Explain The Major Difference Between Data Science And Data Analytics

Written By
Published on July 27th, 2023

Table of Contents

Introduction

Big data has a significant role in the tech world today. Its actionable insights and results can help any business to grow. The creation of large datasets needs the understanding and support of the proper tools on hand. As a result, it uncovers useful information from the pool of data. For better use of big data, the branches of data science and analytics are spreading in every field. They have stepped up from academia, to become crucial elements of Business Intelligence and big data analytics tools. It can be confusing to choose between data analytics and data science. Although the two are interconnected, they provide different results. They pursue different approaches. If you want to study the data your business is collecting, it is important to sort out what they bring to the table.

To help you better use your data, we will explain both categories. Know their differences, and find the value they deliver.

What Is Data Science?

It is a multidisciplinary field that finds useful insights from huge sets of raw and structured data. This field helps find the answers to the things you don’t know. Data science professionals use several techniques to find answers. They use computer science, predictive analytics, statistics, and machine learning. They dive through large datasets to establish solutions to problems that seem to be impossible.

What Is Data Analytics?

This branch aims at processing and performing statistical analysis of the gathered datasets. These experts work to build methods to collect, process and organise data. It enables you to uncover actionable insights for current problems. Thus you can establish the best possible way to present this data. The field of data and analytics is focused on finding answers to questions relevant to your business. Moreover, it aims at producing results that lead to improvements.Data analytics also involves different branches of broader statistics and analysis. It helps combine diverse sources of data and find connections thereby simplifying the results.

What Is the Difference between the two?

Many people use these terms interchangeably. But both are unique fields, with differences in scope and features. Data science is a term that combines fields used to mine large datasets. Data analytics software is a dedicated version of this. Analytics aims at realizing actionable insights to apply immediately based on present queries.

The next main difference between the two fields is the reason for exploration. Data science is not concerned with resolving specific queries, instead, it analyses massive datasets. Sometimes they may be unstructured ways to expose insights. Data analysis performs better when done with a focused mind that needs answers based on current data. Data science creates broader insights to concentrate on which areas need improvement. Whereas big data analytics focus on discovering answers to only the questions being asked.

The functions of both fields are highly interconnected. Data science puts important foundations and allows big datasets to create initial observations. Further, it finds future trends and potential insights that hold importance. The information gathered is useful for some fields, such as modelling. It also helps improve machine learning and AI algorithms as it can enhance how information is sorted and understood. However, data science involves asking important questions that you were unaware of before. By adding data analytics into the work, you can transform those things into actionable insights. You will end up with some practical applications.

Big Data covers everything from texts, and emails, to tweets, and user searches. The generated data via the digital world is very vast and complex. Hence traditional data processing and analysis systems are unable to handle it. As a result, businesses are entering Data Science and Data Analytics.

Big Data, Data Science, and Data Analytics are emerging technologies (they’re still evolving), but the difference between Data Analyst and Data Scientist is stark.

Although Data Science and Data Analytics deal with Big Data, each takes a unique approach. Data Science encompasses Data Analytics and combines multiple disciplines. It involves expertise in

Computer Science, Mathematics, Statistics, Information Science, Machine Learning, and Artificial Intelligence.

It involves concepts such as data mining, and data inference. You must also know predictive modelling, and ML algorithm development. You can extract patterns from complex datasets and transform them into actionable business strategies with data science. While data analytics is concerned with Mathematics, Statistics, and Statistical Analysis.

Data Science enables you to find new and unique insights that can drive business innovation. Whereas, Data Analysis helps you to determine how they can be applied within an organisation to implement data-driven innovation.

Our Learners Also Read: What Is Data Science? Applications, Lifecycle, Use Cases, And Processes

Responsibilities of both the professionals

Both of them can perform a higher degree of data collection, cleaning, and analysis. Hence you can gain useful insights for data-driven decision-making. So, the responsibilities of Data Scientists and Data Analysts may overlap sometimes. There are several data analytics and data science benefits based on their roles and responsibilities.

Responsibilities of Data Scientists

Process, clean, and validate the integrity of data.
Perform Exploratory Data Analysis on huge datasets.
Perform data mining by the creation of ETL pipelines.
Perform statistical analysis through ML algorithms.
Write code that enables automation.
Build resourceful ML libraries.

Responsibilities of Data Analysts

Collect and interpret data.
Identify relevant patterns in a dataset.
Perform data querying via SQL.
Work with different analytical tools.
Use data visualization tools for presenting the extracted information.

Data Science vs. Data Analytics: Core Skills Required

Some of the basic skills, both experts must know are discussed below.

A Data Analyst should

Know Excel and SQL databases.
Aware of tools like SAS, Tableau, and Power BI.
Know R or Python programming.

A Data Scientist should

Know Probability & Statistics.
Aware of Multivariate Calculus & Linear Algebra.
Well-versed in programming in Python, R, Java, Scala, SQL, MATLAB etc.

Conclusion

Businesses are gaining higher profits and growth using the insights obtained from the data gathered in the organisation. Hence there is a huge rise in job opportunities for data scientists and data analysts.

The work of Data Analytics is to process the available datasets and perform statistical analysis. It helps in solving real-time business issues from the data available. It presents the information in a visual format that is easy to understand. One of the important data analytics advantages is that it focuses on delivering outcomes for immediate improvements.

Data scientists look for answers and explore potential areas of study. Although they are less concerned with specific answers but emphasised finding suitable outcomes. They accomplish this by predicting potential trends, exploring disconnected data sources, and finding innovative ways to analyse information.