so. The BS in statistics is more methodological. By the time you get to the MS, you’re working with the professors and they want to teach you a lot of theory. You’re going to learn things from a very academic point of view, which will help you, but only if you want to publish theoretical papers.”
That’s not to say Master’s Degree programs are a thing of the past. Only that it’s important to look at the programs carefully to find out if they are based more in theory or practice.
Pros:
- Diploma upon completion.
- Structured learning with university-level instructors.
- Real-world experience: Many programs include internship placement.
- Ample time to learn and absorb all of the information.
Cons:
- Expensive: Could cost between $20,000 – $70,000, not including living expenses and lost income.
- Most programs are on-campus, and require between 9 and 20 months to complete.
For the Marketer: With more distance education programs opening all the time, the Master’s path is closer within reach for many people. However, it’s worth being aware of the difference in what a degree program will teach versus the practical applications that can be found in self-study or boot camps.
Which Path to Choose?
For many marketers, the Master’s Degree programs are not going to be the right answer. The time constraints are just too great. Although there is a notable exception for those taking part-time courses, distance education, or doing night study.
If you find that a Master’s program doesn’t fit, that leaves self-study programs and boot camps. Both of these provide the same certificates. It’s then up to the marketer to decide which option better fits their needs and schedule.
Job Listings: Only Part of the Story
Look at any job listing that fits the standard we’ve set for a Square Peg. Almost without fail you will see a wishlist set out by the employer that would rule out the majority of candidates. But companies are finding that, by sticking squarely to these wishlists, they’re unable to fill the roles fast enough to match their demand.
[Image: KDnuggets]
Often times, experience matters more. Self-study and boot camp candidates can have an advantage in these situations. They have earned certifications showing that they have been tested according to industry-standard or vendor-specific benchmarks. Add that education to their existing marketing experience, and you have someone who is an instant asset to any marketing team.
It’s an unfortunate truth that many data science job listings don’t differentiate between the various types of positions. To make matters worse, many companies don’t advertise for data science jobs in marketing, even when the data scientist would be an ideal candidate. It’s up to the marketer to look for opportunities that will make good use of their education.
Sometimes the job search will require the candidate to educate the company on the value of a data scientist. More often than not there are clues in the listings that can help present an open door. Even if a job is not listed as a data science position, you should look for keywords like:
- Analytics
- Statistics
- SQL
- Models
- Python/R
A Journey in How to Become a Data Scientist
As a marketer, there are always new problems arising, and data science is allowing us to solve them more effectively than ever before.
Now that you’ve found the right path into data science, you’ve taken the first step on a long journey. The career of a marketer in data science is ever-changing, with new tools and technologies appearing frequently. Continuing education is critical, whether it comes from classes, seminars, or any number of online programs.
We’ve given you the guidance that you need to make an informed decision about your future. Use it to build something great.
