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Ramya Shankar | 08 Aug, 2023

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


It’s easy to see that data is becoming increasingly vital as more companies and organizations push toward data-driven strategies. 

Data professionals are in higher demand than ever before — and, of course, they are currently in short supply. There is a very real data professional talent gap, so companies are willing to pay a premium for knowledgeable individuals. But what does this mean for you?

Pursuing a career in data can be lucrative, especially if you’re highly qualified for the job. Plus, as long as you maintain your performance levels, you likely won’t have to worry about job security.

If you’ve looked through job ads and postings recently, you’ve probably come across a few looking for data analysts or data scientists. Both data scientists and analysts deal with a lot of data and need an analytical mindset, but which role is best suited for you?

On the surface, both seem to be the same. And indeed, you will find similarities to some degree. However, if you look at an in-depth data analyst vs data scientist comparison, you’ll see that there are ways in which one position might fit you more.

If you’re asking, “what is the difference between a data analyst and a data scientist?,” keep reading. In this comparison, we take a closer look at both roles to determine the difference between data analysts and data scientists. Let’s get right to it!

What Does a Data Analyst Do?

A data analyst examines and analyzes the data to answer questions related to business problems. They identify correlations and new metrics, find trends and patterns, apply statistical methods to analyze and mine business data, create reports, and communicate with other teams to get new data to solve problems. 

All this requires good knowledge of spreadsheets like Excel, writing queries to manipulate data, statistical tools to create charts and trends, and know-how of languages like R, Python, and more to write scripts that can automate certain repetitive tasks.

You don’t need any extensive experience to become a data analyst. Here are some of the top data analytics courses to get you started.

What Does a Data Scientist Do?

Data scientists can do all of the above, plus many more things. The scope of the data analyst is limited, whereas data science is vast and has a broader scope. Data science involves more thinking and analysis – not just of data but also of potential business problems and solutions.

As we can see, these tasks need in-depth knowledge of core subjects and industries. Apart from this, a data scientist also needs to know about programming languages like R, Python, and SQL, among others.

Data science is further broken down into several sub-roles – data researcher, data developer, data creative, and data business people. Here’s how to become a data scientist and a few tutorials you can start with.

Suggested Course

Data Analysis Masterclass (4 courses in 1)

Differences Between Data Analyst vs Data Scientist


Let’s discuss the difference between data science and data analytics in detail below.


In all honesty, there are no specific educational requirements to become a data analyst or scientist — at least not yet, anyway. Thus, whether you are self-taught or have a bachelor’s or master’s degree, you have a shot at getting hired as long as you have the necessary knowledge and skills. However, as more people shift into data analytics and data science and the talent gap is filled, some employers may choose candidates with degrees over those without.

That said, education is one way in which data analysts and scientists may differ the most. Consider the table below:

Data Analysts

Data Scientists

Can have no degree at all and be entirely self-taught

Can have a bachelor’s degree in STEM (science, technology, engineering, and math) fields

May have advanced degrees relating to statistics and statistical analysis or analytics

Are likely to have advanced degrees in data science and related fields; commonly have a master’s degree

May pursue additional education to learn skills including:

  • Math and science
  • Databases
  • Programming
  • Predictive analysis
  • Data modeling
  • Data visualization

Are likely to have all of the skills and knowledge data analysts need and may pursue additional education, including:

  • Artificial intelligence
  • Machine learning
  • Data mining
  • Data engineering

As mentioned, you don’t need a degree to become either a data analyst or a data scientist. You can learn all the relevant skills independently, provided you have an analytical mindset or are willing to work to develop one. A degree, although a good investment, isn’t necessary — you may do just as well by completing online courses or a dedicated bootcamp.

Work Experience

If you’re wondering whether it’s possible to become a data analyst with no experience, the answer is yes. It’s also possible to find work as a data scientist without experience. However, the likelihood of getting hired increases if you have a good portfolio to show. Otherwise, it’s recommended to have graduated from a relevant data analysis/data science bootcamp or to have (advanced) degrees in the fields in question.

Although you can get hired without experience, it’s still ideal to build a solid resume with relevant experience by looking for internships or entry-level positions in your chosen field. And, even if you do not have experience as a data analyst or scientist, it’s still recommended to have experience in your chosen industry (i.e., healthcare, tech, education, manufacturing, etc.).


In comparing a data analyst vs a data science professional, we’ve established that data scientists tend to have similar base skill sets to analysts. However, on top of that skill set, data scientists also have more knowledge and capabilities that take them beyond ‘simple’ analytics.

Let’s take a closer look at both positions and see how their skill sets differ in the table below.

Data Analysts

Data Scientists

Have a good understanding of probability and statistics, an eye for numbers, and an analytical mindset

May have knowledge and skills relating to data modeling and data visualization

More focus on statistics and probability

Has a strong foundation in mathematics such as linear algebra, calculus, and probability/statistics (and likely to have advanced knowledge in (predictive) statistics); More focus on computer science and business

Likely has some knowledge or fluency of SQL and programming with R and Python

Generally proficient in programming languages and frameworks such as Python, R, SAS, SQL, Spark, and MATLAB

Can use data analysis and visualization tools such as MS Excel, Tableau, Power BI, and more

Use tools like Excel, SAS, and other business intelligence software

Use tools like MySQL, Hadoop, Spark, and TensorFlow

More focus on data wrangling and examination

Does data wrangling but also does more data modeling

May pursue learning additional skills that can take them into careers as a data scientist

May have additional skills in artificial intelligence, machine learning, and cloud computing

The overlaps between data analysts and data scientists are such that we can say that the vast majority of data scientists can become data analysts — but not necessarily vice versa. That said, many data scientists started as analysts because analysts’ skills are a strong foundation for developing the necessary skills to enter data science.

Roles and Responsibilities

Are data scientists and data analysts the same? If you’ve looked at job postings, it can be challenging to tell one from the other, especially when what companies are looking for may overlap so heavily.

The roles and responsibilities of data analysts and scientists may vary depending on a few factors, such as location, industry, and even company size. For example, some companies may expect a data scientist to wear two hats and perform analyst tasks if they do not have a dedicated data analyst. Similarly, some companies might expect their data analysts to perform tasks more suitable for data scientists.

One of the best things you can do during your job hunt is to look closely at job postings. Examining them carefully can help you determine specific roles and responsibilities required for the position so you can decide whether to apply or not. Let’s take a closer look at some of the most common day-to-day responsibilities of each role:

Data Analysts

Data analysts do things like figure out why or how something happened by looking at data. For example, they may analyze data from a certain period to help them determine why sales may have boomed or dropped during that time. They may also create reports and translate data into an easily understandable format so that laypeople can glean actionable insights from the reports. 

Other examples of their day-to-day responsibilities include:

  • Coordinate with organization/business leaders to help them identify informational needs
  • Acquire data from various primary/secondary sources
  • Clean and reorganize data for later analysis
  • Examine data, spot patterns and trends, and translate them into information with actionable insights
  • Perform different types of data analysis including diagnostic, descriptive, prescriptive, or predictive
  • Create dashboards with business intelligence software to help support KPIs
  • Visualize and present findings to make it easy for laypeople and business leadership to make informed, data-driven decisions

Data Scientists

Data scientists concern themselves more with what may happen. Thus, they use tools such as Spark and various data modeling techniques to help them understand a business’s potential challenges and opportunities. With the tools in their arsenal, data scientists can help give business leadership actionable insights that may guide future data-driven decisions and strategies. Other examples of their day-to-day responsibilities include:

  • Gather, clean, and process raw data — data scientists may spend a significant portion of their working hours scrubbing data
  • Design predictive data models and create AI/machine learning algorithms for mining big data sets
  • Mine data with APIs
  • Build ETL (extract, transform, load) pipelines
  • Develop processes and tools for monitoring and analyzing the accuracy of data
  • Build tools for data visualization, reports, and dashboards
  • Write programs or scripts for automating the collection and processing of data
  • Create techniques like libraries that can help simplify daily data-related processes
  • Perform statistical analysis via machine learning algorithms like NLP (natural language processing), gradient boosting, Random Forest, logistic regression, and more
  • Develop big data infrastructure using a variety of tools

Frequently Used Tools

Data analysts and data scientists have some tools in common, though they also have many tools more suited to their roles. Let’s take a deeper dive into them below.

Analysts and scientists use various tools, including:

  • Business intelligence tools
  • SQL Consoles
  • Statistical analysis tools
  • Programming languages
  • Predictive analytics tools
  • ETL and data modeling tools
  • Automation tools
  • Spreadsheet applications
  • Data visualization tools
  • Data cleansing tools
  • Data science platforms
  • Industry-specific tools and platforms

Some analysts may know how to use tools frequently used by data scientists, and vice versa. You’ll frequently see both roles using tools including but not limited to the ones below:

  • Datapine
  • R-Studio
  • Programming languages like R and Python
  • MySQL Workbench
  • SAS
  • Erwin Data Modeler
  • Jenkins
  • Apache Spark
  • OpenRefine
  • Highcharts
  • MS Excel
  • Tableau

Data scientists also use platforms such as RapidMiner and tools like ggplot2, TensorFlow, Matplotlib, Scikit-learn, MATLAB, Jupyter, and more.

Data Analyst and Data Scientist Salaries: How Much Do They Earn?

Now that we’ve discussed all the differences in detail, let’s take a closer look at data analyst and data scientist salaries.

PayScale shows data analysts earning an average of $63,546 (base salary) each year. This number may fluctuate depending on location and seniority, with some analysts earning over $70,000 on average once they hit the five-year mark. It’s worth mentioning that with more skills and knowledge, you may be able to negotiate a higher salary overall.

Data Scientists have a median yearly salary of $97,694 based on seniority and location. Some professionals can earn well over $130,000 each year. Entry-level data science positions pay an average of $86,000, ramping up to $97,000 by years one to four and $111,000+ by years five to nine.

Data analysts can earn around $46,000 on the low end and over $88,000 on the high end. Data scientists earn approximately $69,000 on the low end and well over $130,000 on the high end.

What Kind of Jobs Can Each Get?

According to, the following positions use data analytics:

  • Data Analyst
  • Business Intelligence Analyst
  • Quantitative Analyst
  • Data Analytics Consultant
  • Operations Analyst
  • Marketing Analyst
  • IT Systems Analyst
  • Project Manager

This article gives examples of data scientist careers, such as:

  • Data Scientist
  • Machine Learning Scientist
  • Machine Learning Engineer
  • Enterprise Architect
  • Applications Architect
  • Infrastructure Architect
  • Data Architect
  • Business Intelligence (BI) Developer

Data Analyst vs Data Scientist: Similarities and Differences in a Nutshell

We’ve discussed the vast majority of similarities and differences between data analysts and data scientists above. Here's a head-to-head comparison to help make it easier for you to remember.

Data Analyst

Data Scientist

Part of the data science lifecycle where a chunk of data is given to the data analyst to give a solution to a specific problem.

Manages the entire business process from defining the problem to making fast and accurate predictions and business decisions.

Explores and analyzes data from a single source (though some analysts may also use secondary sources)

Explores data obtained from various unrelated sources and examines them

Performs day-to-day analyses of data to arrive at trends and patterns

Performs predictions based on various chunks of data obtained over a period of time

Finds answers to the business problems posed by the business team using the problem statement as a reference

Formulates questions and problems relevant to the business and finds solutions using data

Works on SQL, BI tools, or statistical tools to analyze and prepare data

Works on SQL to get the data, cleans it, performs munging (bringing data into correct format) and extracts information from data

Not required to know about statistical models or machine learning and advanced programming levels

Must know about machine learning and creating/training ML models as well as testing them for efficiency

Doesn’t need to transform or create a roadmap with the data

Needs strong data visualization abilities and knowledge to convert data into business stories or use cases

Needs to have in-depth knowledge of SQL and analytics

Must know SQL features but not as deep as a data analyst. Check out the SQL features needed by data scientists here

Requires high skills for decision-making based on statistics and analytical tools.

Requires deep knowledge of machine learning tools like Bayesian, clustering, etc

Knows ETL tools and tools and components of data architecture

Has in-depth knowledge of predictive analysis, correlation, data mining, and statistics

Needs a basic understanding of Hadoop, SQL, Python, Java, etc

Possesses detailed knowledge of Hadoop or Spark-based analytics such as MapReduce, Hive, etc Similarly follow good coding practices for developing scripts in Python, Java, and SQL

Tips for Choosing Between Data Analyst or Data Scientist

Even if you now have a firmer grasp of the similarities and differences between data scientist vs data analyst and can identify the roles and responsibilities of each, you may still need help to choose between the two. The quick tips below might help make choosing between data scientist vs analyst easier.

1. Look at yourself and your background

Although data analysts and data scientists are similar to a degree, they differ in many ways. Two of the primary differences between them are education and work experience.

It’s worth noting that although neither role requires a degree, some companies may feel more inclined to hire those who they feel have the relevant education for the positions. Potential employers may also consider those who have finished relevant courses and bootcamps, even without a degree.

Data analysts tend to seek an education in or have strengths in STEM (science, technology, engineering, and math) fields. As such, analysts may have bachelor’s degrees in related fields. Others may even have advanced degrees relating to analytics. Beyond their educational background, analysts may also pursue knowledge and learn skills in math and science, databases, programming, predictive analysis, and data modeling.

Data scientists need the same analytical mindset as analysts and more. The field of data science demands a much more mathematical and technical background, so professionals likely have more of a background in computer science. Data scientists also need knowledge and skills that can enable them to use tools like artificial intelligence, machine learning, data mining, and more. Thus, data scientists may have (or be working on) an advanced degree relating to data science.

Consider both with regard to your background and what you are willing to learn or improve as you pursue your career. Also look at your strengths and weaknesses, as they can help you decide which option suits you best.

2. What are your interests?

In the battle of data analyst versus data scientist, you’ll also have to consider personal interests. If you’re more excited by numbers, statistics, and finding patterns, data analytics might be for you. However, if you like all of that and also enjoy aspects of business and the field of computer science, you may want to consider data science instead.

What’s important is you choose the field that fits your interests best because doing so can enable you to have a more fulfilling career. And if you haven’t started working on your education and credentials yet, deciding which field fits your interests can make it easier to determine which degrees and courses to go for.

3. What career path and salary do you want in the future?

No data science vs data analyst comparison would be complete without considering career paths and salaries. And as you decide which field to go into, it’s vital to assess what career path you aspire to.

According to this PayScale report, a data analyst can earn an average salary of $63,546, depending on location and seniority. With additional skills and knowledge, you can negotiate a salary increase over time. However, the same report states that those who have reached the senior level may go into data science later. Others may choose to become a developer instead.

Data scientists, on the other hand, are typically more knowledgeable and experienced than analysts. This report shows that data scientists earn an average salary of $97,694 based on seniority and location. Data scientists may choose to become data architects or engineers down the line.

Consider both options to help you decide which field suits you best. You may also want to research more potential career paths each field can take you.


In a nutshell, while a data analyst provides solutions to business problems and creates reports for the same, a data scientist creates business problems and finds the most suitable solution based on the data analysis and other information at each stage. 

Being a data scientist is entirely different and comes with more responsibilities than being a data analyst. Getting insights from data is one thing, but interpreting those insights to arrive at a business solution is totally different. Even if your goal is to be a data analyst, it’s a good idea to understand the whole picture of data science so that you can move up the career ladder anytime you want.

There is no doubt that both data science and data analytics are crucial to organizations and businesses today. No matter which side you choose in the battle of data analyst vs data scientist, you’ll likely find a fulfilling, secure, and lucrative career!

Considering a career as a data analyst? Prepare yourself for interviews with this guide on data analyst interview questions.

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By Ramya Shankar

A cheerful, full of life and vibrant person, I hold a lot of dreams that I want to fulfill on my own. My passion for writing started with small diary entries and travel blogs, after which I have moved on to writing well-researched technical content. I find it fascinating to blend thoughts and research and shape them into something beautiful through my writing.

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