What Are the Differences Between a Data Analyst and a Data Scientist

Many people believe that a Data Analyst is the same profession as a Data Scientist, or is very close. Despite the similarities between these two careers, they differ in how data is leveraged, job functions, and responsibilities. Other differences include the required technical and soft skills, the tools used, the areas of work, the level of education, and the opportunities for career advancement.

This article clarifies the difference between the two jobs.. We will discuss what a Data Analyst and a Data Scientist are, the main distinctions between them, and why it is important to separate these two jobs.

What is a Data Analyst?

A Data Analyst is a person who gathers, evaluates, cleans, and presents structured data to resolve a specified issue, for example, what is the right age group for this commercial, or what trend is popular for requested customers, and helps businesses make important decisions based on that data. This career is popular in various fields like finance, business, health, government, and science.

What are a Data Analyst’s Responsibilities and Functions?

The main responsibilities of a Data Analyst are to evaluate data, transform this information into feedback, patterns, or useful insights. Besides, this job has other roles, which include:

• Make sure that the information is reliable and of high quality.
• Gather data from multiple references.
• Create reports.
• Examine data and detect patterns.
• Create charts and a dashboard for decision support.
• Deliver the results with the team colleagues.

Depending on the industry a Data Analyst works in, like finance or health, their responsibilities can include more functions with field-related nuances.

What are the Required Skills for a Data Analyst to Have?

A Data Analyst should have both technical and soft skills for better task handling and optimised results. The general technical skills, which represent the base of data analysis, include:

• Basic coding knowledge
• Representation of data using Excel
• Structured Query Language (SQL)
• Data visualisation

In addition to the tech stack, soft skills are also essential for an optimized workflow, and they are the following:

• Decision-making
• Analytical thinking
• Communication proficiency

These two groups of skills combined form a domain knowledge which is a collection of common specifics, challenges, goals, and parameters relevant for an industry, in this case, for Data Analysis. They can differ depending on the field a Data Analyst works in.

What Tools Does a Data Analyst Use?

The tools used in Data Analysis have the same scope: to help collect, analyze, and visualize insights, including:

• Excel
• Python, SQL, and R
• Tableau
• Power BI
• MySQL
• KNIME Analytics Platform
• Google Analytics

For a Data Analyst, the most used tool is Excel for analytical tasks. Each of them has different functions and features suitable for various work processes a Data Analyst handles.

What Education is Required for a Data Analyst?

Many Data Analysts hold at least a bachelor’s degree for this profession, and according to IBM, almost 40% of Data Analysts have a master's or a PhD. The following bachelor’s degrees can be an efficient start to a career in Data Analysis:

• Computer science
• Statistics
• Engineering
• Data science
• Mathematics
• Data analytics

According to Zippia, Data Analysts with a master's degree have a salary of $81.723 annually compared to a bachelor's holder with $74,921/year, even though a master's is less in-demand.

What are the Possibilities for Career advancement of a Data Analyst?

A career in Data Analysis can develop in various sectors with a variety of opportunities, including:

• Management reporting
• Budget analyst
• Compensation and benefits analyst
• Actuary
• Web analytics
• Sales Analytics

The path of a Data Analyst career starts from a Freelancer or Data Analyst Intern, gaining experience with the possibility of offering data consulting services. The next step is the job of a Data Analyst, helping companies optimize business strategies. Then comes the position of Data Manager, which is a senior-level analyst who manages daily operations. Chief Data Officer is the last step in the career of a Data Analyst who has the most authority on the tech processes, working together with the chief executive officer and chief financial officer.

What is a Data Scientist?

A Data Scientist uses computer science, statistics, mathematics, and analytics to extract useful information from complex datasets, and works with both structured and unstructured data. Another job responsibility is to use samples, methods, tailored analysis, and forecasting patterns for modeling data. The potential of a Data Scientist extends from identifying national safety dangers to forecasting the best treatment for diseases like diabetes.

What are a Data Scientist’s Responsibilities and Functions?

The responsibility of a Data Scientist includes collecting data from different sources like databases, webpages, devices, spreadsheets, and external tools. Finding patterns in large databases is also a responsibility of this career. Depending on the job specifics, the common functions of a Data Scientist include the following work steps:

• Gather information automatically or manually, depending on the data
• Data cleaning and organizing
• Data examination by creating diagrams, summaries, and evaluating trend differences.
• Develop machine learning models to predict future outcomes
• Presenting and interpreting results

This job can have additional functions, for example, automating work processes with AI tools, and collaborating with various team members to apply the resulting data.

What are the Required Skills for a Data Scientist to Have?

A career in Data Science is built on a variety of technical and soft skills, in addition to various strategies to optimize the workflow. The following tech skills are centered on knowledge in computer science, mathematics, coding, and statistics, and these include:

• Coding abilities
• Develop machine learning models
• Database administration and data wrangling
• Knowledge in machine learning and deep learning
• Data representation
• Using Cloud computing tools

Soft skills represent another important set of abilities a Data Scientist should have. They don’t require tech knowledge but are important for solving business problems, and are the following:

• Problem-solving
• Clear communication
• Emotional intelligence

Although these are the most common set of skills a Data Scientist should have, it can be different depending on the domain one professes in. It can include soft abilities like business sense for enterprises and creative problem-solving. In addition to the tech stack, it can be an accurate application of math and statistics, and knowledge of using self-service analytics apps.

What Tools Does a Data Scientist Use?

There are plenty of tools for Data Scientists to leverage and optimize the work functions. The community of this industry provides tutorials and guides to use these tools to their maximum potential, including:

• Python, SQL, and R
• The Natural Language Toolkit (NLTK)
• Matplotlib
• TensorFlow
• D3.js
• Tableau
• Apache Spark and Apache Hadoop
• KNIME
• Scikit-learn

According to the survey from BrainStation, Data Scientists use mostly Python alongside a variety of other tools, including SQL and Tableau. Depending on the working sector, a Data Scientist’s set of tools can have additions for industry-related tasks.

What Education is Required for a Data Scientist?

To become a Data Scientist, it is recommended to have at least a bachelor's degree, but some employers don't require one for hiring. Despite that, having a degree can be quite helpful and ease the path to creating a career in Data Science. The following bachelor’s, master’s, and PhD degrees provide the skills and knowledge to become a professional in this field.

• Data Science
• Mathematics
• Statistics
• Engineering
• Business

These degrees’ role is to learn the extensive and in-depth details and features of this industry. Also, Universities can offer internships and networking possibilities for career advancements.

What are the Possibilities for Career advancement of a Data Scientist?

Sectors in which a Data Scientist can create a career include finance, academia, scientific research, and e-commerce. Other fields are also an option, for example:

• Marketing
• Government
• Business services
• Business intelligence
• Cybersecurity analysis
• Cloud computing

The career progression of a Data Scientist depends on the known skills and functions. Starting with basic database knowledge as a beginner, to leading research in Data Science, and applying complex machine learning models.

The first position in this career is a Data Science Intern, followed by a Data Scientist with skills in data wrangling and machine learning. Then comes the position of Senior Data Scientists with Deep learning and AI technologies abilities, succeeded by Lead Data Scientist creating predictive algorithms. The final position is the Chief Data Scientist, who is the head of research and development initiatives in Data Science.

What Are the Key Differences Between a Data Analyst and a Data Scientist?

The similarity between a Data Analyst and a Data Scientist can be confusing for many people. This is due to the resemblance in the job titles and because they leverage data for important decisions and solutions. But their responsibilities, required skills, and their engagement with the analytics workflow are different, despite them having common points. This table represents the main contrasts between a Data Analyst and a Data Scientist.

The Most Important Differences Between a Data Analyst and a Data Scientist

	Data Analyst	Data Scientist
Data type	Works with structured data (databases, spreadsheets, reports).	Works with both structured and unstructured data (text, pictures, IoT, and others).
Main job responsibilities and objectives	Collects and clears data, analyzes patterns to create useful insights that answer the following questions: What happened, and why did it happen?	Leverages advanced technologies and develops machine learning models to predict future patterns and outcomes What will happen?
Tech and Soft skills	Mainly using Excel for data organization Decision making	Developing machine learning models Using cloud computing tools Data wrangling Emotional intelligence
Tools	Mostly uses Excel, Basic Python and R Google Analytics and Tableau, in addition	Uses Python and SQL mainly Libraries like NLTK, TensorFlow, Matplotlib, D3.js, and Scikit-learn
Education	Mostly requires a bachelor’s degree in: computer science engineering statistics maths data analytics It is an advantage to have a master’s degree for a Data Analyst	Bachelor’s degree in: data science business mathematics statistics engineering A master's degree is often expected, or even a PhD.
Career advancements	Freelancer/ Data Analyst Intern ↓ Data Analyst ↓ Data Manager ↓ Chief Technology Officer	Data Scientist Intern ↓ Data Scientist ↓ Senior Data Scientist ↓ Lead Data Scientist ↓ Chief Data Scientist

What Are the Similarities Between a Data Analyst and a Data Scientist?

Between these careers exist overlaps that make them very close due to their work foundation, common tools and skills, and even educational degrees. The other similarities are the following:

The Main Similarities Between a Data Analyst and a Data Scientist

Domain Foundation	Both rely on mathematics, statistics, and computer science
Work responsibilities and objectives	They both: Work with structured data Search, clean, and organize data Represent the information for team members
Tech and Soft skills	It is a necessity for these jobs to: Leverage computer knowledge in Python, R, and SQL, Represent data with charts and graphics.
Tools	Common tools: Python, R, and SQL Tableau
Education	For both careers, are helpful bachelor’s degree in: computer science statistics mathematics data science
Career advancements	Both jobs start at entry-level functions and can grow to a leadership position.

Final Thoughts

It is easy to understand why many people make the mistake of thinking that a Data Analyst and a Data Scientist are the same, due to their use of information, working fields, and responsibilities. But the main aspect of why these two are so different is that a Data Analyst can explain the past with helpful insights, while a Data Scientist creates complex models to predict the future.

It is important to know this difference if someone will need to collaborate with a Data Analyst or a Data Scientist. In addition, one more reason is that these jobs differently influence how organizations use information to make important decisions that can also affect us.