Career Insight

Data Analyst vs. Data Scientist: What’s the Difference?

data scientist doing data analysis on computer

What would be the first items you purchase in the event of an incoming hurricane? Batteries, flashlights, and water, perhaps? Definitely food, right? Well, did you know Pop-Tarts and beer are actually the top-selling products right before hurricanes?¹ Perhaps even more surprisingly, this information comes as a result of big data analysis from Walmart. As society, the economy, and the world as a whole continue to change, the importance of big data for corporations and organizations alike significantly grows. Two very important roles stand at the top of big data and how it’s used: data analysts and data scientists.

While not synonymous, the terms “data analyst” and “data scientist” are frequently interchanged. This can cause confusion, especially for those who are considering breaking into the world of tech with a career in big data. Learn more about data analysts, data scientists, their specific roles, key differences, and career opportunities for each.

Let’s Talk Big Data

Before we get into the specifics of data analysts and data scientists, let’s dig a little deeper into the field they both work on, big data. As the name suggests, big data is exactly that — data. It’s regularly collected by organizations for initiatives such as information mining, to improve operations, maximize marketing efforts, or enhance their products. But saying this data is big is a massive understatement. In fact, this type of data is so large and often so unclassified, that it cannot be processed through traditional methods. So, professionals such as data analysts and data scientists help manage the 3 properties of big data:

  • Volume: the sheer amount of data
  • Velocity: the speed at which data streams into an entity
  • Variety: the different types of data (structured, semi-structured, and unstructured)

The Data Analyst

Data analysts are responsible for working with big data by collecting, cleaning, and organizing it into much more usable forms. As they do so, they can identify trends and patterns that can be communicated with other team members in a clear and concise manner. Essentially, a data analyst’s primary goal is to create actionable information out of raw data that can be used in the decision-making process — turning indecipherable data into gold. Some of the most common attributes of data analysts include:

  • Knowledge of programming languages such as Python, SQL, R, and SAS which are especially useful in data extraction and manipulation.
  • Proficiency in creating meaningful data visualizations to help communicate important insights to team members and stakeholders.
  • The ability to clean, transform, and prepare information for data analysis.
  • Strong analytical skills.
  • The ability to identify patterns and relationships in data.
  • A thorough understanding of their company’s ethos to better relate their insights to the overarching company goal.
  • The ability to communicate complex results and insights in a clear and understandable manner.
  • Familiarity with data analysis tools such as Excel, Tableau, or PowerBI.

The Data Scientist

Data science is a broad term that includes data analytics, which means there are plenty of similarities between the two. Much like in data analytics, data science has the ultimate goal of finding value in incomprehensible data. A data scientist collects, cleans, and organizes data just like their analyst counterpart. Yet, one of their primary goals is to create the models and algorithms that make data analysis possible. Data scientists additionally use programming languages and statistical tools to test hypotheses and validate results. In essence, they use the insight found in data to make predictions and inform decisions. The characteristics of a data scientist generally include:

  • Knowledge of programming languages such as Python, R, or SQL.
  • The ability to efficiently analyze statistics to create predictions and draw insights from data.
  • Proficiency with machine learning algorithms to analyze large amounts of data and build predictive models.
  • The ability to properly and effectively communicate data insights via comprehensible visualizations.
  • Well-rounded skills in cleaning, manipulating, and transforming data for analysis purposes.
  • Proficiency in understanding the business context and the ability to ensure the insights they create align with the company’s goals.
  • Effective communication skills to better relay complex results and insights to stakeholders.

How They Differ

There’s a chance that the roles and responsibilities of data analysts and data scientists sound very similar, but things become clearly different once we break down the nature of each role. While both roles use the same data, data analysis aims to inform future decisions while data science seeks to predict their outcome.

Take the hurricane Walmart analysis from the beginning as an example. Stakeholders and decision-makers could have potentially asked data analysts a simple question: what do people buy before hurricanes? The analysts would then use big data to inform the question. However, a data scientist would use the same data to predict a new line of inquiries for the future. It could be questions regarding the popularity of sugary products before storms or the economic impact of alcohol purchases in stressful situations. Whatever the case, it’s the data scientist’s job to find the questions and the answers.

Another big difference is data scientists design and build models, algorithms, and software to make the analysis process faster and more effective. It’s a responsibility that aids them just as much as their data analyst peers. Machine learning is also an area of expertise for data scientists, as it gives them the opportunity to find new ways to run models or predict hypothetical outcomes to theoretical situations.

Career Breakdown

In today’s constantly evolving society, where the economy and technology are perpetually changing, information reigns supreme. The impact the right information can have on any given organization is invaluable and it’s the reason why big data jobs are growing.

According to the Bureau of Labor Statistics (BLS), the average growth rate in employment for data analysts by 2031 is 23%.²  For reference, the average growth rate for all other occupations is just 5%.²  In terms of wages, data analysts make a median annual salary of $82,360.²  That’s almost twice as much as the median annual wage for all other workers, which comes in at $45,760.²

The numbers are even more promising for data scientists. In fact, according to the BLS, the average growth rate for data scientists by 2031 is 36% and the median annual salary is $100,910.³ It’s safe to say that, if you’re ready to take on a rewarding career in tech, data analysis and data science are lucrative and promising options.

Go for Success

The Kenzie Academy program from Southern New Hampshire University can prepare you on your journey toward a career in tech. From the moment you contact Admissions, you’ll get dedicated support to help you identify your goals and stay on the right path to your continued success. Not to mention, our Career Services Team will be by your side as you prepare to transition into the job-seeking process. Learn a popular programming language and take the first step in your tech journey today. Apply today.


¹Datafloq, 4 Surprising Discoveries From Big Data Insights, on the internet (Viewed Feb. 13, 2022)

²Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, Operations Research Analysts, on the internet (Viewed Feb. 13, 2022)

³Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, Data Scientists, on the internet (Viewed Feb. 13, 2022)

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