Data Scientist vs Data Analyst: Key Differences and Career Insights

Data Science
Exponent TeamExponent TeamLast updated

Below, we explore the key differences between data scientist and data analyst roles.

Want to become a data scientist or data analyst? This guide contains excerpts from Exponent's complete data science interview course created with data scientists from Spotify, Amazon, and Instacart.

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Key Takeaways

  • Data analysts interpret structured data to provide actionable business insights.
  • Data scientists use advanced techniques like predictive modeling and machine learning to solve complex problems and predict future trends.
  • A bachelor’s degree is typically sufficient for data analysts, whereas data scientists usually require more advanced degrees such as a master’s or PhD.
  • Data scientists typically need specialized skills in programming, machine learning, and big data technologies.
  • Data scientists generally command higher salaries than data analysts, reflecting the complexity and advanced skills required for their roles. Both roles have promising career growth and strong demand in technology and finance sectors.
Learn more about common types of data science roles.
Key differences between data analyst and data scientist jobs.

Data Analyst vs Data Scientist

Data analysts and data scientists both work with data to assist organizations in making better decisions. However, their approaches and responsibilities differ significantly.

Data analysts focus on interpreting structured data to provide actionable insights for business decision-making.

In contrast, data scientists delve deeper, using advanced techniques like predictive modeling and machine learning to foresee future trends and solve complex problems based on existing data.

What does a data analyst do?

Day to day, data analysts are responsible for:

  1. Collecting data from different sources like user behavior or customer records
  2. Cleaning and transforming data to ensure accuracy and consistency
  3. Preprocessing tasks such as standardizing data formats and cleaning corrupt records

Data preparation is a critical step as it lays the foundation for meaningful analysis. Preparing data means ensuring that all of the data you're comparing is in the same format, style, and organizational structure. This makes it easier to discover trends or outliers.

Entry-level data analysts often focus on these basic preprocessing tasks.

Once the data is prepared, data analysts employ statistical analysis techniques to uncover trends and patterns within the data.

They use tools like SQL and Excel to manipulate and analyze data, turning raw data into actionable insights.

Data visualization plays a crucial role here, as analysts create visual representations using tools such as Tableau and Power BI to communicate their findings to stakeholders in the business.

This could be product managers who want to understand user behavior or the finance team who want to understand the costs of running a particular tool.

The primary objective of data analysis is to guide business decisions.

By interpreting data and presenting it in an understandable format, data analysts help organizations make data-driven decisions. Their primary goals are to

  • improve efficiency,
  • better allocate resources like money and people,
  • and increase profitability.

Their work often involves close collaboration with business units like BizOps and Strategy to ensure that the analysis aligns with strategic objectives of the team, product, or business as a whole.

Data analysts need to be able to effectively communicate their findings to other team members.

While discovering data insights is important, helping stakeholders understand what they're looking at is key.

This combination of technical expertise and communication prowess makes data analysts invaluable to many businesses, especially in tech and finance.

What does a data scientist do?

Data scientists adopt a comprehensive approach to data.

Data scientists work with structured and unstructured data to address unique and specific problems and forecast future trends.

For example, a data scientist can help a business understand why users behave a particular way or what specific actions they're likely to take next.

Data scientists are experienced in advanced statistical methods, machine learning, and predictive analytics, which they use to extract insights from vast amounts of unstructured data.

This often involves developing algorithms to automate the collection of data and creating sophisticated models that can forecast future outcomes.

The role of a data scientist is multifaceted. Data scientists:

  • Collect and manage data from multiple sources
  • Perform advanced data analysis
  • Develop algorithms and models to predict future outcomes
  • Create complex visualizations to communicate their findings

Data preparation, a crucial aspect of data management, involves sourcing, processing, and data modeling to ensure it is ready for analysis.

An important element of data science involves incorporating data-driven decision making into products and services.

Most often, data scientists work closely with other teams to build predictive analytics into a business.

This makes it easier for the business to:

  • Anticipate and respond to market dynamics
  • Make informed decisions
  • Identify trends and patterns
  • Optimize operations
  • Improve customer experiences

This proactive approach can lead to significant business growth and innovation.

Just like analysts, data scientists also need strong communication skills to explain their complex models and predictions to non-technical stakeholders.

This requires not only technical expertise but also the ability to translate data insights into actionable business strategies.

Their work is crucial in driving business growth through data-driven innovation and strategic planning.

A Venn diagram of data science, data analysis, and data engineering skills.
Data science overlaps with other roles like data analysis and data engineering.


Both data scientists and data analysts need a strong educational background to succeed in their roles.

  • For data analysts, a bachelor’s degree is typically sufficient.
  • For data scientists, more advanced degrees, such as a master’s or PhD, are often required.
  • Professional certificates in data analysis or data science can also be beneficial.
  • Self-learning through online courses and resources can help you acquire the necessary skills for these careers, but are best accompanied by a formal education.

Data Analyst Education Requirements

Data analysts generally hold a bachelor’s degree in fields such as:

  • mathematics
  • statistics
  • computer science
  • finance

These degree programs provide the foundational knowledge required to handle data, perform statistical analysis, and create data visualizations.

During various stages of data science interviews, you'll likely be asked about your knowledge in these fields regardless of your educational background.

Professional certificate programs like Google’s Data Analytics Professional Certificate can also help to prepare aspiring data analysts for entry-level roles.

Certification programs in data analytics offer a practical and accelerated pathway into the profession.

Gaining hands-on experience through internships or entry-level positions can also be invaluable.

Data analysis internships let you:

  • Apply theoretical knowledge in real-world scenarios
  • Develop technical skills in SQL and Tableau
  • Build a portfolio of work with tangible business outcomes.

Data Scientist Education Requirements

In contrast, data scientists typically require more advanced education.

A master’s or doctoral degree in one of these fields is often required to land a job as a data scientist:

  • data science,
  • computer science,
  • mathematics,
  • or statistics.

These programs help build deep technical knowledge and expertise needed to handle complex data analysis and machine learning tasks.

Data scientists can also benefit from specialized bootcamps and online courses that focus on specific skills like machine learning, big data technologies, and advanced statistical methods.

These programs can provide practical, hands-on experience and help bridge the gap between academic knowledge and industry requirements.

Data science internships are much more coveted, as there's a large potential for career growth or joining a company full-time.

Essential Skills and Tools

While there is some overlap, such as in data handling, analysis fundamentals, and data visualization, data scientists often require more advanced skills in machine learning and programming languages.

Key Skills for Data Analysts

Both technical proficiency and effective communication are essential for data analysts in their roles.

Some key technical skills for data analysts include:

  • Proficiency in statistical analysis
  • Data manipulation
  • Routine statistical analyses
  • Using tools like SQL and Excel for data querying and manipulation

Creating clear and impactful visualizations using tools such as Tableau and Power BI helps them communicate their findings effectively to non-technical stakeholders.

These visualizations can transform complex data sets into understandable and actionable insights, aiding in data-driven decision-making.

Additionally, data analysts need to explain their data insights clearly and concisely to ensure that stakeholders can make informed decisions based on their analysis.

Key Skills for Data Scientists

Data scientists, on the other hand, require a more advanced skill set. They need to be proficient in machine learning and predictive modeling, using techniques like these to predict future trends:

Familiarity with coding languages like Python, R, and SQL is also essential. These languages are commonly used for data analysis and visualization.

In addition to traditional programming and machine learning coding skills, data scientists must have a strong understanding of:

  • Big data technologies like Apache Hadoop and Spark. These technologies are crucial for handling and processing large datasets efficiently.
  • Developing data models
  • Creating algorithms

Common Tools Used

Both data analysts and data scientists rely on a variety of tools to perform their tasks effectively.

Data visualization tools such as Tableau and Power BI help transform complex data sets into understandable visual formats.

Additionally, cloud computing platforms like AWS and Azure provide the infrastructure needed for data storage, processing, and analysis.

Roles and Responsibilities

Data analysts and data scientists play pivotal roles in enhancing business operations, allocating resources, and making decisions.

Their ability to turn data into actionable insights empowers organizations to increase productivity, reduce waste, and drive business growth.

Data Analyst Responsibilities

Data analysts are responsible for:

  • Identifying and acquiring the necessary data to make decisions from
  • Ensuring data accuracy through cleaning and reorganization processes
  • Performing statistical analyses
  • Creating visual representations of their findings

By analyzing data from various sources such as customer feedback and sales reports, they can:

  • Identify productive times and conditions for operations
  • Determine the most effective allocation of resources
  • Enhance overall efficiency and productivity

Data analysts frequently collaborate with business units to ensure their analysis aligns with organizational objectives.

They communicate their findings through dashboards or applications, making data accessible to decision-makers at all levels.

Data Scientist Responsibilities

Data scientists focus on:

  • Uncovering patterns
  • Using predictive analytics to forecast future trends and identify potential operational bottlenecks
  • Developing machine learning models to anticipate customer behaviors and market dynamics

The key responsibilities of data scientists include:

  • Develop tools and processes that ensure data accuracy and reliability
  • Create sophisticated algorithms and models that automate complex processes, driving efficiency and innovation within the organization
  • Integrate machine learning models into various business operations to optimize resources and predict critical events, such as emergency readmissions in healthcare.

Data scientists also contribute significantly to business growth by:

  • Embedding data-driven decision-making into products and services
  • Analyzing large datasets and deriving actionable insights
  • Helping businesses innovate and develop new strategies for competitive advantage

Job Descriptions

Here are some sample job descriptions for data analyst and data scientist positions at Meta.

Data Analyst Sample Job Description

A job posting for a data analyst at WhatsApp lists the following day-to-day responsibilities:

  • Work with stakeholders in Growth, Marketing, and Product to understand data and make decisions
  • Develop standardized dashboards and ad hoc reports to inform decision making
  • Validate data and ensure that we make accurate decisions
  • Design ETLs and data warehouses for efficiency and re-usability
  • Oversee the design and structure of all intermediate computations
  • Develop and oversee standard methods for combining multiple data streams, even when the data is incomplete or two streams conflict
  • Peer review the designs and work of other analysts
  • Mentor junior analysts
  • Create a culture of best practices and quality

Data Scientist Job Description

A job posting for a data scientist at Instagram lists the following day-to-day responsibilities:

  • Apply your expertise in quantitative analysis, data mining, and data visualization to see beyond the numbers, understand how our users interact and engage with Instagram, and draw actionable insights
  • Partner closely with product, engineering, and marketing teams to solve problems and identify trends and opportunities
  • Lead the definition of product and business success metrics, collaborate with working teams to ensure correct data tracking instrumentation for measurement
  • Build reports and dashboards to monitor KPIs (Key Performance Indicators), understand drivers of KPI changes
  • Help set up experiments, analyze, and interpret results
  • Effectively communicate insights and recommendations to stakeholders
  • Design and implement predictive models to improve engagement, conversion, and retention
  • Lead ongoing decisions concerning data collection, test/study design, and data analyses

How to Get a Job

Preparing for a Career in Data

If becoming a data analyst sounds like the right fit:

  • Choose a STEM major in computer science, mathematics, or a related field.
  • Master Excel; your coworkers will appreciate your expertise.
  • Learn a programming language. Python, SQL, and R are excellent choices.
  • Gain hands-on experience. Join a Meetup, volunteer with an organization like DataKind, or contribute to open-source projects.

If you prefer the role of a data scientist:

  1. Start with the steps listed for data analysts. A strong foundation in analysis is essential.
  2. Consider pursuing an advanced degree in statistics or computer science.
  3. Build a portfolio to showcase your skills. Contribute to open-source projects and tidy up your GitHub Pages. Include diverse projects, such as cleaning data from, performing exploratory analysis on a Kaggle dataset, or completing a machine learning project.
  4. Gain practical experience by working on real-world data science problems from companies like Google, Facebook, and Amazon.
  5. Practice summarizing insights for a non-technical audience. Explain your projects to friends and family to hone your communication skills.

Strengthening Your Application

You’re now a Python expert and can explain the basics of predictive modeling. You’re ready to apply for your dream job, but many candidates have similar qualifications. How can you stand out?

Interview Preparation

First, assemble your materials. Tailor your portfolio to the company and job description.

Ensure your GitHub profile or blog features a few relevant projects.

Your code should be visible and well-documented. Include a README file that explains the setup and summarizes the project.

Tips for a Standout Portfolio:

  • Include at least one data cleaning project and one data storytelling project. These reflect common tasks in the role.
  • Spend time on visuals. Any graphical representation of your work must clearly convey the right story.
  • Choose topics you’re interested in. Your work will be better, as you’ll dig deeper, find more interesting connections, and enjoy summarizing.

Technical Interview Practice

Once you have a strong portfolio, the best way to prepare for a technical interview is to practice.

Check out Exponent's data science interview prep course, as well as our daily practice mock interviews to get real-world practice for data interviews.

Additionally, refresh your programming knowledge in our software engineering interview prep course.

Extra Tips:

  • If you’ve never had a technical interview, ease into problem-solving under pressure by entering a group challenge. Kaggle competitions are ideal; you can choose from low-pressure knowledge challenges to high-stakes prizes. Meetups and forums are great places to meet potential teammates.
  • Simulate real-world conditions. Wear your interview outfit, set a timer, and work on problems in front of an audience.
  • If you have friends in data, ask them to critique you. You can’t prepare for every possible question, but practicing speaking on your feet will be invaluable.

Salary Comparison

The salaries for data professionals fluctuate based on their roles.

Data scientists typically earn more than data analysts due to their advanced technical skills and the complexity of their tasks.

This salary information was collected from, a salary bench-marking tool.

Data Analyst Salary Range

In the United States, data analysts typically earn between $70,000 and $80,000 per year.

Entry-level positions usually start at approximately $60,000 annually. Experience, industry, and geographic location can significantly influence compensation.

For instance, data analysts working in finance or tech may command higher salaries.

Data Scientist Salary Range

In the United States, data scientists typically start with salaries around $90,000 and can earn up to $130,000 or more annually, which is higher than many other professions.

In competitive industries like technology and finance, experienced data scientists can earn salaries exceeding $165,000 per year.

The median annual wage for data scientists is over $100,000, reflecting the high demand and specialized skills required for this role.

Career Paths

Both data analysts and data scientists have bright career prospects.

Data analysts can transition to data scientist roles by acquiring additional skills and education, while both roles can lead to leadership positions overseeing data strategy and operations.

Career Path for Data Analysts

Data analysts typically begin their careers in entry-level positions and advance to more senior roles, such as junior data analyst or business intelligence positions.

As they gain experience, they can move into senior data analyst positions, where they take on more strategic responsibilities, such as translating business challenges into data problems and building measurement plans.

With further experience and expertise, data analysts can advance to:

  • Data analytics manager roles, overseeing the strategic direction and operations of analytics teams
  • Database developer roles by building on their technical skills in languages like Python and relational databases
  • Data scientist roles by acquiring advanced skills in statistics, machine learning, and programming.

Career Path for Data Scientists

Data scientists often start their careers through self-teaching, online courses, and personal projects, gaining foundational knowledge and practical experience.

They may begin as research assistants or junior data scientists, gradually moving up to more specialized roles focusing on big data, machine learning, and AI.

As they gain experience, data scientists can transition into senior roles, leading complex projects and mentoring junior team members.

They may also specialize in areas like big data engineering or machine learning.

Industry Outlook

As organizations continue to recognize the value of data in optimizing operations and driving growth, the demand for skilled data professionals is expected to rise.

Job Outlook for Data Analysts

The growing reliance on data drives emerging opportunities for data analysts across various sectors, including IT services and consulting, which account for a significant portion of job offers.

Moreover, the positive job outlook for data analysts is supported by the projected growth in the data analytics market.

As the market expands, the need for skilled data analysts will continue to rise, providing a stable and promising career path for professionals in this field.

Job Outlook for Data Scientists

The job outlook for data scientists is promising, with a high demand for sophisticated data analysis capabilities, predictive modeling, and AI solutions.

The integration of AI in the workplace has heightened the demand for data scientists, particularly those specializing in emerging areas like deep learning.

Employment of data scientists is projected to grow significantly in the coming years, driven by the increasing volume of data collected by organizations.

The tech and finance industries, in particular, offer numerous opportunities for data scientists.


Both data analysts and data scientists play crucial roles in today’s data-driven world.

While data analysts focus on interpreting structured data to provide actionable insights, data scientists use advanced techniques to predict future trends and solve complex problems.

Whether you choose to pursue a career in data analysis or data science, both fields offer exciting opportunities for growth and innovation.


What is the difference between a data scientist and a data analyst?

The main difference between a data scientist and a data analyst is that data analysts focus on sifting through existing data for patterns and solving business problems. Data scientists interpret trends in the data with predictive models and machine learning to try to understand what will happen next.

What are the qualifications and job responsibilities of a data analyst?

The qualifications for a data analyst include proficiency in SQL/R/Python, project management and agile development experience, advanced skills in Excel, data visualization, and intellectual curiosity.

Their job responsibilities involve working with stakeholders, developing dashboards and reports, validating data, designing ETLs, and mentoring junior analysts.

What are the main skills required for a data scientist?

The main skills required for a data scientist include advanced statistical analysis, machine learning and predictive modeling, programming languages like Python and R, big data technologies such as Apache Hadoop and Spark, artificial intelligence, innovation, problem-solving, and communication.

What is the salary range for a data analyst in the United States?

The salary range for a data analyst in the United States is approximately $60,000 to $80,000 per year, based on factors such as experience, industry, and location.

What is the salary range for a data scientist in the United States?

The salary range for a data scientist in the United States is commonly between $90,000 and $130,000, but can exceed $165,000 with experience and in competitive industries.

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