Data Science Vs. Data Analytics – An In-Depth Comparison Of Similarities And Differences
Last Updated: August 20, 2022
Table of Contents- Data Science Vs. Data Analytics: An In-Depth Comparison Of Similarities And Differences
- Data Science Vs. Data Analytics: At A Glance
- What Is Data Science?
- What Is Data Analytics?
- Similarities Between Data Science And Data Analytics
- What’s The Difference Between Data Science And Data Analytics?
- What Does A Data Scientist Do?
- What Does A Data Analyst Do?
- Conclusion
Data Science Vs. Data Analytics: An In-Depth Comparison Of Similarities And Differences
There’s no question that data is transforming the way we do business. In 2025, the annual revenue from big data markets worldwide is expected to be a staggering $68 billion USD.
The rise of big data–an immense amount of information processed, analyzed, and harnessed–offers organizations an opportunity to gain a competitive advantage in their industry. Data science and data analytics allow organizations to improve their customer service, reduce costs, and optimize operations.
While data science and data analytics share some similarities, they each approach and work with data in distinctive ways.
In this article, we’ll explore the biggest differences between data science and data analytics, the difference between a data scientist and data analyst, and which one might be the better fit for you.
Data Science Vs. Data Analytics: At A Glance
Data science and data analytics are both crucial in the processing of large amounts of data for businesses and organizations worldwide. While both data science and data analytics work with big data, they focus on different aspects of it.
Data science explores data in its earlier and more chaotic form. According to IBM, data science is multidisciplinary. It combines several different fields such as mathematics, statistics, and computer science.
Data science involves the preparation stages of data’s life cycle such as acquisition and extraction as well as the communication stage which involves reporting findings and insights to shareholders.
On the other hand, data analytics deals with a specific stage in the life cycle of data science: analysis. It focuses specifically on answering specific questions and concerns.
Data analytics is concerned with how businesses can use the information and insights learned from big data to solve issues, reach certain goals, and optimize performance. The term data analytics encompasses all types of data analysis.
Since analysis is a part of the data science life cycle, a good way to differentiate between data science and data analytics is by defining data analytics as an important branch of data science.
“Data science lays out the groundwork for data analytics to extract meaningful insights from the huge amount of data gathered by organizations,” explains Nora Simmons, a technical writer at Origin Writings and Brit Student.
What Is Data Science?
Data science is a multidisciplinary approach to big data that combines several STEM fields such as computer science, mathematics, and statistics.
Its purpose is to prepare the massive amounts of data collected for data analysis in order to extract as much valuable information as possible for the organization.
To fulfil the roles effectively, data scientists have a deep understanding of each phase of the data science life cycle. The data science life cycle consists of the following five stages.
Capture
- Digitizing data from the real world (data acquisition)
- Retrieving data from multiple sources (data extraction)
Maintain
- Storing data in a secure location (data warehousing)
- Fixing or removing incorrect and corrupt data (data cleansing)
- Converting raw data into a machine-readable form (data processing)
Process
- Sorting through large volumes of data to find patterns and other useful information (data mining)
- Labelling data (classification)
- Grouping similar data entries together (clustering)
- Creating a data model to visualize the data and to store it in a database (data modelling)
- Presenting data in a simplified and concise manner (data summarization)
Analyze
- Exploring the data in search of trends and p (exploratory data analysis)
- Testing your evidence to confirm your exploratory data analysis (confirmatory data analysis)
- Determining the likelihood of certain trends and future outcomes based on historical data (predictive analysis)
- Examining the relationships between variables and identifying which variables carry the most influence (regression)
- Using artificial intelligence, specifically natural language processing, to uncover insights from unstructured data (text mining)
- Discovering and understanding the meaning behind data sets (qualitative analysis)
Communicate
- Submitting a summary of your findings from the data in an organized format (data reporting)
- Presenting information in a way that’s easier to understand with visual context (data visualization tools)
- Producing a report on the company’s activities, performance measures, and other information that helps with better decision-making (business intelligence)
Due to the advantages, it offers to any organization, a growing number of businesses are turning to data science and data scientists. 83% of business executives pursue big data for a competitive advantage.
What Is Data Analytics?
While data science comprises all five stages of the data science life cycle, data analytics focuses mostly on the analysis and communication phases. Data analytics or big data analytics is the analysis of raw data in order to extract valuable information from it.
Its purpose is to organize big data in a more meaningful way, extract intelligence from data, and report any notable patterns that could help the organization with its decision-making process.
All types of data analysis fall under the broad category of data analytics. However, its four main components are:
- Answers that question, “What happened?”
- Examines current and past data to understand the business more
- Considered to be essential for business intelligence
- Used as the foundation for diagnostic analytics, prescriptive analytics, and predictive analytics
- Identifies notable trends through tools such as Microsoft Excel
- Answers the question, “How could this happen?”
- Focuses on the causes behind a trend or relationship between variables
- Usually follows descriptive analytics and is used to support a decision with convincing data
- Employs techniques such as hypothesis testing, causation or correlation, and diagnostic regression analysis
- Answers the question, “What’s going to happen?”
- Uses historical data, machine learning, modelling, and data mining
- Predicts future risks and opportunities for the company based on data trends
- Builds models capable of forecasting future events
- Valued across several industries including:
- healthcare (used to detect chronic illness and care for patients)
- finance (used to prevent fraud and determine credit risk)
- human resources (used to predict the health of the labour market)
- retail (used for product recommendations and sale forecasts)
- marketing (used for marketing campaigns)
- Answers the question, “What actions should be taken in order to reach a certain solution or goal?”
- Uses tools such as machine learning, neural networks, graph analysis, simulation, and complex event processing
- Collects data from descriptive and predictive sources to create models
- Provides recommendations on how to grow as a business and optimize performance
- Identifies the best course of action for the organization
A study found that companies that take advantage of data analytics to inform their decision-making are 23 times more likely to acquire more new customers than other companies in their industry that do not analyze big data.
“As a result of its ability to boost customer acquisition and improve business performance, more and more companies are taking advantage of data analytics,” according to Mary Ramirez, a business writer at Write my X and 1 Day 2 write.
“Soon, it might be impossible for some organizations to keep up with their competitors without using big data analytics.”
Similarities Between Data Science And Data Analytics
Both data science and data analytics allow organizations to gain a competitive advantage in their industry. Here are some more similarities between data science and data analytics.
Interpret Big Data
Data science and data analytics sort through large volumes of data to find patterns and relationships. Though their scope may be different, data science and data analytics both try to make sense of data.
Technical-Based
Data science and data analytics frequently borrow techniques and tools from highly technical fields such as computer science, statistics, and information technology.
Impact On Other Functional Areas
Findings from data science and data analytics often affect marketing, sales, product development, and operations.
What’s The Difference Between Data Science And Data Analytics?
Since data analytics could be considered a part of the data science life cycle, the key difference between data science and data analytics is the extent to which they work with big data.
Here are a few more ways to distinguish between the two.
Focus
While data science aims to detect any patterns and possible insights from the vast amounts of data collected by a company, data analytics focuses on answering specific questions and concerns.
Technique and Tools
Data analytics typically relies on automated processes and algorithms for assistance in forming conclusions.
On the other hand, data science relies on techniques and processes from various disciplines such as statistical models, machine learning, and data mining.
Educational Backgrounds
Most people in data science possess an undergraduate degree in a technical or mathematical field like computer science in addition to a master’s degree.
In contrast, those who pursue data analytics could have a bachelor’s degree from a wide array of STEM subjects.
What Does A Data Scientist Do?
Similar to other big data professions, a data scientist helps businesses derive the most value from the volumes of data they collect.
In addition to being one of the most demanded jobs by businesses around the world, a data scientist has been ranked in the past as the best job in America.
According to Indeed, the average salary for a data scientist in the state is $74, 968 as of 2021.
However, plenty of leading companies in their industry is willing to pay significantly more in order to attract talented and skilled data scientists.
If you’re considering hiring a data scientist, here are a few reasons why these professionals are so highly valued by businesses worldwide.
A data scientist fulfils the following responsibilities:
- Acts as a knowledgeable adviser to executives who want to use data to make better decisions
- Works with other departments such as marketing, sales and development to recognize patterns and potential areas of improvement
- Presents trends found in the data using visual models to help others understand its meaning and implications
Due to the broad scope and highly technical nature of the work, data scientists must be skilled in a diverse set of techniques and processes:
- Creating algorithms and statistical models to ensure machine learning is fed large volumes of high-quality data
- Writing code using programming languages such as JavaScript, Python, and R
- Detecting patterns and anomalies in the data using statistical analysis
- Communicating the key takeaways from the data in a coherent, concise, and less technical manner
What Does A Data Analyst Do?
As data scientists, data analyst helps organizations make more effective and profitable business decisions.
While a data scientist makes informed predictions based on data trends, data analysts ensure that stakeholders within the company understand the insights from these patterns.
According to Indeed, the average salary for data analysts is $64,756 as of 2021. Although this is the national average, many companies pay higher in an effort to recruit top talents in data analytics.
If you’re hoping to add a data analyst to your team, their role would include these responsibilities:
- Acts as a sort of interpreter who assists other stakeholders within the organization comprehend the meaning of big data
- Provides clear and organized reports to company executives who, in turn, use the data analyst’s conclusions to assist with important decision-making
- Collaborate with professionals from other departments to implement data-based modifications and improvements across the organization
While the work of a data analyst may not be as technical as that of a data scientist, most data analysts are well-rounded and knowledgeable in technical skills.
Some of the most significant techniques and processes used by a data analyst are:
- Programming languages such as Python, SQL and R
- Various analysis methods and techniques such as diagnostic analysis and predictive analysis
- Charts, graphs and other visualization tools
- Artificial intelligence and machine learning models
Conclusion
Depending on your company’s objectives and access to high-quality data, data science and data analytics could help your organization stay ahead of its competitors.
If your business wants data-based solutions to a particular set of issues or formulate strategies for specific concerns, your needs would be better met by incorporating data analytics.
On the other hand, if your goal as an organization is to gain valuable knowledge and actionable insights from big data, data science can help you meet these goals.
George J. Newton is a business development manager at Write my literature review and PhD Kingdom. He has been married for ten years, perfecting the art of the apology throughout. He writes more about data trends and analysis for Coursework help.
Related Articles
- Best Types of Charts in Excel for Data Analysis, Presentation and Reporting
- KPI Meaning, Examples, Calculation & Dashboard Tutorial
- What type of chart to use to compare data in Excel
- Conversion Rate in Google Analytics – Learn to Analyze & Report
- Google Analytics Mistakes that kill your Analysis & Conversions
- Top 20 reasons why people misinterpret data and reports
- What You Should Know about Historical Data in Web Analytics
- Making Good Marketing Decisions Despite of Faulty Analytics Data
- Data Trend Analysis in Google Analytics
- Google Analytics Averages – Learn to Analyze & Report above Average
Last Updated: August 20, 2022
- Data Science Vs. Data Analytics: An In-Depth Comparison Of Similarities And Differences
- Data Science Vs. Data Analytics: At A Glance
- What Is Data Science?
- What Is Data Analytics?
- Similarities Between Data Science And Data Analytics
- What’s The Difference Between Data Science And Data Analytics?
- What Does A Data Scientist Do?
- What Does A Data Analyst Do?
- Conclusion
Data Science Vs. Data Analytics: An In-Depth Comparison Of Similarities And Differences
There’s no question that data is transforming the way we do business. In 2025, the annual revenue from big data markets worldwide is expected to be a staggering $68 billion USD.
The rise of big data–an immense amount of information processed, analyzed, and harnessed–offers organizations an opportunity to gain a competitive advantage in their industry. Data science and data analytics allow organizations to improve their customer service, reduce costs, and optimize operations.
While data science and data analytics share some similarities, they each approach and work with data in distinctive ways.
In this article, we’ll explore the biggest differences between data science and data analytics, the difference between a data scientist and data analyst, and which one might be the better fit for you.
Data Science Vs. Data Analytics: At A Glance
Data science and data analytics are both crucial in the processing of large amounts of data for businesses and organizations worldwide. While both data science and data analytics work with big data, they focus on different aspects of it.
Data science explores data in its earlier and more chaotic form. According to IBM, data science is multidisciplinary. It combines several different fields such as mathematics, statistics, and computer science.
Data science involves the preparation stages of data’s life cycle such as acquisition and extraction as well as the communication stage which involves reporting findings and insights to shareholders.
On the other hand, data analytics deals with a specific stage in the life cycle of data science: analysis. It focuses specifically on answering specific questions and concerns.
Data analytics is concerned with how businesses can use the information and insights learned from big data to solve issues, reach certain goals, and optimize performance. The term data analytics encompasses all types of data analysis.
Since analysis is a part of the data science life cycle, a good way to differentiate between data science and data analytics is by defining data analytics as an important branch of data science.
“Data science lays out the groundwork for data analytics to extract meaningful insights from the huge amount of data gathered by organizations,” explains Nora Simmons, a technical writer at Origin Writings and Brit Student.
What Is Data Science?
Data science is a multidisciplinary approach to big data that combines several STEM fields such as computer science, mathematics, and statistics.
Its purpose is to prepare the massive amounts of data collected for data analysis in order to extract as much valuable information as possible for the organization.
To fulfil the roles effectively, data scientists have a deep understanding of each phase of the data science life cycle. The data science life cycle consists of the following five stages.
Capture
- Digitizing data from the real world (data acquisition)
- Retrieving data from multiple sources (data extraction)
Maintain
- Storing data in a secure location (data warehousing)
- Fixing or removing incorrect and corrupt data (data cleansing)
- Converting raw data into a machine-readable form (data processing)
Process
- Sorting through large volumes of data to find patterns and other useful information (data mining)
- Labelling data (classification)
- Grouping similar data entries together (clustering)
- Creating a data model to visualize the data and to store it in a database (data modelling)
- Presenting data in a simplified and concise manner (data summarization)
Analyze
- Exploring the data in search of trends and p (exploratory data analysis)
- Testing your evidence to confirm your exploratory data analysis (confirmatory data analysis)
- Determining the likelihood of certain trends and future outcomes based on historical data (predictive analysis)
- Examining the relationships between variables and identifying which variables carry the most influence (regression)
- Using artificial intelligence, specifically natural language processing, to uncover insights from unstructured data (text mining)
- Discovering and understanding the meaning behind data sets (qualitative analysis)
Communicate
- Submitting a summary of your findings from the data in an organized format (data reporting)
- Presenting information in a way that’s easier to understand with visual context (data visualization tools)
- Producing a report on the company’s activities, performance measures, and other information that helps with better decision-making (business intelligence)
Due to the advantages, it offers to any organization, a growing number of businesses are turning to data science and data scientists. 83% of business executives pursue big data for a competitive advantage.
What Is Data Analytics?
While data science comprises all five stages of the data science life cycle, data analytics focuses mostly on the analysis and communication phases. Data analytics or big data analytics is the analysis of raw data in order to extract valuable information from it.
Its purpose is to organize big data in a more meaningful way, extract intelligence from data, and report any notable patterns that could help the organization with its decision-making process.
All types of data analysis fall under the broad category of data analytics. However, its four main components are:
- Answers that question, “What happened?”
- Examines current and past data to understand the business more
- Considered to be essential for business intelligence
- Used as the foundation for diagnostic analytics, prescriptive analytics, and predictive analytics
- Identifies notable trends through tools such as Microsoft Excel
- Answers the question, “How could this happen?”
- Focuses on the causes behind a trend or relationship between variables
- Usually follows descriptive analytics and is used to support a decision with convincing data
- Employs techniques such as hypothesis testing, causation or correlation, and diagnostic regression analysis
- Answers the question, “What’s going to happen?”
- Uses historical data, machine learning, modelling, and data mining
- Predicts future risks and opportunities for the company based on data trends
- Builds models capable of forecasting future events
- Valued across several industries including:
- healthcare (used to detect chronic illness and care for patients)
- finance (used to prevent fraud and determine credit risk)
- human resources (used to predict the health of the labour market)
- retail (used for product recommendations and sale forecasts)
- marketing (used for marketing campaigns)
- Answers the question, “What actions should be taken in order to reach a certain solution or goal?”
- Uses tools such as machine learning, neural networks, graph analysis, simulation, and complex event processing
- Collects data from descriptive and predictive sources to create models
- Provides recommendations on how to grow as a business and optimize performance
- Identifies the best course of action for the organization
A study found that companies that take advantage of data analytics to inform their decision-making are 23 times more likely to acquire more new customers than other companies in their industry that do not analyze big data.
“As a result of its ability to boost customer acquisition and improve business performance, more and more companies are taking advantage of data analytics,” according to Mary Ramirez, a business writer at Write my X and 1 Day 2 write.
“Soon, it might be impossible for some organizations to keep up with their competitors without using big data analytics.”
Similarities Between Data Science And Data Analytics
Both data science and data analytics allow organizations to gain a competitive advantage in their industry. Here are some more similarities between data science and data analytics.
Interpret Big Data
Data science and data analytics sort through large volumes of data to find patterns and relationships. Though their scope may be different, data science and data analytics both try to make sense of data.
Technical-Based
Data science and data analytics frequently borrow techniques and tools from highly technical fields such as computer science, statistics, and information technology.
Impact On Other Functional Areas
Findings from data science and data analytics often affect marketing, sales, product development, and operations.
What’s The Difference Between Data Science And Data Analytics?
Since data analytics could be considered a part of the data science life cycle, the key difference between data science and data analytics is the extent to which they work with big data.
Here are a few more ways to distinguish between the two.
Focus
While data science aims to detect any patterns and possible insights from the vast amounts of data collected by a company, data analytics focuses on answering specific questions and concerns.
Technique and Tools
Data analytics typically relies on automated processes and algorithms for assistance in forming conclusions.
On the other hand, data science relies on techniques and processes from various disciplines such as statistical models, machine learning, and data mining.
Educational Backgrounds
Most people in data science possess an undergraduate degree in a technical or mathematical field like computer science in addition to a master’s degree.
In contrast, those who pursue data analytics could have a bachelor’s degree from a wide array of STEM subjects.
What Does A Data Scientist Do?
Similar to other big data professions, a data scientist helps businesses derive the most value from the volumes of data they collect.
In addition to being one of the most demanded jobs by businesses around the world, a data scientist has been ranked in the past as the best job in America.
According to Indeed, the average salary for a data scientist in the state is $74, 968 as of 2021.
However, plenty of leading companies in their industry is willing to pay significantly more in order to attract talented and skilled data scientists.
If you’re considering hiring a data scientist, here are a few reasons why these professionals are so highly valued by businesses worldwide.
A data scientist fulfils the following responsibilities:
- Acts as a knowledgeable adviser to executives who want to use data to make better decisions
- Works with other departments such as marketing, sales and development to recognize patterns and potential areas of improvement
- Presents trends found in the data using visual models to help others understand its meaning and implications
Due to the broad scope and highly technical nature of the work, data scientists must be skilled in a diverse set of techniques and processes:
- Creating algorithms and statistical models to ensure machine learning is fed large volumes of high-quality data
- Writing code using programming languages such as JavaScript, Python, and R
- Detecting patterns and anomalies in the data using statistical analysis
- Communicating the key takeaways from the data in a coherent, concise, and less technical manner
What Does A Data Analyst Do?
As data scientists, data analyst helps organizations make more effective and profitable business decisions.
While a data scientist makes informed predictions based on data trends, data analysts ensure that stakeholders within the company understand the insights from these patterns.
According to Indeed, the average salary for data analysts is $64,756 as of 2021. Although this is the national average, many companies pay higher in an effort to recruit top talents in data analytics.
If you’re hoping to add a data analyst to your team, their role would include these responsibilities:
- Acts as a sort of interpreter who assists other stakeholders within the organization comprehend the meaning of big data
- Provides clear and organized reports to company executives who, in turn, use the data analyst’s conclusions to assist with important decision-making
- Collaborate with professionals from other departments to implement data-based modifications and improvements across the organization
While the work of a data analyst may not be as technical as that of a data scientist, most data analysts are well-rounded and knowledgeable in technical skills.
Some of the most significant techniques and processes used by a data analyst are:
- Programming languages such as Python, SQL and R
- Various analysis methods and techniques such as diagnostic analysis and predictive analysis
- Charts, graphs and other visualization tools
- Artificial intelligence and machine learning models
Conclusion
Depending on your company’s objectives and access to high-quality data, data science and data analytics could help your organization stay ahead of its competitors.
If your business wants data-based solutions to a particular set of issues or formulate strategies for specific concerns, your needs would be better met by incorporating data analytics.
On the other hand, if your goal as an organization is to gain valuable knowledge and actionable insights from big data, data science can help you meet these goals.
George J. Newton is a business development manager at Write my literature review and PhD Kingdom. He has been married for ten years, perfecting the art of the apology throughout. He writes more about data trends and analysis for Coursework help.
Related Articles
- Best Types of Charts in Excel for Data Analysis, Presentation and Reporting
- KPI Meaning, Examples, Calculation & Dashboard Tutorial
- What type of chart to use to compare data in Excel
- Conversion Rate in Google Analytics – Learn to Analyze & Report
- Google Analytics Mistakes that kill your Analysis & Conversions
- Top 20 reasons why people misinterpret data and reports
- What You Should Know about Historical Data in Web Analytics
- Making Good Marketing Decisions Despite of Faulty Analytics Data
- Data Trend Analysis in Google Analytics
- Google Analytics Averages – Learn to Analyze & Report above Average
Register for the FREE TRAINING...
"How to use Digital Analytics to generate floods of new Sales and Customers without spending years figuring everything out on your own."
Here’s what we’re going to cover in this training…
#1 Why digital analytics is the key to online business success.
#2 The number 1 reason why most marketers are not able to scale their advertising and maximize sales.
#3 Why Google and Facebook ads don’t work for most businesses & how to make them work.
#4 Why you won’t get any competitive advantage in the marketplace just by knowing Google Analytics.
#5 The number 1 reason why conversion optimization is not working for your business.
#6 How to advertise on any marketing platform for FREE with an unlimited budget.
#7 How to learn and master digital analytics and conversion optimization in record time.
My best selling books on Digital Analytics and Conversion Optimization
Maths and Stats for Web Analytics and Conversion Optimization
This expert guide will teach you how to leverage the knowledge of maths and statistics in order to accurately interpret data and take actions, which can quickly improve the bottom-line of your online business.
Master the Essentials of Email Marketing Analytics
This book focuses solely on the ‘analytics’ that power your email marketing optimization program and will help you dramatically reduce your cost per acquisition and increase marketing ROI by tracking the performance of the various KPIs and metrics used for email marketing.
Attribution Modelling in Google Analytics and BeyondSECOND EDITION OUT NOW!
Attribution modelling is the process of determining the most effective marketing channels for investment. This book has been written to help you implement attribution modelling. It will teach you how to leverage the knowledge of attribution modelling in order to allocate marketing budget and understand buying behaviour.
Attribution Modelling in Google Ads and Facebook
This book has been written to help you implement attribution modelling in Google Ads (Google AdWords) and Facebook. It will teach you, how to leverage the knowledge of attribution modelling in order to understand the customer purchasing journey and determine the most effective marketing channels for investment.
About the Author
Himanshu Sharma
