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What is a data analyst, what is a data scientist—and how can you get started in these lucrative careers?

Data professionals need skills in computer programming, statistics, and communication to thrive.

Data analyst and data scientist are two related job titles that are becoming more and more important in the modern workplace. Both jobs involve working with data to solve business problems: Data analysts tend to focus on simpler, more immediate issues, whereas data scientists—who hold more advanced degrees and earn more money—look at the bigger picture. But the two jobs share many concerns and require many of the same baseline skills, and it's not uncommon for data analysts to become data scientists as their careers progress.

Data analyst vs. data scientist: Definitions

Data analysts and data scientists are both tasked with wrangling the rapidly expanding data lakes enterprises have at their disposal and making something useful out of them. Northeastern University, which offers both undergraduate and graduate programs in data science, defines the two jobs this way:

"Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data scientists, on the other hand, design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis."

Those definitions may make the differences between the two jobs seem a bit abstract. But Simplilearn, which offers a big data and analytics certification program, draws the distinction between the two jobs like this:

"A data scientist is expected to formulate the questions that will help a business and then proceed in solving them, while a data analyst is given questions by the business team to pursue a solution with that guidance."

As always, you should take job descriptions with a grain of salt. There's obviously room for overlap here, especially within a small team. Jobs vary from employer to employer, and sometimes job title inflation means that someone called a data scientist will spend more of their time fulfilling the duties of a data analyst. But this should give you a general lay of the land.

 

What does a data analyst do, and what does a data scientist do?

To get a better understanding of the nuts and bolts of these jobs, take a look at what a typical day is like for folks who hold these job titles. For instance, Eric Fandel, a data analyst at a company that helps fill ad slots on mobile apps, says his main duties are "building internal reporting/analytics dashboards, providing account managers with relevant datapoints to set them up for success, and helping our engineering optimization team understand how new product features and algorithms are performing." In other words, he combs through data to help his company understand how it's performing when it comes to well understood metrics and communicates that information to other people within the company.

Northeastern lists some typical data analyst job responsibilities:

  • Designing and maintaining data systems and databases, and keeping the data and code clean
  • Mining data from primary and secondary sources
  • Using statistical tools to interpret data with an eye for relevant trends and patterns
  • Presenting conclusions to the rest of the organization, including leadership
  • Collaborating with coworkers to develop data processes and policies

Coursera has a blog post that describes a day in the life of a fictional data scientist, which gives you a great feel for their bigger picture concerns. This composite character works for a beverage company, and her day is taken up by two big tasks: figuring out how to analyze social media sentiment to assess what millennials think about three features of a new line of natural sports drinks, and meeting with company leaders to discuss building an algorithm that can revise the distribution network to get the company's products to retailers in a more systematic way. In both cases, the job involves analyzing data. But, in contrast with the issues a data analyst faces, to tackle these problems, our data scientist needs to refine the questions as well: "How can we use social media data to tell us what customers think? What are our goals for getting bottles onto shelves, and how can we build models that can tell us how to meet those goals?"

The University of Wisconsin, which also offers a data science program, lists these data scientist job responsibilities:

  • Identifying the data analytics problems whose answers will be most promising to the organization
  • Collecting and validating the appropriate data from various sources pertinent to those problems
  • Coming up with the models and algorithms for analyzing that data
  • Finding the patterns and trends within that data
  • Telling the rest of the organization about what you've learned

Data analysts and data scientists: Skills

These lists of responsibilities offer a picture of the skills data analysts and data scientists need. Both must have foundational knowledge of math and statistics in order to understand the data they're working with. And to work with that data on computers, they need proficiency in the various tools they'll be using, according to Northeastern. That includes programming languages like SQL, R, and Python (data science mentor Jeremie Harris says Python is an absolute must); data visualization software like Tablaeu and Qlik; and mastery of the mysteries of Excel and other spreadsheet apps. Data scientists need to build on these foundations and go further, particularly when it comes computer science: They need to understand machine learning and artificial intelligence as they build the models that use the collected data to predict future events. On Twitter, Slack's Josh Wills perhaps captures the overlap best, noting that a data scientist is a "person who is better at statistics than any software engineer and better at software engineering than any statistician."

But data professionals are intermediaries between the raw data and the humans who need to understand what that data means, so data analysts and scientists must also have a host of soft skills in their arsenal. Good communication and interpersonal skills are foremost among them: Data analysts and scientists need to be able to convey the information they've discovered to their coworkers and company leaders in the most effective way possible.

They must also understand their organization's business needs so they can know what questions to pose to the data under their control. Check out Alluvium CEO Drew Conway's data science Venn diagram. He sees data science as the zone in the overlap between programming skills, math and stats knowledge, and substantive expertise—that is, knowledge about and ability to work in the field in which your company operates. To revisit our "day in the life" examples above, data analyst Fandel needs to know how the online advertising ecosystem works in order to do his job, and our fictional data scientist needs to know at least the basics of how beverages are manufactured, marketed, and distributed. Data professionals need to be voracious learners who can quickly get up to speed on their organization's business.

How to become a data analyst or data scientist: Training, degrees, certifications, and career path

As noted, a number of universities offer programs in data science or similar fields, and while they aren't the only degrees that feed into data professional careers, they certainly offer a leg up. A data scientist typically has a graduate degree of some kind—if not in data science, then in computer science or some other field—whereas many data analysts have only a bachelor's degree. (CIO offers its take on the best data science programs out there, if you're interested.) But data science is a fast growing and changing field, and not every job is simply being filled with newly minted college grads with a specialized degree. For those looking to catch up, there are plenty of self-directed courses and MOOCs out there; the Open Data Science Conference offers its top picks in that category.

Data science mentor Harris outlines three paths to a career in this field, with tips on how to thrive in each:

  • The road of the self-taught, building a brand with open source projects and teaching themselves the basics via MOOCs and self-exploration
  • The pivot of the software engineer, possibly taking a pay cut as they port their computer skills to the world of data
  • The upward climb of the recent STEM graduate, who probably still needs to fill some gaps

And, as it seems to be a prerequisite for any tech-related field, there are certifications aplenty you can take to demonstrate your data science bona fides. A CIO article offers a list of the most prestigious and profitable.

Data analyst and data scientist salaries

The pay range for data analysts can be quite broad. This makes sense, since the job title is often given to entry-level employees but also held by folks who've spent several years on the job and grown into it, gaining new skills along the way. Robert Half Technology's 2019 Salary Guide puts the typical range between $81,750 and $138,000 per year.

With their bigger job responsibilities and typically higher levels of education, data scientists can command a heftier salary: RHT's range runs from $116,000 to $163,500. For the past few years, data scientist has been ranked the most promising job in America, based on the high demand and potential for salary growth. However, TechRepublic reports that in 2019, growth in data scientist wages started to plateau, becoming more in line with the mainstream of software developer salaries. Still, data professionals earn great salaries in general compared with most fields out there, and if the job interests you and you have the skills, the rewards are waiting for you.

This article/content was written by the individual writer identified and does not necessarily reflect the view of Hewlett Packard Enterprise Company.