Data Analytics vs Data Science – Comparison

By TechFunnel Contributors - Last Updated on March 18, 2020
Data Analytics vs Data Science

The terms data science and data analytics are not unfamiliar with individuals who function within the technology field. Indeed, these two terms seem the same and most people use them as synonyms for each other. However, a large proportion of individuals are not aware that there is actually a difference between data science and data analytics.

It is pertinent that individuals whose work revolves around these terms or the information and technology industries, should know how to use these terms in the appropriate contexts. The reason for this is quite simple: the right usage of these terms has significant impacts on the management and productivity of a business, especially in today’s rapidly data-dependent world.

Having clarified why the disparity in data science vs. data analytics is necessary, the remaining part of this article will be enlightening you on the respective concept of these two terms and the major difference between data analytics and data science.

What is Data Science?

The concept of data science is without doubts very wide. It simply refers to the various means and processes by which information is derived for specific purposes. The concept involves science branches of mathematics and statistics, among other models that are employed in the evaluation and analysis of data. Therefore, in summary, any form of model or tool that is utilized in the derivation, processing, or/and analysis of data and information, can be categorized under the broader scope.

Data science is a very interesting subject matter that explores unknown data so as to study, understand, or develop useful patterns for the growth of a business. It is not all about the questions, rather it is about transcending to insightful discoveries by exploring newer innovations that were hitherto inconspicuous in a particular data.

(Also Read: What is Data Science? Everything You Need to Know)

What is Data Analytics?

In layman’s language, data analytics is simply a branch under the wider concept of data science. It has close ties with the concept of data science, but it is however more specific and narrowed. The job of data analysts is to focus on specific and deliberate goals while analyzing a data. Really, it is simply more concentrated and focused.

Data analytics involves an inquiry into a hypothesis with the primary objective of uncovering insights that would support and grow a business in a particular area. Data analysts are all about strategies that will impact an organization to materialize its targets.

(Also Read: The Big Hoopla Surrounding Big Data Analytics)

Differences Between Data Science and Data Analytics

Unlike data analytics which entails analyzing a hypothetical result, data science focuses on evaluating and manipulating results for a future purpose. The difference between in data analytics vs. data science will be discussed under 7 umbrellas below:

  1. Scope

    Data science is much broader in scope compared to data analytics. The scope involves the creation of questions concerning a data source.

    The scope of data analytics is narrow. In fact, it can be categorized under the umbrella of data science. It does not involve highly technical skills.

  1. Goal

    Data scientists thrive to evaluate the past patterns of data in order to project future insights and expectations.

    On the other hand, the primary goal of data analytics is to make meaningful inquiries into details that are initially hidden, so as to unravel and transform them into executable insights that are potentially practicable. Here, data analysts work to provide answers to the preexisting series of questions.

  1. Major Fields

    The most prominent fields involved in data science are machine learning, corporate analytics, search engine engineering, and artificial intelligence.

    Data analytics: the major fields here basically include various industries with urgent need of data, some of the fields are; travel agencies, gaming companies, healthcare providers and some others. The field of data analysts consists of operations analysts, sales analysts, database analysts, pricing analysts, market research analysts, international tactics analysts, and marketing and advertising analysts.

  1. Skillset

    Data science requires knowledge in the following skillset: mathematics, statistics, and hacking. It involves a knowledge base of calculating the abstract. A data scientist would be well grounded in programming, having reliable knowledge of Python, Scale, R, SAS, SQL database coding, machine learning, and other multiple analytical skills that demonstrate the capacity to analyze unstructured data from different numerous sources.

    Data analytics: a data analyst should have the skills of making in-depth inquiry into data while demonstrating a good understanding of math and statistics, PIG/ HIVE, Python and R, and data manipulation.

  1. Exploration

    Data Scientists explore modeling methods(1), creative algorithms, and data design so as to discover the necessary information that would be useful in solving the problems of a business or organization.

    On the other side of the coin, data analysts explore data systems and databases in order to find innovative solutions that drive the business forward.

  1. Using big data

    Data Science is involved in the gathering, retrieval, evaluation, and processing of huge amounts of data, collectively known as big data. Data scientists evaluate big data so as to model and produce custom analysis, algorithms and other data models.

    Data Analysts also evaluate big data. However, their evaluation is targeted at developing visual presentations that will enable an organization to make better tactical decisions.

  1. Interests

    The interests of data scientists slightly varies from data analysts. The interest of a data scientist would usually be in statistical evaluation

    On the other hand, the interests of a data analyst would usually tend to align along with the love of numbers, comprehensive analysis, and of course a liking for the business industry.

Final Thoughts

In conclusion, data science is just an incorporation of a number of different disciplines including data analytics, machine learning, data engineering, predictive analytics, artificial intelligence, corporate analytics and software engineering among others.

One major information to keep in mind is that data science and data analytics are both highly demanded for in the business industry today. They work hand in hand; complementing and consolidating upon the efforts of each other to give a desired result.

Data science vs data analytics are very important fields that are currently being explored to create a better future where data utilization is optimally efficient. Therefore, knowledge in either area can help you establish a lucrative career for yourself.

TechFunnel Contributors | is an ambitious publication dedicated to the evolving landscape of marketing and technology in business and in life. We are dedicated to sharing unbiased information, research, and expert commentary that helps executives and professionals stay on top of the rapidly evolving marketplace, leverage technology for productivity, and add value to their knowledge base.

TechFunnel Contributors | is an ambitious publication dedicated to the evolving landscape of marketing and technology in business and in life. We are dedicate...

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