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Key Differences Between Data Science and Statistics

Data in general


Ever since technology took a toll over all the major and minor spheres that constitute our personal and professional lives, data, and more precisely, big data has become the talk of the town. The most common perception of data that prevails in the general masses with an average technological acumen is that data is the byproduct that is produced in the trail of various technological processes that take place in various organizations and industries on a regular basis.
The rise and toll of Big Data has brought along certain associated terms and jargons such as data mining, data analytics, data statistics and data sciences. Since all these jargons and terminologies revolve around a common pivot of data, they are generally perceived as synonymous with each other, with little or no significant difference involved. This is but a viewpoint of the general and ‘non-tech geek’ masses.

Let the debate begin

Despite the general ambiguities that prevail over the concept, statistics and data sciences is always a matter of an interesting debate in the domains of economics, management information, and data technology. Here are some interesting takes on the ongoing debate between statistics and data sciences that we came across on social media:

“A data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician.”
“A data scientist is a statistician who lives in San Francisco.”

Define them

In order to understand what exactly is statistics and data science and how essentially they differ from each other, let’s push aside all humor and ambiguities and peek into the standard definition of the concept:
Statistics and Data Science, Defined!


The Investopedia defines statistics as:
“Statistics is a form of mathematical analysis that uses quantified models, representations and synopses for a given set of experimental data or real-life studies. Statistics studies methodologies to gather, review, analyze and draw conclusions from data.”

On the other hand, Investopedia explains data science as:
“A field of Big Data which seeks to provide meaningful information from large amounts of complex data. Data Science combines different fields of work in statistics and computation in order to interpret data for the purpose of decision making.”

A comparative analysis of both the terms makes it clear why the concepts have become intertwine and indistinguishable. However, a deeper and more scrutiny clearly indicates that data science is a relatively broader domain and statistics constitute an essential fragment of data science.

Big one, small one

Since the concepts are interdisciplinary, data scientists use statistics as a fundamental and handy problem-solving tool. In the simplest connotation, data science is the bigger picture, while statistics is a small but essential and significant component of it. Therefore, to render data sciences as equivalent to statistics is to disparage the expanse of the domain.


Statistics is a comparatively confined entity with its special tools such as regression analysis, mean, median and variance analysis, frequency analysis, kurtosis and skewness to name a few. Data science, on the other hand, makes use of the tools, techniques and principle models of various other correlating disciplines, including statistic, to accumulate data, and sift it through to categorize into proper data sets. In the next step, the data science examines and scrutinizes the data, and deduces factual, quantitative as well as statistical inferences. These inferences are translated into strategic interpretations and provide a solid foundation for the decision making process.


Considering the more extensive host of operations involved in data science as compared to statistics, the essential skill set required by a data scientist and a statistician also varies and differs.

A statistician needs a grip over:

  • the fundamental tools and core principles of statistics

That they can use to make a numeric and quantitative analysis.

A data scientist, on the other hand, has to analyze and simplify complex data problems, look for credible and relevant insights and figure out the opportunities lurking within the problem at hand. To pull off this responsibility, it is essential for a data scientist to have a grip over:

  • arithmetical,
  • technological,
  • visualization,
  • interpretation,
  • analytical,
  • relevant computer skills.

This extensive range of skills, in a combined effect, enable the data scientist to integrate, incorporate and interpret data sets, illustrate data models and squeeze out practical and realistic analysis and interpretations.

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