Five Major Points of Difference Between Data Science and Data Analytics

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The world is incomplete without data. A humongous amount of data is generated by users every day. What if the same data is analyzed and interpreted to capture what users want and make innovations accordingly. It could bring a revolution where businesses can provide state-of-the-art solutions to the problems faced by a common man, and that too, at low costs. There are various types of data that helps in running and upgrading business models to a great extent.

The article will explore and talk about the two popular verticals of computer science that we hear every day – Data Analytics and Data Science.

Let’s understand about it on a lighter note –

The huge amount of data that is collected from various sources is called big data. Next, when the data is filtered for the relevant target group, statistical models are applied, and future decisions are predicted basis the current data, that data now falls in the category of data analytics. It helps in performing statistical analysis for finding the answers to business problems. Whereas when we talk about machine learning, predictive analysis, and visualization, it is about data science.

Both the terms, data analytics and data science, sound very familiar to each other but are a lot different. For a clear idea, the table below will illustrate how both the terms, data science and data analytics, are related and yet different.

Data analytics

It is quite clear from the above paragraph that there is a lot of data collection, which is further analyzed to derive business benefits. Such an analysis of the fetched information and gaining meaningful insights for solving a business problem is called data analytics. It involves various steps through which data is analyzed and revamped –

  • Determining the data and grouping the ones with a similar nature. The groups could be based on specific concerns or a business problem. Data can be grouped in any manner that is most appropriate. For example, in terms of age, location, interest, lifestyle, gender, etc.
  • Collecting data from different sources, such as online and offline – social media, physical surveys, and computers.
  • Undergoing the organization of data for further analysis. Spreadsheets are one of the most common methods to organize data.
  • Lastly, incomplete, inconsistent, and duplicate data is removed and cleaned before analysis. Also, errors in the data are corrected, and it is readied for analysis.

Data analytics is becoming more important in all the major domains, which include finance, retail, tourism, healthcare, and hospitality. The data is exploited, mined, and modified to provide descriptive, predictive, prescriptive, and diagnostic analysis.

Data science

The term data science is broader when compared to data analytics. Data analytics is contained in data science and is one of the phases of the data science lifecycle. Things that happen before and after analyzing the data is the part of data science. Also, unlike data analytics, data science combines statistical and domain knowledge to produce insights from data that drastically improves business.

It has three main components, namely, statistics, machine learning, and data visualization. The components help in the analysis, interpretation, and presentation of data for easy understanding and comprehension. It also helps in making quicker decisions by highlighting the key takeaways.

Now, let us take the table view of data analytics and data science.

Tabular differentiation of data analytics and data science

Data ScienceData Analytics
The scope of data science is said to be macro.The scope of data analytics is said to be micro.
The aim of data science is to find and define business problems that lead to innovation.The problem is already known, and with analytics, the analyst tries to find the best solution to the problem.
The main components of data science are search engine exploration, artificial intelligence (AI), and machine learning.Data analytics is concerned with the usage of statistical tools and analytical techniques.
It is used in internet research, image recognition, speech recognition, and digital marketing.It is used across domains such as healthcare, travel and tourism, finance, and gaming.
The input is raw or unstructured data, which is cleaned and organized to be further sent for analytics.The input is structured data on which design principles and visualization techniques are applied for further analysis.
It is one of the highest-paid fields under the subject of computer science.Though the job is well paid, it is pretty less than that of a data scientist.
Data science involves finding solutions to new and unknown problems. It happens in discovering them and converting the data into stories and use cases.The data doesn’t lead to a road or solution to the problem. It only helps in doing analysis and interpretation from the raw data.

To sum up

Though there is certainly a lot of difference, both data science and data analytics are an important part of the future of work and data. A company should look forward and embrace both the terms if they want to lead the technological change and understand their data to make organizations run. The role played by each of these terms is capable enough to bring a revolution in the working and growth of an organization.

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