Are you interested in a career working with data but unsure if you should become a data analyst or a data scientist? While both data analytics and data science focus on transforming raw information into actionable business insights, they involve different tools, skills, and career trajectories. This guide breaks down the differences between data analytics and data science, outlines the responsibilities and required skills for each role, and helps you determine which career is the right fit for you.
What is the difference between data analytics and data science?
Data analytics uses statistical and visualization techniques to interpret data and provide actionable business insights, while data science focuses on creating predictive models and algorithms to process and analyze large, often unstructured, datasets.
Data analytics vs. data science: What’s the difference?
Although "data analytics" and "data science" are often used interchangeably, they serve different business needs.
Data analysts examine structured data to identify trends and help business leaders make data-driven decisions. They use tools like SQL, Excel, Tableau, or Power BI for data visualization and interpretation.
Data scientists, in contrast, develop advanced algorithms, predictive models, and frameworks to analyze both structured and unstructured data. They use statistical analysis, machine learning, and programming languages such as Python, R, and Scala.
In summary:
- Data Analysts: Find actionable insights from data using statistics and visualization tools.
- Data Scientists: Build models and predictive algorithms for deeper business intelligence.
What is data analytics?
Data analytics is the process of examining raw datasets to discover trends and solve business problems. There are four main types of data analytics:
- Descriptive analytics: What happened?
(Identifies trends and patterns using current and historical data.) - Diagnostic analytics: Why did it happen?
(Explores causes behind trends.) - Predictive analytics: What could happen in the future?
(Uses AI and machine learning to forecast outcomes.) - Prescriptive analytics: What should we do?
(Recommends solutions based on testing and analysis.)
Data analytics skills
- Proficiency in Excel, SQL, Tableau, and Power BI
- Strong statistics foundation
- Data visualization and storytelling
- Ability to interpret and explain findings to stakeholders
What is data science?
Data science combines mathematics, statistics, programming, and domain expertise to extract knowledge from large volumes of data, often unstructured. Data scientists are responsible for:
- Designing automated data collection processes
- Processing and analyzing complex datasets
- Developing machine learning algorithms
Key data science skills
- Programming in Python, R, or Scala
- Statistical modeling and machine learning
- Data mining and wrangling
- Experience with Big Data tools like Hadoop
Handling unstructured data
Unstructured data (text, social media posts) requires transformation before analysis. Data scientists use classification, categorization, and natural language processing techniques to make this data usable.
Day-in-the-life: Data Analyst vs. Data Scientist
What does a Data Analyst do?
- Design and maintain databases
- Mine and interpret data from various sources
- Create visual reports and dashboards
- Collaborate with stakeholders to drive business improvements
What does a Data Scientist do?
- Liaise with business leaders to define analytical goals
- Develop, test, and implement predictive models
- Clean, process, and integrate data from multiple platforms
- Communicate insights and recommendations to non-technical audiences
Education and work experience: Data analytics vs. data science careers
While we've been concentrating on the differences between working as a data analyst and a data scientist, you might be surprised to see that there is some overlap in education requirements, work experience, and skills.
What do I need to become a data analyst or a data scientist?
| Requirement |
Data analyst |
Data scientist |
|
Education |
Bachelor’s in analytics, IT, or statistics |
Bachelor’s/Master’s in computer science, math |
|
Technical skills |
SQL, Excel, Tableau, Power BI |
Python, R, machine learning, Hadoop |
|
Professional skills |
Data storytelling, analytics, teamwork |
Critical thinking, business acumen, communication |
Good news: You can break into the field from related roles (like marketing, finance, or operations) by building your technical skills with self-paced online courses, bootcamps, and certifications.
Salary comparison: Who earns more?
- Data analyst average salary: $60,000 to $138,000 (source: PayScale, Glassdoor)
- Data scientist average salary: $105,750 to $180,250 (source: Robert Half Technology)
Tip: Data analyst roles can be stepping stones to more advanced careers in data science, engineering, or leadership.
Choosing your career path in data analytics or data science
Consider these questions:
- What experience do you already have?
— Inventory your existing technical and interpersonal skills. - What excites you most?
— Do you enjoy building models (science) or interpreting data (analytics)? - What are your goals?
— Factor in compensation, growth opportunities, and personal fulfillment.
Both careers are in high demand, and your best choice depends on your interests, strengths, and aspirations.
Certification: A smart step forward
Data-focused careers are booming—and whether you pursue data analytics or data science, you’ll find opportunities across nearly every industry. Invest in your skills and future with a professional certification like CompTIA Data+.
Ready to get started in data analytics? Try CompTIA CertMaster Learn + Labs for Data+ and boost your skills and credibility in the job market.