Key Takeaways
- Data scientists are able to turn raw data into valuable insights and help guide the direction of a business.
- A data analyst examines data to identify trends and insights that help businesses make informed decisions.
- While data scientists use advanced techniques to predict what might happen next, data analysts mainly look at historical data to spot trends that help improve current business choices.
Hundreds of millions of terabytes of data are created, captured, copied, or consumed every single day—an astounding amount that adds up to 147 zettabytes annually. What’s even more impressive is the fact that this number isn’t static—it’s growing rapidly, thanks to advancements in technology. Making sense of that data and actually using it to aid decision-making is where the real value lies. This task falls to skilled professionals in roles like data scientist and data analyst.
These two professions are often grouped together, but they serve distinct purposes. To understand the differences between a data scientist vs. data analyst, we need to see how they complement each other and where the two roles diverge.
What Is a Data Scientist?
A data scientist is essentially a professional in the field of data science who works with massive sets of data, trying to find patterns or trends that can help businesses make better-informed decisions. They focus on harnessing this data to predict what might happen in the future or to tackle tricky business challenges.
To do this, they often build computer programs that can analyze data automatically and make predictions, like how sales might look next quarter or what customers might buy based on past behavior.
Data scientists use a variety of tools to analyze the data and create models. Two of the most common programming languages they rely on are Python and R. These languages are popular because they come with a large number of pre-built libraries and frameworks, which make it easier for data scientists to manipulate data and build complex models without having to start from scratch.
Some other often-used tools include:
- TensorFlow for building machine learning models, particularly for deep learning applications
- SQL for querying and managing databases
- Tableau and Power BI for creating visualizations that help communicate data insights
- Apache Spark for handling and processing large datasets quickly
These tools, along with others like scikit-learn for machine learning, are part of a data scientist’s toolkit that allows them to tackle complex problems and generate valuable insights from data.
What does a data scientist do?
Since the main responsibility of a data scientist is to harness the power of data to create predictive models and solutions on a day-to-day basis, they’re typically tasked with the following:
- Cleaning and preprocessing raw data
- Analyzing large datasets
- Building and training machine learning models
- Evaluating and testing models to ensure they provide reliable predictions
- Collaborating with cross-functional teams
- Creating visualizations to make complex data more understandable
- Continuously updating models with new data to improve accuracy
What Is a Data Analyst?
A data analyst’s work is closely related to that of data scientists since they work with organized data and help businesses make smarter decisions. Essentially, they look at information that’s already been sorted, like sales numbers or customer data, and search for patterns or trends that could point to ways to improve how the business runs.
For example, a data analyst working in sales might analyze sales reports in order to figure out which products are flying off the shelves and also identify the ones that are barely moving.
After gathering these insights, the analyst presents their findings using charts or reports, ensuring the information is easy for others to understand. This enables decision-makers to quickly see which products are worth focusing on and which might need adjustments. The data analyst’s role is crucial because it helps the company identify successful strategies and areas for improvement.
One of the most commonly used tools by data analysts is Excel because it is great for smaller datasets and has features like charts and graphs that make it easier to see patterns and understand the data. When the data gets larger or more complex, SQL may be used. If the analysts decide to conduct a more advanced analysis, Python and R can also be quite useful for statistical and predictive modeling.
In all cases, tools like Google Analytics help track how websites are performing and analyze user behavior, while Hadoop is used for processing huge datasets, especially when handling large amounts of data, also known as big data.
Once they have the results, data analysts can use tools like Tableau and Power BI to present them in a more interactive and easy-to-understand way, even for other teams not necessarily familiar with data analysis.
What does a data analyst do?
For a data analyst to gather insights from structured datasets, they should:
- Extract data from databases
- Clean and organize raw data for accuracy before analysis
- Analyze data to spot trends, outliers, and patterns
- Conduct statistical analysis to validate findings and ensure data integrity
- Develop charts, graphs, and dashboards to visualize key trends and insights.
- Provide actionable recommendations based on data findings
- Collaborate with teams to define business questions and ensure data solutions meet their needs
The Core Differences Between Data Scientists and Data Analysts
Think of a data analyst as someone who works like a historian, looking back at past data to understand trends and patterns, while a data scientist acts more like a futurist, using advanced methods to predict what might happen in the future and solve complex problems. Their approaches, skill sets, and the type of work they do are quite different and set the two apart.
Skills and expertise
The skill sets required for being a data analyst and data scientist differ mainly due to the varying complexity of their roles. However, generally speaking, data scientists need to have a solid understanding of advanced programming techniques, statistical modeling, and machine learning, which they use to build predictive models and uncover insights from complex, often unstructured data.
In contrast, data analysts focus more on using tools that help organize and simplify existing datasets. While they also have some coding skills, their main tasks revolve around making sense of the data and communicating insights effectively.
Amanda Quick, a Syracuse University graduate, offers valuable insight into this aspect of data analysis, noting, “Combining creative storytelling with analytical skills provides a competitive edge in modern workplaces.” She, rightfully so, points to how data analysts, in addition to analysis, also craft narratives that make even complex findings accessible and understandable for decision-makers.
Approach to data
Their approach to data is another difference between data scientists and data analysts. The former are often tasked with exploring unstructured data, and their work often leads to prescriptive insights. On the other hand, the latter generally work with structured data, and they provide descriptive insights.
A data analyst would look at historical sales data to find out which products performed best over the past quarter. They would then generate reports or charts to explain these trends. On the other hand, a data scientist might take that same sales data and build a predictive model to forecast future sales trends or customer behavior, using machine learning to make recommendations on what products to focus on next.
Typical career paths
Data science is a broad field, covering everything from data engineering to machine learning, predictive modeling, and artificial intelligence. The beauty of it all is that it offers so many career opportunities, and when professionals decide to specialize—whether it’s in a specific tool or a certain area like AI—it opens up even more options.
Specializing can take you from the generalist role of a data scientist into more focused areas where you can really dive deep and make a bigger impact. For example, the iSchool’s Applied Data Science Master’s Degree provides students with the option of gaining credits through courses on the following tracks:
- Artificial Intelligence
- Big Data
- Data and Business Analytics
- Data Pipelines and Platforms
- Language Analytics
- Project Management
- Visual Analytics
And for those who decide to specialize, there’s the potential to reach leadership positions, such as chief data officer or data strategist, where you can influence the direction of an entire company’s strategy.
The demand for data scientists, especially those with expertise in AI and machine learning, is huge right now. With more and more industries relying on data to make key decisions, this kind of expertise is becoming even more critical.
Jeffrey S. Saltz, an academic leader at Syracuse University, points out that there’s a growing need for professionals who can handle AI, stating, “We decided to launch this new AI program to meet the growing demand from industry, where many companies, across diverse industries, need professionals capable of leveraging Generative AI.” This move shows just how much AI is shaping the future of business and the role data scientists will play in that.
While data analysts typically have a more structured path, with roles often starting in positions like business intelligence analyst or data visualization expert, the opportunities for growth are no less exciting. Over time, these analysts can transition into higher-level roles in business intelligence, data architecture, or even into leadership positions within analytics.
What’s key for data analysts is the ability to not only crunch numbers but to present those findings in a way that’s easily understood, helping leaders make informed decisions. As analysts grow in experience, they can move into positions such as analytics manager or business intelligence director, where they oversee teams and shape the data strategies that guide their organizations forward.
Educational Requirements
Becoming a data scientist requires having a solid background in math, statistics, and computer science. For those just starting out, a bachelor’s degree in data science or other relevant fields like computer science or statistics is often enough to get your foot in the door.
However, if you’re aiming to go into specialized areas like machine learning, AI, or advanced data modeling, pursuing a master’s or even a Ph.D. in data science or a related field is a common next step. Actually, a master’s degree is particularly useful for those hoping to take on leadership roles, handle complex projects, or focus on advanced topics like machine learning.
Syracuse University’s Applied Data Science Master’s Degree is a great option for anyone looking to specialize and refine their skills. Saltz emphasizes the program’s relevance, saying, “The iSchool has been a pioneer in AI and data science education, and we were one of the first universities to offer an Applied Data Science program.”
The iSchool’s programs allow students to apply the theories they learn about to the real world. Gabriel Davila-Campos, a current student, shares his experience in this regard, saying, “In my classes, whether it’s ‘Data in Society’ or ‘Intro to Networks & Cloud,’ I find myself constantly challenged and intrigued. It’s not just about learning the theories; it’s about seeing how these concepts come alive in the real world.”
For data analysts, the entry requirements are a bit more straightforward as many data analysts start with a bachelor’s degree in applied data analytics, business analytics, or computer science. Once again, the iSchool’s Applied Data Analytics Bachelor’s Degree program is an excellent option in this case, providing graduates with the necessary skills and knowledge for this field.
While a master’s degree in data analytics can open up doors to higher positions, it’s not a must for starting out. Certifications in tools like Tableau, SQL, and Power BI can also give professionals a leg up, fast-tracking them to more senior roles.
Job Outlook and Salaries
The job outlook for both data scientists and data analysts is incredibly strong. As more and more businesses realize the value of data-driven decisions, professionals who can analyze and interpret data will continue to be in high demand across different industries.
Regarding salaries, data scientists typically earn more. They have an average salary of around $122,738 per year, reflecting the advanced skills required, including machine learning, AI, and complex predictive modeling expertise.
Data analysts, while still in high demand, tend to earn around $82,640 a year. Although their salary is generally lower than that of data scientists, data analysts still have plenty of room to grow, and their role is crucial for companies looking to understand past trends and make data-backed decisions.
Data Scientist or Data Analyst: Which Should You Choose?
Choosing between two data-focused career paths can feel tricky, especially when both offer strong potential. Data science and data analytics present impressive job opportunities, competitive salaries, and the chance to contribute to meaningful projects.
No matter which path you choose, both fields will equip you with highly sought-after skills that are in demand across many industries. So, the important thing is not to try and decide which one is “better” but rather to determine which best aligns with your personal interests and skill set.
To help you come to a conclusion, take some time to ask yourself these questions:
- Do I love tackling complex problems and coming up with innovative solutions using technology?
- Am I excited by coding and creating models that predict future outcomes?
- Do I want to explore advanced fields like AI and machine learning?
- Am I more interested in analyzing past data to uncover key trends?
- Do I enjoy transforming data into clear, easy-to-understand stories for others?
- Am I drawn to leadership roles where I can guide teams and shape business strategies?
If you found yourself answering “yes” to the first three and being more interested in what was mentioned there, data science could be the right fit for you. Whereas, if the last three resonate more, then data analytics might be the better path.
The Bottom Line
It’s worth reiterating, once again, that whether you choose data science or analytics, you’ll be stepping into a field brimming with growth, innovation, and impact. What truly matters isn’t necessarily which one you choose but how you approach your career—what kind of education you pursue, the skills you hone, and the institution you trust to guide you toward success.
With that in mind, Syracuse University’s iSchool programs are tailored to both data science and data analytics. Our goal is to give you the foundation to thrive on either path and find your way to success.
Frequently Asked Questions (FAQs)
Can a data analyst become a data scientist?
Of course! Data analysts can transition into data scientists, though they would need to gain advanced skills in coding, machine learning, and predictive modeling.
Do data scientists need to know data visualization?
Yes, data visualization is a valuable skill for data scientists to have, particularly when needed to communicate insights.
Does becoming a data analyst require coding?
Yes, coding is often required for data analysts, especially when dealing with tasks like data manipulation and automating processes.