data science


Data Visualization Vs Data Science

Data Science refers to the process or art of interpreting data and creating useful information, whereas Data Visualization refers to the representation of data. Although both of them are different but are interlinked with each other, we can say that data visualization is a subset or part of data science. Let’s elaborate on the difference.


Basis Of DifferenceData ScienceData Visualization
1. MeaningData Science is the study of data and converting it into useful information.It is the process of translating large data sets into charts, maps, graphs, and other visuals.
2. Data SizeIt works on any size of data.It works on a massive amount of data.
3. GoalThe main goal of data science is to gain knowledge from raw data and analyze it to extract useful information.The main purpose of data visualization is to visualize data by representing it in pictorial form.
4. Professionals who perform it?Data scientists, Data Analysts, MathematiciansData Scientist, UI/UX
5. Tools & TechniquesTableau, TensorFlow, BigML, SAS, Apache Hadoop, MATLAB, Apache Spark, Excel, Jupyter, NLTK, Python, etc.Zoho Analytics, Domo, Sisense, Looker, Qlik Sense, Tableau, SAP, IBM Cognos Analytics, etc.
6. ProcessDefine business objectives, Collect the data, Data Cleaning, Data Analyzing, Building, and test models, Deploy Models, Monitor and Validate objectives.Exploration, analysis, synthesis, and presentation.
7. Skills RequiredStatistics and algorithmsData analysis, and plotting techniques.
8. ImportanceAlmost all organizations require data science to make better decisions.It helps data scientists to understand the data and how to solve the problem and represent it for providing recommendations.
9. ConceptData science implies multiple statistical solutions to solving problems.In this, a data scientist analyses data and represents it to the end result.
10. UsesIt is about training the machine by algorithms.It is about graphs, plotting, etc based on representation.

11. Examples


Manufacturing Industry: Producers depend on data science to create predictions for product demand.


Philosophy: explaining ideas by visuals depiction

Data Visualization by video

Cinema: Explaining the movie plot

12. Application In Real-Life

Fraud and Risk Detection

Internet Search

Targeted Advertising

Website Recommendations

Healthcare: Medical Image Analysis



Health Care Industries

Business Intelligence


Finance Industry


Real Estate Business




Although Data Science and Data Visualization have few differences, both of them are bounded by one major objective of extracting useful information. In data science, lots of techniques, tools, and skills are required to extract useful results whereas, in data visualization, tools are required to represent data in the form of visuals like graphs, charts, etc. The Data Science process is broader than data visualization. So, we can say that data science is a broader term as compared to data visualization. Both terms are different in their entities but data visualization is part of data science. And both are important for almost every organization for better decision-making.


Why Is Data Visualization Important In Data Science?

Data visualization is important as it discovers the trends in data. It gives a clear idea of what information means by presenting it in the form of visuals like graphs, charts, maps, etc. This makes data more comprehensible for the human mind and as a result, makes it easier to identify patterns in large datasets.

Data Visualization provides companies with clear insights into untapped information. No matter what field or business it is, data visualization helps all businesses by delivering data in the most efficient way. Data visualization takes the raw data, models it, and extracts the conclusions from it.

There are many reasons why data visualization is important in data science, here are a few listed below:

  •       Discovers The Trends In Data: It discovers the trends in data, as data visualization represents data in the form of visuals. It makes it much easier to observe data trends and their patterns.


  •       Converts Data Into More Interactive: Data Visualization allows the user to obtain a universal view of data by translating data into charts, graphs, usage of colors, shapes, etc. It tells a story to users and makes data interactive.



  •       Explains A-Data Process: It can be used to exhibit data processes from beginning to end. This can be done with the help of different charts.


  •       Saves Time:  It is a faster tool to gather perceptions from data. Data visualization saves a lot of time by creating insights from translated and easily comprehensible data.



  •       Presenting Data Beautifully: Primary purpose of data visualization is to easily impart information to users along with concentrating on beautifully presenting it so that viewers don’t lose interest in it.


All of the reasons mentioned above explain the importance of data visualization in data science. It demonstrates the trends and patterns of the data and presents it beautifully which makes it more appealing to people than just presenting data in the form of rows. Although data visualization is an element of data science, it plays an important role in modifying data and making it interesting, so that all viewers can get accurate messages of information extracted from raw data. At last, data visualization helps in representing data, and data science is extracting useful information by converting raw data by using various tools and skills, which help organizations in making better decisions to either solve problems or either to achieve its objective.


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Uriah  Dietrich

Uriah Dietrich


How To Build A Data Science Career In 2021

For this week’s data science career interview, we got in touch with Dr Suman Sanyal, Associate Professor of Computer Science and Engineering at NIIT University. In this interview, Dr Sanyal shares his insights on how universities can contribute to this highly promising sector and what aspirants can do to build a successful data science career.

With industry-linkage, technology and research-driven seamless education, NIIT University has been recognised for addressing the growing demand for data science experts worldwide with its industry-ready courses. The university has recently introduced B.Tech in Data Science course, which aims to deploy data sets models to solve real-world problems. The programme provides industry-academic synergy for the students to establish careers in data science, artificial intelligence and machine learning.

“Students with skills that are aligned to new-age technology will be of huge value. The industry today wants young, ambitious students who have the know-how on how to get things done,” Sanyal said.

#careers # #data science aspirant #data science career #data science career intervie #data science education #data science education marke #data science jobs #niit university data science

 iOS App Dev

iOS App Dev


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

'Commoditization Is The Biggest Problem In Data Science Education'

The buzz around data science has sent many youngsters and professionals on an upskill/reskilling spree. Prof. Raghunathan Rengasamy, the acting head of Robert Bosch Centre for Data Science and AI, IIT Madras, believes data science knowledge will soon become a necessity.

IIT Madras has been one of India’s prestigious universities offering numerous courses in data science, machine learning, and artificial intelligence in partnership with many edtech startups. For this week’s data science career interview, Analytics India Magazine spoke to Prof. Rengasamy to understand his views on the data science education market.

With more than 15 years of experience, Prof. Rengasamy is currently heading RBCDSAI-IIT Madras and teaching at the department of chemical engineering. He has co-authored a series of review articles on condition monitoring and fault detection and diagnosis. He has also been the recipient of the Young Engineer Award for the year 2000 by the Indian National Academy of Engineering (INAE) for outstanding engineers under the age of 32.

Of late, Rengaswamy has been working on engineering applications of artificial intelligence and computational microfluidics. His research work has also led to the formation of a startup, SysEng LLC, in the US, funded through an NSF STTR grant.

#people #data science aspirants #data science course director interview #data science courses #data science education #data science education market #data science interview

Ananya Gupta

Ananya Gupta


What Are The Advantages and Disadvantages of Data Science?

Data Science becomes an important part of today industry. It use for transforming business data into assets that help organizations improve revenue, seize business opportunities, improve customer experience, reduce costs, and more. Data science became the trending course to learn in the industries these days.

Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In online Data science course you learn how Data Science deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.

Advantages of Data Science:- In today’s world, data is being generated at an alarming rate in all time lots of data is generated; from the users of social networking site, or from the calls that one makes, or the data which is being generated from different business. Because of that reason the huge amount of data the value of the field of Data Science has many advantages.

Some Of The Advantages Are Mentioned Below:-

Multiple Job Options :- Because of its high demand it provides large number of career opportunities in its various fields like Data Scientist, Data Analyst, Research Analyst, Business Analyst, Analytics Manager, Big Data Engineer, etc.

Business benefits: - By Data Science Online Course you learn how data science helps organizations knowing how and when their products sell well and that’s why the products are delivered always to the right place and right time. Faster and better decisions are taken by the organization to improve efficiency and earn higher profits.

Highly Paid jobs and career opportunities: - As Data Scientist continues working in that profile and the salaries of different position are grand. According to a Dice Salary Survey, the annual average salary of a Data Scientist $106,000 per year as we consider data.

Hiring Benefits:- If you have skills then don’t worry this comparatively easier to sort data and look for best of candidates for an organization. Big Data and data mining have made processing and selection of CVs, aptitude tests and games easier for the recruitment group.

Also Read: How Data Science Programs Become The Reason Of Your Success

Disadvantages of Data Science: - If there are pros then cons also so here we discuss both pros and cons which make you easy to choose Data Science Course without any doubts. Let’s check some of the disadvantages of Data Science:-

Data Privacy: - As we know Data is used to increase the productivity and the revenue of industry by making game-changing business decisions. But the information or the insights obtained from the data may be misused against any organization.

Cost:- The tools used for data science and analytics can cost tons to a corporation as a number of the tools are complex and need the people to undergo a knowledge Science training to use them. Also, it’s very difficult to pick the right tools consistent with the circumstances because their selection is predicated on the proper knowledge of the tools also as their accuracy in analyzing the info and extracting information.

#data science training in noida #data science training in delhi #data science online training #data science online course #data science course #data science training

Gerhard  Brink

Gerhard Brink


Data Science vs Big Data: Difference Between Data Science & Big Data

In the digital era that we live in, data has become the biggest and most valuable asset for most organisations. Data is rapidly transforming the way we live and communicate, and it is by collecting, sorting and studying this data, that organisations across the world are looking for ways to impact their bottom lines.

When working with all terminology related to data, it is essential to have a clear understanding of the different scope of work related to it. In this article, we’ll discuss the differences between Big Data and Data Science. Though these terms are interlinked and often used interchangeably, there’s a vast underlying difference between them in all aspects.

Let us begin by defining the two terms.

Big Data is a standard way to define it is as an assortment of data which is too large to be stored or processed using the traditional database systems within a given period. A common misconception while referring to it is when the term is used to refer to data whose size of the volume is of the order of terabytes or more. However, it is a purely contextual term. For example, even a file of 250MB is Big Data in the context of an email attachment.

Data exhibits key attributes that must be taken into consideration when processing a dataset. They are most commonly known as the 5 Vs. Each of the Vs has specific implications in terms of handling them, but, when all of them are seen in combination, they present even bigger challenges.

#big data #big data vs data science #comparison #data science #difference between big data and data science