Data Analyst vs. Data Scientist: What's the Difference?
Understanding the Key Differences & Similarities So You Can Decide Which Role is Right for You
You love numbers, you absolutely crushed your last Stats class, and you're an Excel wiz. Now you're thinking a career in data analysis/data science might be perfect for you. But which career should you pursue? And is there a difference between them, anyway?
The answer to the second question is a resounding yes, though the exact distinction often varies by company. To help you answer the first question, we interviewed experienced Data Scientists and Analysts at Attune Insurance, Cloudflare, Expedia Group, Pluralsight, and WW. They explained the unique skills needed to succeed in each role, as well as differences and similarities between them.
For starters, you should know that both roles are in high demand. The demand for Data Scientists is particularly high, and research indicates that the number of qualified Data Scientists is not growing at the same rate as the number of available data science positions.
But don't let these numbers be your only criteria — both roles can be quite lucrative as you advance your career, so read on to learn which role suits you best!
Data Analyst vs. Data Scientist: Defining The Roles
To kick things off and clear the air about the Data Analyst vs. Data Scientist roles, let's define the terms.
What They Have in Common:
As Carl Anderson, Director of Data Science at WW, explains, both Data Analysts and Data Scientists "tend to be numerate, methodical, and curious data lovers who love digging into numbers to ask and answer questions, tell compelling stories, and generate impactful insights for the business. There is no formal definition for these roles, and a role that might be termed Analyst in one organization might be termed a Data Scientist in another."
That said, all five companies we interviewed shared the following core tenets in their definition of each role:
Data Analyst Definition:
Data Analysts use data to define and measure success metrics or Key Performance Indicators (KPIs). They work closely with business development and product teams to provide insights on why these KPIs are or aren't being achieved.
Data Scientist Definition:
Data Scientists leverage their understanding of statistics and software engineering in order to build predictive models.
So what does this mean in practice?
Consider this example from the the data team at Cloudflare (composed of Head of Data & Analytics, Sirisha Gorantla, Lead Data Analyst, Nikole Phillips, and Head of Data Science, Katrina Riehl):
If a business wanted to know how many customer sign-ups there were in the last quarter, Data Analysts would be responsible for finding and sharing this information. They might also then compare that quarter's performance to the previous one, and share some reasons for the growth or decline in sign-ups.
If the data analysts determined that sign-ups were down, then Data Scientists could help address the problem by building a predictive model to help identify prospects that have a higher likelihood to convert as customers.
Summing It Up
Katelyn Tolbert, current Product Manager and former Data Scientist at Pluralsight, summed it up well:
Data Analysts use descriptive statistics to understand KPIs and make sense of past data. Data Scientists use advanced statistics and modeling to make predictions for the future.
What You Need to Succeed: Skills of Data Analysts vs. Data Scientists
Now that you understand the basic definitions of each of these roles, let's take a look at the personal qualifications, characteristics, and skills you need to succeed in each role.
As with the roles themselves, there is significant overlap in the skills you need to succeed in either of these roles. Martha Dreiling, Head of Analytics and Corporate Operations at Attune Insurance, explains it like this:
"In any data-centric role there are three really critical skills someone needs to have. The first is being statistically savvy, the second is data engineering skills and the ability to manipulate data, and the third is having a strong business sense and the ability to communicate findings to less technical individuals. Data Scientists are generally more indexed on the first and second skills. Great Data Scientists are great at all three. Similarly, Data Analysts are typically great on skills 2 and 3, but great ones also have a solid understanding of statistics."
While all data-centric roles require this basic suite of skills, there are some specific skills/areas of knowledge you should have depending on the type of role you'd like to apply for.
For instance, because Data Scientists need to have an advanced understanding of statistics in order to understand and analyze big data, those with a PhD in a data-oriented field may find themselves well-suited to a career in Data Science.
That's exactly how Soma Bhattacharya, who holds a PhD in economics, ultimately landed a job as a Data Science Manager at Expedia Group. In her words, "The only thing that changed when I shifted gears from academia to health policy to industry, was the kind of questions I was solving for." So if you have an academic background in statistical modeling and are ready to make the switch to industry, a career in data science is likely a great fit for you!
Alternatively, if you have less experience with advanced stats or prefer working more directly with business stakeholders, you might be better suited to a career as a Data Analyst.
Consider which of these skills you already have and which ones you'd like to develop as you decide whether a career in data analysis or data science may be a better fit for you:
Data Analyst Skills
- Strategic thinking
- Business acumen
- Data Visualization Tools (Tableau, Power BI, etc.)
- Basic skills in R/Python
Data Scientist Skills
- Highly competent in scripting languages like Python and/or R
- Machine learning
- Statistical modeling
- Data mining concepts
- SDLC (Software Development Life Cycle)
The Cloudflare team also rightly points out that both Data Analysts and Data Scientists "need to be proficient in data discovery and analysis to have a strong understanding of the data. Both roles also need to have a fundamental understanding of data stores/databases because these roles require consistent collaboration with Data Engineers."
(By the way, if you're wondering about the difference between Data Scientists and Data Engineers, a data engineer develops/maintains databases and large-scale processing systems, while Data Scientists are the ones who make sense of all that data.)
Responsibilities of Data Analysts vs. Data Scientists: What You'll Do Each Day
Wondering what you'll likely do at work each day? These tasks are commonly within the scope of job descriptions for data analysts and data scientists.
Data Analyst Tasks/Responsibilities
- Build dashboards
- Report daily/weekly reporting on KPIs
- Complete one-off analyses/queries
- Visualize data (often in Tableau)
- Communicate findings/collaborate with key internal stakeholders
Data Scientist Tasks/Responsibilities
- Build predictive models & algorithms
- Mine and analyze data to optimize business outcomes
- Communicate findings/collaborate with key internal stakeholders
- Data analysts and data scientists work on similar business problems. They both engage with data engineers and business stakeholders on a regular basis to do their job.
- Data Analysts work with business to define success metrics, build dashboards, perform exploratory analysis, and present key insights and takeaways to help measure the progress and drive actionable strategic outcomes. This role requires more human intuition than the Data Scientist role.
- Data Scientists work on building predictive models to help improve on the success metrics — they have the same goal as data analysts, but their approach is different. They rely more heavily on machine learning & data processes than on human intuition.
Martha gave an example of how this works at Attune Insurance, a small-but-growing company:
"Given that we are a small but growing company, the two roles work closely together and have some overlap. Data science is more focused on predictive analytics, whereas data analytics is focused on getting data to end users (other teams internally) by cleaning it and making it available via reporting tools. Both roles are responsible for being able to communicate findings to the rest of the business, but data analytics may interact with other teams more frequently."
Have more questions about data science, data analysis, or even data engineering? Let us know in the comments!
And if you're on the fence about whether a career in data analysis/data science is right for you, we'll leave you with a piece of advice from Soma, Senior Data Science Manager at Expedia Group:
"If solving for real-world problems using the power of big data and scientific methodology excites you, then I'd encourage you to jump in and start snorkeling into this ocean… you never know what you might discover."
Ready to dive in?
Check out these open data science and data analysis roles:
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