Data Scientist vs Data Analyst: Key Differences Explained

by | Careers

In the world of data-driven decisions, two prominent roles have emerged: data analysts and data scientists. These professionals play a crucial role in helping organizations harness the power of data, but their responsibilities and skill sets are quite different.

Data analysts focus on using data visualization and statistical analysis to understand data and identify patterns. They are usually required to have at least a bachelor’s degree in a relevant field like mathematics, statistics, computer science, or finance.

Data scientists, on the other hand, work on creating frameworks and algorithms to collect and process data, often requiring advanced knowledge in areas such as machine learning and artificial intelligence. They typically hold advanced degrees or possess more specialized skills in comparison to data analysts.

Broadly speaking, both professions involve extracting valuable insights from data; however, their approaches and skill sets do vary.

In this article, we will explore the differences between data scientists and data analysts and highlight the unique skills and responsibilities required for each role.

Let’s dive in.

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

Data scientist vs data analyst.

While data scientists and data analysts both work with data, they have distinct roles and responsibilities.

Understanding the differences between these two roles is important for organizations seeking to build an effective data team. Also, it is crucial for those that would like a career in data to understand.

In this section, we will explore the key differences between data scientists and data analysts, including their educational backgrounds, technical skills, and the types of problems they are typically tasked with solving.

The table below gives a quick overview of the differences between the two roles:

Education/BackgroundData ScientistData Analyst
DegreeBachelor’s degree in business, economics, statistics, or a related fieldBachelor’s degree in business, economics, statistics, or related field
Programming skillsProficient in languages such as Python, R, and SQLProficient in Excel, SQL, and basic scripting languages
Mathematics skillsStrong mathematical skills, including linear algebra, calculus, and statisticsStrong statistical skills, including regression analysis and hypothesis testing
Work experienceExperience with big data technologies, machine learning, and data visualizationExperience with statistical analysis, data modeling, and reporting

Education and Background

A data analyst with a degree.

Data analysts: Acquiring an entry-level data analyst job typically requires a bachelor’s degree in fields such as statistics, mathematics, economics, or computer science. However, it’s not uncommon for analysts to have a background in business or a related field.

Many data analysts start their careers as data entry or data management specialists, where they gain experience working with databases and basic data software.

Data scientists: Since data scientists perform more complex tasks than data analysts, the job requires more advanced degrees, such as a master’s or a Ph.D. in fields like statistics, computer science, or data science.

Joining a boot camp, using tutorials, or completing online courses or certificate programs may not cut it.

Data scientists should have a strong foundation in mathematics, statistics, and computer science, as well as hands-on experience with programming languages such as Python, R, and SQL.

Many have a background in fields such as physics, engineering, or neuroscience, as well as machine learning and artificial intelligence.

Now, let’s get technical.

Technical Skills Compared for Data Analysts vs Data Scientists

A data scientist woman performing tasks on a laptop.

Data analysts: Technical skills needed to be successful in a data analyst job include data analysis, data visualization using graphs, and tools like Microsoft Excel. You need to be proficient in statistical analysis, data modeling, and reporting using tools like Tableau.

Other data analyst skills include working with databases and having basic scripting language skills.

Good communication skills are also important for data analysts, as they often work with stakeholders across different departments to provide insights and recommendations.

What about data scientists?

Data scientists: The role requires advanced technical skills in areas such as machine learning, deep learning, natural language processing, and big data technologies. You will need to be proficient in programming languages such as Python and R too.

The job involves working on large data sets, developing predictive models, and extracting insights from data. Like data analysts, it also requires soft skills like communication and collaboration since you often need to work with different teams.

Roles and Responsibilities Compared

To-do list page for data analysts.

Data analysts: Very simply, a data analyst’s job involves analyzing and interpreting data to provide insights and recommendations to stakeholders.

You may be tasked with working with different data sources to identify trends and patterns that can inform business decisions.

Some specific responsibilities of data analysts can include:

  • Collecting, cleaning, and organizing data from various sources
  • Conducting statistical analysis to identify trends and patterns in data using software like Tableau
  • Creating reports and dashboards to visualize data and communicate insights to stakeholders
  • Identifying areas for process improvement and making data-driven recommendations to stakeholders
  • Developing and maintaining databases and data systems to support data analysis
  • Keeping up-to-date with the latest trends and developments in data analysis and visualization.

Now, things get a little more complex.

Data scientists: Being a data scientist involves analyzing complex data sets, developing predictive models, and extracting insights from data.

They work closely with stakeholders across different departments to provide insights and recommendations based on their data analysis.

Some specific responsibilities of data scientists include:

  • Conducting exploratory data analysis to identify patterns and trends in data
  • Developing predictive models using statistical and machine learning techniques
  • Building and testing machine learning models to improve predictive accuracy
  • Using problem-solving skills and business intelligence to come up with data-driven solutions to business problems
  • Communicating complex findings and recommendations to non-technical stakeholders
  • Collaborating with data engineers and software developers to build and deploy data-driven solutions

In the next two sections, we’ll take a look at the future job prospects and salary expectations for the two professions.

Career Path: Data Scientist

A female data scientist standing next to server racks.

Job Prospects

The job outlook for data scientists in 2023 is very promising as organizations across industries continue to collect and analyze increasing amounts of data.

According to the U.S. Bureau of Labor Statistics (BLS), employment of data scientists is projected to grow by 36% from 2021 to 2031, which is much faster than the average when compared to other occupations. Job opportunities in the field are driven by the increasing use of data and analytics to drive decision-making in organizations of all sizes.

Salary and Compensation

Data scientists are generally more highly-compensated compared to data analysts, reflecting the advanced skills and expertise required to work with large datasets and handle raw data.

According to Glassdoor, the national average salary for data scientists in the United States is around $103,000 per year. Many organizations also offer various additional forms of compensation for data scientists, such as bonuses, equity, and other benefits like medical insurance and paid time off.

Please note that compensation can vary widely depending on location, industry, and years of experience.

Career Path: Data Analyst

A group of data analysts reviewing charts and graphs.

Job Prospects

According to the BLS, employment of management analysts (which includes data analyst careers) is projected to grow by 11% from 2020 to 2030. Like data scientists, the job outlook for data analysts is very positive for the foreseeable future.

Salary and Compensation

Compensation for data analysts may vary based on factors such as experience, industry, and location. Entry-level data analysts typically earn lower salaries, they can expect their pay to increase as their skills and expertise develop over time.

In terms of salary, the national average for data analyst positions in the United States is around $65,850 per year, according to Glassdoor.

The job prospects and compensation for both data scientists and data analysts are very promising, but how can you decide which career is right for you? We’re going to take a look at factors to consider in the next section.

Choosing the Right Path for You

A forest pathway splitting into two roads.

Deciding which career path is right for you can feel daunting, but think of it as an exciting opportunity to explore this wonderful world of data!

The two fields may seem similar at first glance, and in a way, they are, but they require different skill sets and offer unique career paths.

With the right information and guidance, you can choose the path that is best suited for your skills, interests, and career goals.

In this section, we’ll provide some tips and insights to help you navigate this decision and choose the right path for you.

Factors to Consider

When considering a career in data science or data analysis, it’s important to think about your skills, interests, and career goals.

Here are some specific factors to consider:

  1. Technical skills: Data scientists typically require more advanced technical skills, such as programming, machine learning, and big data technologies. If you enjoy working with raw data and understand the fundamentals of programming languages such as Python and R, a career as a data scientist may be a good fit. Data analysts, on the other hand, may focus more on statistical analysis, data visualization, and Excel skills.
  2. Education and background: Data scientists often have advanced degrees in fields such as computer science, statistics, or mathematics. Data analysts may have degrees in fields such as business, economics, or statistics but can also come from a wide variety of backgrounds with no prior experience.
  3. Roles and responsibilities: Data scientists are often responsible for more strategic and complex initiatives, such as developing predictive models or creating machine learning algorithms. Data analyst roles focus more on day-to-day operations and providing insights to stakeholders.
  4. Job outlook and salary: Both data scientists and data analysts have strong job prospects and competitive salaries, but the specific job outlook and salary can vary depending on the industry, location, and years of experience.

It’s a good idea to seek advice from mentors or network with professionals in each field to gain a better understanding of specific roles.

Ultimately, the right path for you will come down to your individual goals and aspirations.

Now one great thing about data skills is that they can be applied in most industries, let check them out.

What Industries Need Skilled Data Professionals?

Two data scientists in hard hats looking at papers.

The field of data science and data analytics is in high demand across a wide range of industries and company types.

Here are some examples of industries that both commonly employ data scientists and data analysts:

  • Finance and Banking: The finance and banking industry relies heavily on data analytics to identify trends, assess risk, and make informed business decisions. Business analysts are in high demand.
  • Healthcare: Healthcare organizations use data science and data analytics to improve patient outcomes, manage resources, and drive innovation in medical research.
  • E-commerce: E-commerce companies use data analytics to better understand their customer’s behavior, preferences, and purchasing habits in order to improve marketing and sales strategies.
  • Marketing and Advertising: Marketing and advertising firms use data analytics to measure the effectiveness of their campaigns, identify target audiences, and optimize marketing strategies. There are a lot of job postings in this field, and hiring managers are always on the lookout for fresh talent.
  • Technology: Technology companies use data science and data analytics to develop new products and services, improve user experiences, come up with real-world solutions, and identify areas for innovation and growth.

Data science and data analytics are critical functions in a wide range of industries that interpret vast amounts of data in order to drive business decisions, improve customer experiences, and gain a competitive advantage.

There are employment opportunities across different company types, including startups, large corporations, consulting firms, and government agencies.

Understanding the diverse range of industries and company types that rely on data professionals is crucial for individuals looking to build successful careers in these fields.

It’s also important to note that both fields are evolving, and there are emerging trends that are worth considering.

Emerging Trends – The Future of Data Jobs

Tablet showing data trends.

In addition to industry types, consider emerging trends in data science and data analytics that are changing the landscape of the two fields.

Here are some current trends that are shaping the future of data science and data analytics:

  1. Artificial intelligence and machine learning: AI and machine learning are increasingly being used in data science and data analytics to automate data processing, identify patterns, and make predictions. These technologies have the potential to revolutionize industries from healthcare to finance to marketing.
  2. Cloud computing: Cloud computing has made it easier and more cost-effective to store, manage, and analyze large amounts of data. As cloud infrastructure and technology continue to improve, it’s expected that cloud-based data analytics and machine learning will become more widespread.
  3. Data ethics and privacy: As more and more data is collected and analyzed, concerns about data ethics and privacy have come to the forefront. Data scientists and analysts are being called upon to ensure that data is being used ethically and responsibly and to implement measures to protect sensitive data.
  4. Internet of things (IoT): The IoT refers to the network of interconnected devices and sensors that collect and share data. With the increasing adoption of IoT technology, there is a growing need for data scientists and analysts who can manage and analyze the vast amounts of data generated by these devices.
  5. Natural language processing (NLP): NLP is a branch of AI that focuses on the interaction between humans and computers using natural language. As NLP technology advances, it’s expected that it will have a significant impact on data analytics, particularly in areas such as sentiment analysis and chatbot development.

By staying up-to-date with emerging trends in data science and data analytics, professionals in these fields can better position themselves for success and stay ahead of the curve in this fast-paced and constantly evolving industry.

Make sure you check out our LinkedIn, to stay up-to-date with the latest in data trends.

The Bottom Line

In the world of data, both data scientists and data analysts play important full-time roles in a business. While there are similarities between the two, they possess distinct differences in terms of responsibilities and required skills.

Data analysts primarily focus on working with structured data to solve tangible business problems using SQL, R, or Python programming languages, data visualization tools, and statistical analysis. They help organizations identify trends and derive insights from data.

On the other hand, data scientists are more involved in programming machines, optimizing systems, and creating frameworks and algorithms for collecting usable data. Their primary duties lie in collecting data and designing robust data-driven solutions.

While both job descriptions work within the realm of big data, identifying the right path depends on your interests, skills, and career goals. Whichever path you choose, both data scientists and data analysts are in-demand careers, making them an exciting and rewarding choices for those interested in working with data.

To learn more about one of our favorite data analysis tools, check out our video below.

Best of luck with your job hunt!

Still unsure about a career in data, test the water and start learning for free here.

author avatar
Sam McKay, CFA
Sam is Enterprise DNA's CEO & Founder. He helps individuals and organizations develop data driven cultures and create enterprise value by delivering business intelligence training and education.

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