Data Science and Data Analytics: Major Similarities and Distinctions

Those working in the field of technology hear the terms ‘Data Science’ and ‘Data Analytics’ probably all the time. These two words are often used interchangeably. Big data is a major component in the tech world today due to the actionable insights and results it offers for businesses. In order to study the data that your organization is producing, it is important to use the proper tools needed to comprehend big data to uncover the right information. To help you optimize your analytics, it is important for you to examine both the similarities and differences of data science and data analytics.


What is Data Science?


Data science is a blend of various algorithms, tools, and machine learning principles that operate with the goal of discovering hidden patterns from raw data. It is used to make decisions and predictions by using prescriptive analysis, predictive causal analysis, and machine learning. It is used to scope out the right questions from the dataset. It is a multidisciplinary field that works at the raw level of data (structured, unstructured, or both) to make predictions, identify patterns and trends, build data models, and create more efficient machine learning algorithms. Data science experts work in the realm of the unknown. Some of the data science techniques are regression analysis, classification analysis, clustering analysis, association analysis, and anomaly detection.


What is Data Analytics?


Data analysis or data analytics is the process of applying statistical, logical, and analytical techniques to data sets for discovering information that will aid in making informed decisions. A data analyst uses tools such as data mining, textual data analysis, and Business Intelligence (BI). The information gathered through data analysis is highly dependent on the quality of the data. Data analysis is driven by business goals. Data analysis curates meaningful insights from past data and is generally not used for predictions.


Similarities of Data Science and Data Analytics


Both are significant parts of the future of work and data. It is important for companies to embrace these two terms as these are the main driving forces when it comes to the smooth functioning of business operations and if they wish to be the forerunners of technological change. Both are the future of the data-driven world. Data analysis is a subset of data science. Both work towards gaining bigger outcomes for the business or society, and both work with big data. Both data scientists and data analysts must be familiar with the business. In both the fields, you need a background in mathematics and statistics, and programming skills in languages such as SQL, Python, HADOOP, and R. Both these fields are growing and lucrative.


Difference between Data Science and Data Analytics


Though both these terms are used synonymously and are interconnected, they pursue different approaches and deliver different results. Data science can first be differentiated based on scope. Data science is an umbrella term covering a group of fields used to mine large datasets. Data analytics, on the other hand, is a smaller part of the larger process of data science. Data analytics is more focused and is devoted to realizing actionable insights that can be immediately applied based on existing queries.


Data science and data analytics can also be distinguished, based on exploration. Data science does not answer specific queries but instead deals with analyzing massive datasets in unstructured ways to expose insights. Data analysis works more efficiently when it is focused. Data science produces broader insights that concentrate on the questions to be asked, whereas data analytics gives importance to discover the answers to the questions being asked. Data science is more focused on asking questions, rather than finding the answers.


Data science refers to connecting information and data points to find connections to be made that will be useful for business. It deeply analyzes the world of the unknown. Instead of checking a hypothesis, it builds connections to plan for the future. It moves an organization from inquiry to insights. It provides a new perspective on the data.


Data science refers to the house that holds the tools and methods, and data analytics refers to a specific room in that house. Though it is related and similar to data science, it is more specific and concentrated. Instead of only looking for connections among data, data analysts have a specific goal to sort through data with the aim of finding ways to support. Data analytics helps sort out data into things that organizations know they don’t know, and organizations know they know to measure the past, present, and future events. It moves data from insights to impact. It is more focused on business and strategies.


Let us take a brief look at the differences between data science and data analytics that stand out.

  • Data science is used to formulate the right questions, and data analytics is used to solve questions coming from a business perspective.


  • In data science, the data for analysis is prepared by processing, massaging, cleansing, and organizing the data. Data analysis helps mine data to identify patterns and discover correlations.


  • Data science uses data from several datasets for solving real-world problems. Data analytics identifies data quality issues and uses a single data set.


  • A data science expert must have the creativity to create a story from the data. A data analyst needs to find straightforward answers to questions.


  • Tools used in data science are Python, SPSS, SAS, R, Hadoop, Matlab, Scala, and Hive. The tools of data analysis are SQL, HTML, JavaScript, etc. The data visualization tools used are Spotfire, Tableau, and QlikView.


  • Typical uses of data science are weather prediction, gaming, dynamic pricing, personalized marketing, fraud detection, mental health research, etc. Loyalty programs, recommendation engines, targeted advertising, etc. are typical uses of data analytics.





Data science and data analytics are the two fields that are in great demand today. If you love decoding big data and believe that you have an analytical mindset, you can consider a career in data science or data analysis as these jobs are in high demand today. When we think of these two disciplines, it is important to think of them as parts of a whole that are vital not only to understand the information we have but also to analyze and review it.


To know more about data science and big data certifications, check out Global Tech Council.