Data Analyst vs Data Scientist: Skills, Education, Job Requirements

Data science is an interdisciplinary field that encompasses various roles, including (among others) data scientist and data analyst. With the skills and functions of those in data-related positions often overlapping, drawing distinct lines between them can pose a challenge.

This article outlines the data analyst vs data scientist differences and defines the required skills, education, and responsibilities for each role. The information is from our recent study examining data science and analytics job offers.

Roles and Responsibilities
Research Methodology
Skills
Education
Salary
Career Path
Key Differences

Data Analyst vs Data Scientist: Roles and Responsibilities

The difference between data analyst and data scientist positions isn’t clear-cut because of the overlap between data-related roles. Hiring managers’ different interpretations of the functions and requirements also contribute to the confusion.

The following sections examine each job’s required skills and education from the employer’s perspective (i.e., as advertised in job ads). The data scientist vs data analyst comparison highlights the overlap and differences between the two roles.

But first, let’s define each position and the specialists’ primary responsibilities.

What Is a Data Analyst?

The data analyst collects, cleans, explores, and analyzes data to answer specific questions and derive meaningful insights. They spend significant time creating visualizations and reports to present their findings to business owners and stakeholders meaningfully and concisely.

Data analysts’ primary goal is to assist management’s decision-making by discovering past trends and unveiling the reasons behind them. They typically achieve this with backward-looking statistical analyses (i.e., data analysis). But some advanced roles may involve the usage of predictive analytics (forward-looking—i.e., data analytics).

Although predictive analytics is typically associated with data scientist responsibilities, the line between the data scientist vs data analyst field can become blurred. The difference between analysis and analytics isn’t clear-cut either, especially in job ads. So, when discussing the data analyst role, the current article refers to specialists familiar with both data analysis and analytics.

What Is a Data Scientist?

The data scientist uses techniques from various disciplines, including statistics, computer science, machine learning, and AI, to extract valuable insights from data and create business value. Another key function is the ability to translate business goals into a data strategy and data into business insights.

The data scientists’ primary goal is to inform decision-making by discovering past trends and making data-based predictions. Depending on the problem’s complexity, they may use more straightforward data analysis solutions or build complex machine learning, deep learning, or AI models.

Although both data science and analytics professionals operate in the data realm, data scientists use more advanced techniques. The data scientist is well-versed in data analysis, but the data analyst can only perform some data science functions.

And while their role and responsibilities overlap to a great extent, the emphasis is on different functions and capabilities. To better understand where one role ends, and the other begins, let’s examine the data analytics vs data science skills and education.

Research Methodology

We studied over 2,000 data science vs data analytics LinkedIn job offers to uncover the most sought-after skills and education for each position.

Our initial search for data analytics jobs generated 1,071 results. After excluding irrelevant results—such as business analyst or data engineering positions—the sample size was reduced to 996. The following findings regarding the data analyst skills and education are based on the remaining 996 LinkedIn offers extracted in December 2022. More insights are available in The Data Analyst Job Outlook article.

Our initial search for data scientist jobs generated over 1,000 results, which we narrowed to 806 after excluding the irrelevant results. The following insights regarding the data scientist skills and education are based on this sample of LinkedIn job offers extracted in December 2022. You can find other valuable findings from our research in The Data Scientist Job Outlook article.

Data Analyst vs Data Scientist: Skills

Employers look for data analysis, visualization, and programming skills in both data analyst and data scientist candidates but prioritize them differently.

While Python is a must for data scientists, it appears in only around a third of data analytics job offers. In contrast, SQL is equally important for both jobs. Employers consider Excel important enough to mention it in just over half of the data analytics job ads and 26% of the data science ads. Tableau and Power BI are more critical requirements for data analysts than for data scientists.

Although there is considerable overlap, some requirements are unique.

PowerPoint and Word are commonly mentioned in 12% and 8% of data analyst job offers, respectively.

Meanwhile, data science positions might require advanced techniques like deep learning (11%), clustering (11%), predictive modeling (8%), neural networks (5%), natural language processing (5%), scikit-learn (4%), and decision trees (3%).

Data Analyst vs Data Scientist: Education

Data scientists and analysts come from similar backgrounds, but employers’ preferences differ slightly for each role. The following graph shows the percentage of job offers in which each degree is mentioned. (Most employers accept several disciplines.)

Around a third of data scientist job offers mention data science degrees, while a data analyst major is not on employers’ lists of preferred education. Computer science, statistics, engineering, and mathematics are almost equally desired disciplines for data scientists and analysts. Although less frequently, economics, architecture, and data engineering degrees also appear among the education requirements for both positions.

And while a degree in one of these fields will give you an advantage when applying for jobs, a comprehensive online education program can also help you acquire the skills to become a data analyst or data scientist. Our Data Analyst and Data Scientist Career Tracks cover all essential topics and teach you the necessary skills for each role.

Data Analyst vs Data Scientist: Salary

Another difference between a data scientist and a data analyst is the remuneration.

The median pay for data analysts is $80,093/year; for data scientists, it’s $152,134/year. Of course, salaries vary significantly depending on the industry, company, location, employee experience, seniority level, and negotiation skills.

But the difference in pay persists with more senior positions. The following are data scientists’ salary reports from Glassdoor:

Senior data analyst: $120,641/year
Senior data scientist: $201,440/year
Principal data analyst: $164,758/year
Principal data scientist: $228,596/year
Chief data analyst: $192,637/year
Chief data scientist: $269,660/year

The data scientist role is the more lucrative career path, but how easy is it to climb up the ladder?

Data Analyst vs Data Scientist: Career Path

Our research showed that the number of entry-level data analyst positions approximates that of the mid-senior positions (391 vs 327). This difference is much more significant for data scientist job offers (444 entry-level vs 246 mid-senior positions).

The categories ‘Others’ encompass one director and one executive position for data analysts, and one internship, executive, and full-time (unspecified level) offer for data scientists.

This suggests that the data scientist career path is steeper, making it more challenging to progress. Becoming a data analyst requires a lot of hard work, but it is an easier choice regarding career progression.

In addition, data analytics is often used as a stepping stone to a data science role. And since it involves a lot of the same skills and education, the transition is relatively easy.

How to Become a Data Analyst

Does the data analyst career path sound like the right choice for you?

Start by acquiring fundamental theoretical knowledge in statistics and mathematics. Continue your education with SQL, Excel, and PowerPoint, and master the necessary programming languages and data visualization tools.

Our Data Analyst Career Track combines all relevant topics in one comprehensive training. Learn everything from the fundamentals to advanced techniques and obtain a certificate of achievement to prove your competence.

Download our Data Analyst Career Guide for more valuable resources and information for the job application process, including:

Role description and requirements
Common data analyst interview questions and answers
Cover letter templates
Resume guidelines and templates
The data analyst job outlook for 2023

How to Become a Data Scientist

Do you wish to build a career as a data scientist?

Obtain the necessary probability, statistics, and linear algebra foundations and build solid programming skills. Continue your training with advanced topics like machine learning and deep learning. Finally, supplement your technical knowledge with the soft skills, business acumen, and strategic thinking necessary to derive meaningful insights from data and bring business value.

Our Data Scientist Career Track provides a structured learning path containing the essential skills and knowledge. Complete your training and earn a recognized certificate of achievement to prove your capabilities.

For further support with the job application process, refer to our Ultimate Data Science Career Guide. It contains everything you need to launch a successful career in data science.

Data Analyst vs Data Scientist: Key Differences

A data analyst collects and analyzes historical data to provide valuable insights that support business decision-making. In addition, a data scientist uses advanced machine learning and predictive modeling techniques to forecast events, providing strategic insights.

As such, the data scientist job is more lucrative but has steeper learning and career advancement paths. In addition to being an excellent career choice, data analytics is often used as a stepping stone to a data science role.

If you’re still unsure what each field of work entails, our Introduction to Data and Data Science course will help you understand the various data-related roles and functions.