6 Data Science Careers You Could Launch with a Master’s Degree

 photo of a data scientist working at computer

Data science is a field where job titles are forming and changing quickly. While the “newness” of the field can be exciting, it also can lead to confusion as job titles and the work typically done by people in these roles gets ironed out. Because of this, it can be hard to figure out which career options are even out there, let alone which ones appeal most to you.

And, when it comes to education requirements, things get even a little more confusing. Sometimes employers will post a position like “data analyst,” asking for a bachelor's degree—or even a high school diploma with industry experience. All while other employers are seeking candidates for the same job title, yet asking for a Master’s degree in Data Science or similar field, at the minimum. There’s no denying it—data science is evolving too fast for total uniformity on the job boards.

But if you are interested in data science careers, you’ll need to be able to navigate your career prospects. That’s why we dug into the research for you, to help define the terms and job titles employers are using now to find the data science talent they need. We used real-time job analysis software, as well as some expert insight, to help clarify the situation.

What difference does a master’s degree make in data science?

“A master's degree in data science offers in-depth knowledge of industry-specific skills,” says Donald Wedding, Data Science instructor for Rasmussen University. Courses go deeper than simply covering the principles of programming or statistics, diving into current data science tools, techniques and best practices.

“The goal is that when you walk out, you’ll need a minimum of onboarding time for employers, and that saves them time and money,” Wedding says.

Even beyond the skills you gain, a Master’s degree in Data Science is one of the only nationally recognized thresholds in the industry. “Companies don’t find the term ‘data scientist’ all that meaningful,” Wedding says. “Anyone can call themselves a data scientist. You see plenty of people who took a few courses in statistics or programming who write ‘data scientist’ on their resume.”

Right now, a Master’s degree is the validation that those professionals actually know the data science industry. “You can find places that will take someone with a Bachelor’s,” Wedding says. “But I’ve worked all over, and the vast majority of employers expect a Master’s degree. You’d have to really be something special to get through the door without one.”

6 data science careers to consider

So what are some of these “doors” leading to? Here are six data science careers a Master’s degree can lead to.

1. Data architect

Also called: database architect, data warehouse specialist

Data-related work typically comes in one of three stages—collection, analysis or implementation.

“At the beginning, raw facts need to be collected and sorted into data,” Wedding explains.

Facts that are able to be tracked and recorded need a system to collect and make sense of them. That’s where data architects come in.

“The data doesn’t magically show up in a database,” Wedding says. “There’s lots of computer science and skill involved in gathering that information into data tables that make sense.” It’s essential that data architects do careful, accurate work. “If they get something wrong, then everything downstream involving that data is going to be wrong,” Wedding says.

These professionals are often meticulous people who don’t mind the repetitive tasks required to build data sets. The more a data architect understands about what makes data useful, the more valuable they will be. “If you have a data architect who also understands data science and can point you to useful or overlooked information, they are worth their weight in gold,” Wedding says.

The top five skills employers were looking for in 31,870 database architect job postings were:1

  1. SQL
  2. Database architecture
  3. Data warehousing
  4. Extraction transformation and loading (ETL)
  5. Data modeling

2. Data scientist

Also called: predictive modeler, predictive analyst

This term seems to encompass the widest array of jobs, and it has become something of a buzzword in big data careers. But whatever title it goes by, this is the role of the professional in the second stage of data. “If you are taking data and turning it into new information—you’re a data scientist,” Wedding says.

“Data scientists extract valuable information from raw data,” Wedding says. “Then they hand it off to someone else who turns that into a strategy.” That third stage of the data stream is often a professional with a business degree, but, once again, Wedding emphasizes that employees who understand the big picture of the data they are analyzing are really the future of the industry.

“I could see these roles merging at some point,” Wedding says. “The more you understand about where to find the data, how to interpret it, and what it’s for, the better.”

The top five skills employers were looking for in 23,934 data scientist job postings were:2

  1. Data science
  2. Machine learning
  3. Statistics
  4. Python®
  5. Apache Hadoop®

3. Data modeler

Data modelers are a kind of data scientist, according to Wedding. These professionals create conceptual models, logical models and physical data models for data warehouse systems. According to job posts, many employers are looking for professionals who have familiarity with information technology as well as modeling and excellent communication skills. Job duties might include creating and revising data integration modeling standards, collaborating with other teams and mapping source and target data.

The top five skills employers were looking for in 3,785 data modeler job postings were:3

  1. Data modeling software
  2. SQL
  3. Data warehousing
  4. Oracle software
  5. Extraction transformation and loading (ETL)

4. Data analyst

Also called: Data miner, data mining analyst

Data analysts analyze and break down complex data in easily understood formats. They analyze data for quality assurance and inconsistencies. Though these job duties look pretty identical to those of a data scientist, some in the field place data analyst in a less “predictive” role. This breaks down the difference into analysts who find answers to questions the company has, and scientists who come up with new questions that pertain to the future.

That being said, you might find the difference in these roles may vary from company to company. For more specifics, check out Data Analytics vs. Data Science: Deciphering the Differences.

The top five skills employers were looking for in 54,796 data analyst job postings were:4

  1. Data analysis
  2. SQL
  3. Tableau®
  4. Data quality
  5. SAS

5. Data developer

Also called: data engineer

Data developers incorporate architectural standards into application designs, participate in code reviews and identify applicable system platforms, components and dependencies. These data science professionals have a heavy design and development focus.

They also have responsibilities revolving around compliance and monitoring data security.

The top five skills employers were looking for in 11,032 data developer job postings were:5

  1. SQL
  2. Big data
  3. Extraction transformation and loading (ETL)
  4. Java®
  5. Data warehousing

6. Data engineer

Data engineers develop, construct, test and maintain architectures such as databases and large-scale data processing systems. This role is very similar to that of a data architect and in some cases (or job postings) might be interchangeable. But data engineering can also imply an overall vision for gathering data or even management responsibilities.

Common duties of a data engineer are to collaborate with data architects, modelers and IT team members on project goals, provide assistance to resolve all database issues related to performance, and develop PL/SQL scripts and indexes.

The top five skills employers were looking for in 14,829 data engineer job postings were:6

  1. Big data
  2. Python®
  3. Apache Hadoop®
  4. SQL
  5. Java®

Do you love analyzing data?

Now that you have a better idea of the data science careers a Master’s degree could lead to, you’re one step closer to the data science world. If some of the skills or duties listed above sounded like gibberish to you, don’t get too hung up on it—you’ll learn the ropes with formal education and practice. The most important factor is your enthusiasm for putting data to work.

“I’ve been in this industry for over two decades,” Wedding says. “There is one quality that will make you good at this—you have to love analyzing data.” Wedding shares that he has met brilliant statisticians from prestigious places who are terrible at data science, as well as stay-at-home moms and forest rangers who became incredible data scientists because they loved it.

“When you love it, you get good at it,” Wedding says. If you think data science is something you could really love, check out the Rasmussen University Master of Data Science program page to learn more.

1Burning-Glass.com (analysis of 31,870 database architect job postings, Jun. 1, 2017 – May 31, 2018)
2Burning-Glass.com (analysis of 23,934 data scientist job postings, Jun. 1, 2017 – May 31, 2018)
3Burning-Glass.com (analysis of 3,785 data modeler job postings, Jun. 1, 2017 – May 31, 2018)
4Burning-Glass.com (analysis of 54,796 data analyst job postings, Jun. 1, 2017 – May 31, 2018)
5Burning-Glass.com (analysis of 11,829 data developer job postings, Jun. 1, 2017 – May 31, 2018)
6Burning-Glass.com (analysis of 14,829 data engineer job postings, Jun. 1, 2017 – May 31, 2018)

Python is a registered trademark of The Python Software Foundation.
Apache Hadoop is a registered trademark of The Apache Software Foundation.
Oracle and Java are registered trademarks of Oracle Corporation.
Tableau is a registered trademark of Tableau Software.

Brianna Flavin

Brianna is a content writer for Collegis Education who writes student focused articles on behalf of Rasmussen University. She earned her MFA in poetry and teaches as an adjunct English instructor. She loves to write, teach and talk about the power of effective communication.

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This piece of ad content was created by Rasmussen University to support its educational programs. Rasmussen University may not prepare students for all positions featured within this content. Please visit www.rasmussen.edu/degrees for a list of programs offered. External links provided on rasmussen.edu are for reference only. Rasmussen University does not guarantee, approve, control, or specifically endorse the information or products available on websites linked to, and is not endorsed by website owners, authors and/or organizations referenced. Rasmussen University is a regionally accredited private university.

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