Data Science vs Data Analytics

A data scientist is someone who can predict the future based on past patterns whereas a data analyst is someone who merely curates meaningful insights from data. To know more about their differences read the blog.

Updated by Theres Ann on 28th January 2019

The terms Data Science and Data Analytics are two streams of the trending domain of information extraction. With an increasing population, an increase in the number of gadgets, and an improvement in technology, the information-extraction, and information-manipulation industries have bagged a high position among the most trending jobs in the Information technology. Though the terms data science and data analytics are related, they are two different concepts.

  • When different methods and models of data extraction are used then the methodology is placed under data science. Intelligent methods are used for data extraction, and the information retrieved helps in deriving insights for business growth. Data science is in a way a future planner. The derivation of an organization's insights is provided by the data science wing of the organization. 

  • Data analytics is a part of data science and is a much-refined stream of data science. Analytics involves creating actionable insights for a variety of questions pertaining to the data.

 What is Data Science?

The collaboration of data inference, algorithm development, and technology is known as data science. Using the available data in creative ways to generate business insights from it is known as data science. The revelation of data insights, along with smart business decisions can steer the growth of the organization. A few of the fields in which data science is as given below.

  • The entire digital marketing spectrum works on data science. The targeted customer is found out from the internet search behavior of the user. Moreover, the higher Click-through-Rates of digital ads are due to the impact of data science in analytics.

  • Recommender systems are another example of the usage of data-science in audience-targeting. Based on the past search history of the user, recommendations are made for products and services.

  • The face-recognition algorithm used by Facebook is another implication of data science in social media. The reason why your face gets recognized for tagging is the clearest representation of how vast the data-science domain has grown.

  • Machine learning algorithms of data science are also used in the gaming industry today. There are games that analyze the skill of the user to upgrade itself to the next level.

  • Data science has also helped in marketing, finance, human resources, health-care, government policies, and many more. The impact of data science keeps on growing day by day. 

Source: Analytics India Magazine

What is data analytics?

The qualitative and quantitative techniques to enhance productivity and business gain is combinedly called as data-analytics. It is most relevant in a Business to the Consumer application. Most organizations big or small, archive data that is associated with business processes, customers, market trends, available practical experience and many more. Data related to user preferences, community interests, segments and more are grouped based on categories in the organization. 

  • Security, transportation, fraud, and risk detection, risk management, and delivery logistics are fields that clearly uses data analytics.

  • Smart spending, customer interactions, city planning, health care, travel and many more also use data-science to implement their use of data analytics. 

  • Personalized travel management, energy management, web search, digital advertisement also use data analytics to improve productivity.

Source: Base

Skills required for a data scientist

The necessary skills for a data scientist are as given below.

  • In-depth knowledge of SAS and/or R.

  • Programming skills in Python

  • Knowledge of the Hadoop platform

  • Knowledge of database and coding

  • Ability to work with unstructured-data

Skills  required to become a data analyst

A few skills required to become a data analyst are as follows.

  • Programming skills

  • Statistical skills

  • Mathematical skills

  • Machine learning skills

  • Data wrangling skills

  • Communication skills

  • Data visualization skills

  • Data intuition 

Source: Doxim

Data science techniques

A few of the data science techniques are as listed below.

  • Linear regression

  • Logistic regression

  • Jackknife regression

  • density estimation

  • confidence interval

  • test of hypothesis

  • Pattern recognition

  • clustering

  • supervised learning

  • time-series

  • Neural Networks

  • Support Vector Machines

  • Search engine

  • Attribution Modelling

  • And many more

Source: Analytics India Magazine

Data Analytics Tools

A few of the data analysis tools are as given below.

  • Excel

  • Trifecta

  • Rapid Miner

  • Rattle GUI

  • QlikView

  • WEKA

  • Knime

  • Orange

  • Data Wrapper

  • Data Science Studio

Data science roles

A few of the data science roles are as given below

  • Data Researcher

  • Data Developer

  • Data Creative

  • Data Business People

Source: Seattle Data Guy

Data Analytics roles

A few of the data analytics roles are as given below

  • Data Architect

  • Database Administrator

  • Analytics Engineer

  • Operator

Responsibilities in data-science

The work-pattern and responsibilities of a data science professional are as given below.

  • Unlock the value of data to become the critical data analyzer.

  • Cleaning, processing and organizing data for analysis

  • Identifying business questions that can add value

  • creation of analytical methods and the generation of machine learning models

  • Identification of root causes of observed results

  • Data narration and visualisation

Responsibilities in Data Analytics

A few routines and responsibilities of a data-analytics professional is as given below.

  • Creation of SQL queries to answer complex questions

  • Analysis and mining of business data for the identification of patterns from various data points.

  • Identification of data quality issues 

  • Mapping and tracking of data across various systems

  • Gathering incremental new data

  • Generation of reports to facilitate better decision making

  • Application of statistical analysis


When data science is used in web development, digital ads, e-commerce, internet search, finance, telecom and utilities, data analytics is used in traveling, transportation, financial analysis, retail, research, energy management, and research. Though the terms are different their underlying principles are similar and aims at improving the development strategies of an organization. Though the tasks of both the domains are quite similar, data scientists earn a higher salary than data analysts.


  How to become a data scientist?

A few followable steps to become a data scientist are as follows.

  • Learn to work on data

  • Learn by trying to process data: cleansing, manipulating, deriving insights

  • Learn to identify the insights

  • Try to increase the degree of difficulty

  How is Statistics used by data scientists?  

Data scientists use statistics in a number of ways. They are as given below.

  • To validate decisions for designing and interpreting experiments

  • To create models that perform decisions

  • To derive interpretable models from big-data

  • To understand user-engagement, conversion, retention and, leads

  What yields better data science or data analytics?

Data scientists are paid better in the industry.

  Differentiate between data science and data analytics.

Data Science - Involves the usage of data along with algorithms

Data Analytics - Contains techniques used for deriving insights from data

  Which are a few analytics tools?

A few analytics tools are as follows.

  • R

  • Tableau

  • Python

  • SAS

  • Apache Spark

  • Excel

  • RapidMiner

  • Knime

  • QlikView

  • Splunk

  Name a few data science tools.

Tools for data scientists include data analytics tools, data visualization tools, database tools and more. A few of the tools used in data science are as given below.

  • Apache Giraph

  • Apache Hadoop

  • Apache HBase

  • Apache Hive

  • Apache Kafka

  • Apache Mahout

  • Apache Pig

  • Apache Mesos

  • And many more