Data Analyst vs Data Scientist: What's the difference?

In today’s data-driven world, data is more valuable than gold, but many firms fail to capitalize on it. Only around 10% of the data produced by businesses has been thoroughly examined and put to use as of yet. But as generative AI gains traction, companies are rapidly learning how crucial it is to leverage data. The need for data scientists and analysts is expected to increase dramatically in the upcoming years, increasing by 35% for data scientists and 23% for data analysts by 2032, according to the U.S. Bureau of Labor Statistics.

The duties, skill sets, and career pathways of these two employment jobs vary greatly, despite their shared emphasis on data. In this blog, we’ll go into the details of what data analysts and data scientists do, their significant differences, educational requirements, wages, and much more.

Table of Contents

• What Does a Data Analyst Do?
• What Does a Data Scientist Do?
• Differences and Similarities Between Data Analysts and Data Scientists
• Education: Data Analyst vs. Data Scientist
• Skill Comparison: Data Analyst vs. Data Scientist
• Jobs Available: Data Analyst vs. Data Scientist
• Job description: Data Analyst vs. Data Scientist
• Salary Comparison: Data Analyst vs. Data Scientist
• FAQs on Data Analyst vs. Data Scientist

What Does a Data Analyst Do?

A data analyst’s primary role is to interpret and analyze existing data to help organizations make informed decisions. They play a vital role in translating raw data into clear, actionable insights. Data analysts gather data, process it, and create reports and visualizations to help stakeholders understand the implications of the data.

1. Key Responsibilities of Data Analyst

• Data Collection and Cleaning: They gather data from multiple sources, ensuring that the data is accurate and complete.
• Statistical Analysis: Data analysts apply statistical techniques to identify trends and patterns in the data.
• Reporting and Visualization: They use tools like Excel, Power BI, and Tableau to present findings in easy-to-understand reports, graphs, and dashboards.
• Decision Support: By presenting clear insights, data analysts help businesses make data-driven decisions.

2. Tools Used by Data Analyst

• SQL: For querying databases and extracting the required data.
• Excel: For performing basic analysis and data manipulation.
• Power BI & Tableau: For creating interactive reports and visualizations that help stakeholders make informed decisions.

For example, a data analyst examines customer purchase data to discover trends and forecast future sales, assisting a retail company in optimizing inventory and promotions. In technical terms, this is also referred to as predictive analytics.

3. Roles and Responsibilities of Data Analyst

A Data Analyst has a variety of roles and responsibilities, some of which are listed below:

• Data Collection from several sources, guaranteeing completeness and accuracy while removing mistakes.
• Utilize tools from R, Python, and SQL to identify trends, patterns, and linkages in the data.
• Make dashboards, graphs, charts, and other condensed reports and visualizations.
• Monitors important metrics and looks for changes or irregularities that need attention.
• Informs stakeholders of findings and insights, making sure they comprehend the ramifications.

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What Does a Data Scientist Do?

Data scientists go beyond analyzing existing data—they develop algorithms and models to predict future trends and automate processes. They work with large, complex datasets, often using machine learning and advanced statistical methods to uncover insights that are not immediately obvious.

1. Key Responsibilities of a Data Scientist

• Data Acquisition: To prepare it for future analysis, data scientists gather information from a variety of sources, including both structured and unstructured data.
• Advanced Statistical Analysis: To find trends and forecast outcomes, they employ machine learning and sophisticated statistical techniques.
• Model Building: To improve business procedures, data scientists create, evaluate, and implement predictive models.
• Automation: To improve workflows and save time, they automate data processing tasks.

2. Tools Used by Data Scientists

• Python and R: For data manipulation, programming, and creating machine learning models.
• Hadoop and Spark: For processing of Large datasets (big data).
• PyTorch with TensorFlow: For applications involving deep learning.

A data scientist uses patient data, such as medical history, genetic information, and lifestyle factors, to create a model that predicts a person’s chance of getting a specific disease, such as cancer or diabetes. In order to improve patient outcomes, this model can be used to identify high-risk people who can profit from early screening or preventative actions.

3. Roles and Responsibility of Data Scientist

A Data Scientist has following list of roles and responsibilities:

• Defines and presents data science problems as business problems.
• Gathers, purifies, and gets data ready for modeling.
• Creates, assesses, and improves models for machine learning.
• Deploys and keeps an eye on production models.
• Shares conclusions and revelations with interested parties.

Differences and Similarities Between Data Analysts and Data Scientists

While both roles deal with data, they focus on different aspects of data analysis and use different methodologies to solve problems.

1. Difference Between Data Analyst and Data Scientist

Here’s a comparison between these two roles elaborately: 

2. Similarities Between Data Analysts and Data Scientists

While data analysts and data scientists differ in their focuses and skills, they share a number of significant similarities. The following tells us about some similarities that both of these roles share:

• Goal: Both aim to use data to understand business problems and inform decision-making.
• Foundational Skills: Both require strong analytical and problem-solving skills, critical thinking, and communication abilities.
• Technical Skills: Both need proficiency in data preparation, cleaning, and manipulation, often using tools like SQL and data visualization platforms.
• Communication and Collaboration: Both need to effectively communicate insights and findings to both technical and non-technical audiences, often collaborating with colleagues from various teams.
• Impact: Both play crucial roles in driving data-driven decision-making and creating value for organizations.

Educational Requirements

Both roles require a solid foundation in data science and analytics, but the level of education and specialized knowledge required differs between data analysts and data scientists.

Education for Data Analyst

• Degree: In many cases, a bachelor’s degree in business analytics, computer science, mathematics, or statistics is adequate.
• Certifications: Having certifications in programming languages (such as SQL, Python, or R) and data analysis tools is quite advantageous and can make a person stand out in the employment market.

Education for Data Scientist

• Degree: The majority of data scientists have a master’s or doctoral degree in statistics, computer science, data science, or a similar discipline.
• Certifications: To improve abilities and career prospects, advanced certifications in data science, machine learning, and artificial intelligence are frequently needed.

Skill Comparison: Data Analyst vs. Data Scientist

The skill sets of data analysts and data scientists are crucial in determining their respective job functions. Below is a detailed comparison of the skills required for both roles.

Jobs Opportunities

Both roles are in high demand globally, with numerous job opportunities available for data analysts and data scientists.

Jobs Opportunities for Data Analyst

Source: Data Analyst Jobs India LinkedIn

Source: Data Analyst Jobs US LinkedIn

• India: Approximately 116,000 job openings for Data Analysts.
• United States: Around 174,000 job openings for Data Analysts.

Jobs Available for Data Scientists

Source: Data Scientist Jobs India LinkedIn

Source: Data Scientist Jobs US LinkedIn

• India: Approximately 95,000 job openings for Data Scientists.
• United States: Around 150,000 job openings for Data Scientists.

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Job Description: Data Analyst vs. Data Scientist

To give you a clearer understanding of the differences between data analyst and data scientist job descriptions, read the simplified job role outlined.

1. Data Analyst Job Description

Data analysts examine information from a variety of sources, including user behavior, market research, sales records, and logistics. They use their technological expertise to ensure the data is accurate. Then, by comprehending, illustrating, and elucidating the data, they evaluate and exchange insights to assist people, organizations, and groups in making decisions.

We reviewed a few job descriptions for data analysts that were posted on the LinkedIn employment board. This is how Genpact’s job description appears:

Source: Genpact Data Analyst Job Description LinkedIn

Here is Data Analyst job description for PayU Payments Ltd.:

Source: PayU Payments Data Analyst Job Description LinkedIn

2. Data Scientist Job Description

Data scientists are individuals who extract meaning from raw information. To uncover the hidden facts in datasets, they gather data from various sources and apply scientific processes, systems, algorithms, and approaches. Through exploratory data analysis, they investigate the data’s characteristics and gain a thorough comprehension of its content. By developing and putting into practice machine learning models and algorithms, they maximize model performance.

We reviewed a few job descriptions for data scientists that were posted on the LinkedIn job board. The Deloitte job description reads like this:

Source: Deloitte Data Scientist Job Description LinkedIn

The data scientist job description for Tata Technologies:

Source: Tata Technologies Data Scientist Job Description LinkedIn

Salary Comparison: Data Analyst vs. Data Scientist

Salaries for data analysts and data scientists reflect the skills and experience required for each role. Data scientists typically earn higher salaries due to their advanced expertise and responsibilities.

Data Analyst Salary

Source: Data Analyst Salary – Glassdoor

• India: ₹6 lakhs to ₹8 lakhs per annum, with potential earnings up to ₹18 lakhs per annum with experience.
• United States: $85,000 to $100,000 per annum, with the potential to earn up to $150,000 with experience.

Data Scientist Salary

Source: Data Scientist Salary – Glassdoor

According to Glassdoor,

• India: ₹9 lakhs to ₹15 lakhs per annum, with potential earnings up to ₹22 lakhs per annum with experience.
• United States: $100,000 to $130,000 per annum, with the potential to earn up to $200,000 with experience.

Conclusion

Although they both play important roles in assisting businesses in using data to make decisions, data scientists and analysts have different areas of expertise, specializations, and career paths. While data scientists create prediction models and algorithms to extract deeper insights from big and complicated datasets, data analysts use the data that is already available to deliver straightforward insights for decision-making.

Knowing the differences between these two positions will assist you in selecting the best professional route for your objectives, interests, and abilities. In the rapidly expanding data-driven business, there are plenty of prospects for advancement and financial gain in both starting as a data analyst and moving up to a data scientist. If you are interested in learning about these technologies, then you should definitely check out out Courses on Data Science or Data Analytics.

FAQs on Data Analyst vs. Data Scientist