Data Science VS Data Analytics.


Data Science and Data Analytics sound similar and are often interchangeably placed in a different scenario.  Even though being two sides of the same coin, there is a vast difference between them and have different implications. Knowing the difference between these terms is of utmost importance as this will help you to place yourself in the industry in a better way. As you are already aware, Big Data is growing with rapid pace, becoming a part of our everyday lives.

So if you aspire to step in the field of Big Data and gain name and fame, here are the major differences between Data Science and Data Analytics.

What is Data Science?

Data Science is a broad term enveloping models and methods to gain information. It mainly comprises of maths science statistics modeling and other tools that are used to analyze and manipulate data. Data Science dwells in the world of the unknown, finding answers to questions that are known and unknown. In a more precise manner, Data Science tries to build connections and plan for the future.

Role of a Data Scientist

A Data Scientist is a professional who understands data from a business point of view. He is in charge of making predictions to help businesses take accurate decisions. Data scientists come with a solid foundation of computer applications, modeling, statistics, and math. What sets them apart is their brilliance in business coupled with great communication skills, to deal with both business and IT leaders. They are efficient in picking the right problems, which will add value to the organization after resolving it. Data engineers are responsible for setting up the database and storage to facilitate the process of data mining and other processes.

Scope of Data Science

Data Science helps in company growth by providing with accurate data and assists in the research. Companies these days are largely dependent on Data Scientist to analyze data and provide insight in order to derive solutions to complex problems. Skills in mathematics, statistics, and programming are used to clean and organize data. Data Scientists are responsible to convert raw data into meaningful information that can render fruitful results. Eye to detail and future prediction and analysis of data are the key responsibilities of a data scientist.

Characteristics of a Data Scientist

1-    Understanding of the Organizational Behaviour: A Data Scientist should not only be well equipped in software like Python and Hadoop but should have a keen understanding of business dynamics. Apart from being technically uplifted, he should be a good communicator and innovative. He should have the ability to leverage data and deliver accurate results.

2-    Intellectual Inquisitiveness: The main role of a Data Scientist is not to deal with numbers alone or indulge completely in technology but to carefully blend both of them. Their motivation is to be able to use creativity and ingenuity to solve tough problems as they indulge in intense intellectual curiosity

3-    Communication skills: It is important for a data scientist to be able to clearly and fluently engage in conversations with members of non-technical teams to be able to communicate their technical findings. Only with strong communication skills, can a data scientist enable businesses to make logical decisions that are backed by quantified data.

What is Data Analytics?

    Data Analytics is the process of examining data sets in order to draw conclusions about the information, they contain with the help of specialized systems and software. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.  Data analytics focuses on coming to conclusions based on what the analyst already knows.

Role of Data Analysts

 Data Analyst interprets data and turns it into information which can offer ways to improve a business, thus affecting business decisions. Data Analysts gather information from various sources and interpret patterns and trends. A Data Analyst is responsible for collecting and interpreting data. He deals with analyzing the results and reporting it back to the relevant members of the organization. Moreover, A Data Analyst is innovative and works alongside the business to establish   patterns and trends in data sets

Scope of Data Analytics

Data Analysis helps in deriving insight and provides solutions to problems. While Data Science reveals the problems and creates questions, data analysis provides an answer to those questions and encourages actionable results. Big Data Analytics aids an organization to tackle the data and utilize it to find new opportunities. This leads to more smart business moves, higher profits, efficient operations, and happy customers. The idea is to share the business prospects in a better way in the future and to use it with analytics concept.

Characteristics of Data Analysis

1. Enhancing Efficacy

All the data collected by the business is not only related to the individuals external to the organization. Most of the data collected by the businesses are analyzed internally. With the advancements in technology, it is become very convenient to collect data. This data helps to know the performance of the employees and also the business.

2. Better Market Understanding

With the development of algorithms nowadays, huge datasets can be collated and analyzed. This process of analysis is called Mining. Regarding the other kinds of physical resources, data collection is done in raw form and thereafter refined. This enables the collection of data from a wide variety of people, which further proves out to be fruitful for better marketing strategy.

3. Diminishing Cost

Big data technologies like cloud-based analytics and Hadoop can bring huge cost advantages if it relates to the storage of large data. They can also identify efficient ways to do business. You not only save money in terms of infrastructure but too, save on the cost of developing a product which would have a perfect market-fit.

4. Better Decision-Making

The high-speed in-memory analytics and Hadoop in combination with the ability for analyzing the new data sources, businesses can analyze the information almost instantly. This comes out to be a big time-saver as you can now deliver more efficiently and manage your deadlines with ease.


Data Science and Data Analytics are most in demand domains in the industry right now.  An amalgamation of right skill sets and right pragmatic exposure can help you secure a strong career in these trending domains.

I would suggest that you chose your profile based on your strengths:

  • Data Scientists require good understanding of tools such as Python / R / MATLAB.
  • Data Analyst needs to be good in SQL and Scripting Languages.

I hope this blog has helped you gain an understanding of data science and its connected fields.

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