Data science and data analytics major similarities and distinctions (1)

Copyright © Global Tech Council www.globaltechcouncil.org
DATA SCIENCE AND DATA ANALYTICS: MAJOR
SIMILARITIES AND DISTINCTIONS
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Copyright © Global Tech Council www.globaltechcouncil.org 3
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.
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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.
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What is Data Analytics?

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.
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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.
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Difference between Data Science and Data Analytics

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.
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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.
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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.
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Let Us Take A Brief Look At The Differences Between
Data Science & 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.
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● 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.

Conclusion
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.
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