Data Visualization- A Need Of SEO

Data Visualization- A Need Of SEO

<ul> <li>To be able to realize how creatively you may display different varieties of data, there are a number of brilliant data visualization tools which are up to the job of supplying amazing outcomes. It permits you manage your data and makes...

  • To be able to realize how creatively you may display different varieties of data, there are a number of brilliant data visualization tools which are up to the job of supplying amazing outcomes. It permits you manage your data and makes it feasible for you to keep an eye on your cross-channel campaigns from a certain spot. The data ought to be used for predictions, internal alterations, and executive decisions otherwise, it's merely a bunch of pretty numbers. Whether structured data affects rankings has become the topic of much discussion and several experiments.

  • It's possible to choose the remaining 5 books once your initial 4 books are reviewed and accepted. You can opt to review more than 1 book per week if you prefer. There are several Blogpost SEO guides that you'll find on The web, and a lot of Them are associated with Template editing and all, but in WordPress, plugins make it simpler to optimize your Blog.

  • Brands interact with their clients, and their clients interact with one another. Companies need to find opt-in consent when personal information is collected, or any time a consumer will be reached. For instance, a Business might utilize U.S. Census data to produce decisions about marketing campaigns. In the event, the business isn't located closeby, it may simply cease to exist. Video Marketing is by far one of the utmost effective advertising tactics. PPC advertising supplies a cost-effective method to boost an advertisement online whilst still netting a substantial profit. Your site content would be offered with unique contents that are related to your company.

  • If your site (a single page isn't a website) is new (under a year old), you should make some compromises about the sort of Keywords you can begin your optimization process with. Pages which are linked from other search engine indexed pages do not have to be submitted since they are located automatically. Your site is your property. There are a lot of things you should be aware of in regards to Marketing your site or company online. Search engines exist for users to obtain the information that they require. Thus, the kinds of searches they've been conducting require the brands to be a lot more responsive than before.

  • Search engine optimization is the ideal way to find traffic organically, but is just 1 facet of Online Marketing. Sometimes SEO is only a matter of creating sure your Website is structured in a manner that search engines understand. SEO isn't a proper strategy for every single site, and other online advertising strategies can be more effective like paid advertising through pay-per-click (PPC) campaigns, based on the website operator's goals. Search engine optimisation is not any different than every other skill the excellent results will always come from big work. Search engine optimisation is the magic you've got to work on your article, so as to make Google very very likely to include your article among the very best results whenever someone searches for that keyword.

  • There are a number of tools it is possible to utilize to research keywords, but among the keyword research tools which has to the absolute most up-to-date and trustworthy data is the Google AdWords Keyword Tool. There are many tools available that permit you to capture interested leads. Always time-box your task, nothing ought to be longer than one hour, you are going to be too dull after that. Fortunately, there are tons of information Visualization tools which may help. Naturally there are tools to observe your degree of brand awareness and total public opinion, but they are able to cost a lot (for the features you really need), and to find the absolute most out of them you still need to clock a couple of hours a day using them. There are many tools you'll be able to utilize to optimize your site to be a registration generation locomotive.

  • The professionals at SEO Manager Would provide you with with the appropriate ideas and strategy to have your nearby business listed on the cover of the Search Engine list. You must also optimize a couple of things in order for an excellent search engine experience. You get insights into the amount of visitors which are engaging in your content and your site. You will be comforted by their Perspective and you will get input that boosts your being.

  • Chatbots Now you can with the assistance of chatbots, programs which exist within messaging apps including Kik or Facebook Messenger. Attempting to navigate through the newest Developments and advances in technology can be rather hard. Although a number of them might seem familiar and already used by you, they help evaluate different competitors on the Market Lastly, the quantity of total links you have does of course matter also, and you will need to over timebuild high high quality backlinks at scale. Now you have a notion of the basics of SEO, I'll have a look at several of its components in detail.

Data Visualization and its different tools

Data Visualization and its different tools

Data Visualization is one of the most talked about areas of technology. It is increasingly becoming a major component of Big Data. Big Data, as we all know, is the data that is generated by the www. Let us give you some perspective of the [data...

Data Visualization is one of the most talked about areas of technology. It is increasingly becoming a major component of Big Data. Big Data, as we all know, is the data that is generated by the www. Let us give you some perspective of the data generated all over the cyberworld:

o Google receives more than a trillion hits in searches every year
o Figures about devices are startling, to say the least: there will be some 50 billion connected devices by the next decade or so, with each of them collecting, sharing and analyzing data
o Using this data is the biggest challenge for organizations around the world. If they get this technique right, their revenue can shoot up by at least two thirds
o If a Fortune 1000 company boosted its data utilization capabilities by just a tenth, it could enhance its revenue by an average of $ two billion.

Now, what do organizations do with this knowledge? It is one thing to sit back and be amazed at this phenomenal output, but another to use it judiciously for improving businesses. Wouldn’t it be better off if organizations were fed this data in a manner that is both easy to understand, as well as insightful? What if the huge quantities of raw data were presented in an easy to understand fashion, and are also packed with the right levels of vision? This is the area of Data Visualization.

So, the mindboggling numbers should drive home the importance of Data Visualization. But for Data Visualization, all these data would be cryptic and obscure, offering companies little by way of understanding. Data Visualization goes about its work by using certain tools. Let us examine a few of these Data Visualization tools:

1. Tableau: With its rich array of features such as drag and drop and updates in real time, Tableau continues to be among the most popular Data Visualization tools. add to it the fact that it is highly interactive, and you understand why so many businesses from around the world, right from startups to multinationals, use it so extensively to represent data.

2. Sisense: Sisense is known for its dashboards and other usability features which make reading data very easy and attractive. It has the robustness to perform data analysis on high amounts of data. Plus, it is known for its very reliable support.

3. Google Charts: Ideal for mobile devices and browsers, Google Charts has an array of charts with which the user can customize according to her needs.

4. Infogram: Infogram is intuitive, and it helps the user create engaging infographics and reports in virtually no time and helps create beautiful data.

5. Power BI: Microsoft’s Power BI is another highly popular Data Visualization tool that helps users explore and analyze data both in premises and on the cloud, all in one view. It enables users to collaborate and share dashboards and interactive reports with ease, and can be scaled very easily.

Closing thoughts

Now that we have given you some idea about the tools used for Data Visualization, we want to draw your attention to how you can start becoming a Data Visualization professional right away! Simpliv offers online courses in Data Visualization that you can take up and start taking the first steps to a lively and rewarding career in Data Visualization.

These courses, being designed by industry experts, offer you tremendous insights into how to make Data Visualization work in a very efficient manner, so that you or your organization derives maximum value from your work!
And, before signing off, do let us know if you liked this blog and what else you would have liked us to write in this!

Data Science vs Data Analytics vs Big Data

Data Science vs Data Analytics vs Big Data

When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them

When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them. In this article on Data science vs Big Data vs Data Analytics, I will understand the similarities and differences between them

We live in a data-driven world. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Now that Hadoop and other frameworks have resolved the problem of storage, the main focus on data has shifted to processing this huge amount of data. When we talk about data processing, Data Science vs Big Data vs Data Analytics are the terms that one might think of and there has always been a confusion between them.

In this article on Data Science vs Data Analytics vs Big Data, I will be covering the following topics in order to make you understand the similarities and differences between them.
Introduction to Data Science, Big Data & Data AnalyticsWhat does Data Scientist, Big Data Professional & Data Analyst do?Skill-set required to become Data Scientist, Big Data Professional & Data AnalystWhat is a Salary Prospect?Real time Use-case## Introduction to Data Science, Big Data, & Data Analytics

Let’s begin by understanding the terms Data Science vs Big Data vs Data Analytics.

What Is Data Science?

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.


It also involves solving a problem in various ways to arrive at the solution and on the other hand, it involves to design and construct new processes for data modeling and production using various prototypes, algorithms, predictive models, and custom analysis.

What is Big Data?

Big Data refers to the large amounts of data which is pouring in from various data sources and has different formats. It is something that can be used to analyze the insights which can lead to better decisions and strategic business moves.


What is Data Analytics?

Data Analytics is the science of examining raw data with the purpose of drawing conclusions about that information. It is all about discovering useful information from the data to support decision-making. This process involves inspecting, cleansing, transforming & modeling data.


What Does Data Scientist, Big Data Professional & Data Analyst Do?

What does a Data Scientist do?

Data Scientists perform an exploratory analysis to discover insights from the data. They also use various advanced machine learning algorithms to identify the occurrence of a particular event in the future. This involves identifying hidden patterns, unknown correlations, market trends and other useful business information.

Roles of Data Scientist

What do Big Data Professionals do?

The responsibilities of big data professional lies around dealing with huge amount of heterogeneous data, which is gathered from various sources coming in at a high velocity.

Roles of Big Data Professiona

Big data professionals describe the structure and behavior of a big data solution and how it can be delivered using big data technologies such as Hadoop, Spark, Kafka etc. based on requirements.

What does a Data Analyst do?

Data analysts translate numbers into plain English. Every business collects data, like sales figures, market research, logistics, or transportation costs. A data analyst’s job is to take that data and use it to help companies to make better business decisions.

Roles of Data Analyst

Skill-Set Required To Become Data Scientist, Big Data Professional, & Data Analyst

What Is The Salary Prospect?

The below figure shows the average salary structure of **Data Scientist, Big Data Specialist, **and Data Analyst.

A Scenario Illustrating The Use Of Data Science vs Big Data vs Data Analytics.

Now, let’s try to understand how can we garner benefits by combining all three of them together.

Let’s take an example of Netflix and see how they join forces in achieving the goal.

First, let’s understand the role of* Big Data Professional* in Netflix example.

Netflix generates a huge amount of unstructured data in forms of text, audio, video files and many more. If we try to process this dark (unstructured) data using the traditional approach, it becomes a complicated task.

Approach in Netflix

Traditional Data Processing

Hence a Big Data Professional designs and creates an environment using Big Data tools to ease the processing of Netflix Data.

Big Data approach to process Netflix data

Now, let’s see how Data Scientist Optimizes the Netflix Streaming experience.

Role of Data Scientist in Optimizing the Netflix streaming experience

1. Understanding the impact of QoE on user behavior

User behavior refers to the way how a user interacts with the Netflix service, and data scientists use the data to both understand and predict behavior. For example, how would a change to the Netflix product affect the number of hours that members watch? To improve the streaming experience, Data Scientists look at QoE metrics that are likely to have an impact on user behavior. One metric of interest is the rebuffer rate, which is a measure of how often playback is temporarily interrupted. Another metric is bitrate, that refers to the quality of the picture that is served/seen — a very low bitrate corresponds to a fuzzy picture.

2. Improving the streaming experience

How do Data Scientists use data to provide the best user experience once a member hits “play” on Netflix?

One approach is to look at the algorithms that run in real-time or near real-time once playback has started, which determine what bitrate should be served, what server to download that content from, etc.

For example, a member with a high-bandwidth connection on a home network could have very different expectations and experience compared to a member with low bandwidth on a mobile device on a cellular network.

By determining all these factors one can improve the streaming experience.

3. Optimize content caching

A set of big data problems also exists on the content delivery side.

The key idea here is to locate the content closer (in terms of network hops) to Netflix members to provide a great experience. By viewing the behavior of the members being served and the experience, one can optimize the decisions around content caching.

4. Improving content quality

Another approach to improving user experience involves looking at the quality of content, i.e. the video, audio, subtitles, closed captions, etc. that are part of the movie or show. Netflix receives content from the studios in the form of digital assets that are then encoded and quality checked before they go live on the content servers.

In addition to the internal quality checks, Data scientists also receive feedback from our members when they discover issues while viewing.

By combining member feedback with intrinsic factors related to viewing behavior, they build the models to predict whether a particular piece of content has a quality issue. Machine learning models along with natural language processing (NLP) and text mining techniques can be used to build powerful models to both improve the quality of content that goes live and also use the information provided by the Netflix users to close the loop on quality and replace content that does not meet the expectations of the users.

So this is how Data Scientist optimizes the Netflix streaming experience.

Now let’s understand how Data Analytics is used to drive the Netflix success.

Role of Data Analyst in Netflix

The above figure shows the different types of users who watch the video/play on Netflix. Each of them has their own choices and preferences.

So what does a Data Analyst do?

Data Analyst creates a user stream based on the preferences of users. For example, if user 1 and user 2 have the same preference or a choice of video, then data analyst creates a user stream for those choices. And also –
Orders the Netflix collection for each member profile in a personalized way.We know that the same genre row for each member has an entirely different selection of videos.Picks out the top personalized recommendations from the entire catalog, focusing on the titles that are top on ranking.By capturing all events and user activities on Netflix, data analyst pops out the trending video.Sorts the recently watched titles and estimates whether the member will continue to watch or rewatch or stop watching etc.
I hope you have *understood *the *differences *& *similarities *between Data Science vs Big Data vs Data Analytics.

Best 10 JavaScript Charting Libraries for Every Data Visualization Need

Best 10 JavaScript Charting Libraries for Every Data Visualization Need

If you're interested in learning how to visualize your findings, read on for a list of great data viz libraries to help get you started. In this article, you'll see best 10 JavaScript charting libraries for every data visualization need

If you're interested in learning how to visualize your findings, read on for a list of great data viz libraries to help get you started. In this article, you'll see best 10 JavaScript charting libraries for every data visualization need

Nowadays, the amount of data grows exponentially, and the more information we see, the harder it gets to process it. That’s why we need data visualization — in charts and dashboards, preferably interactive. It helps us humans save a lot of time and effort to view, analyze, and understand data, and make the right, informed decisions based on that.

In the modern HTML5 web, one can hardly deny that JavaScript is the most versatile and simplest technology to make use of for visualizing data. So if you are a front-end web developer, you either already know what JS charts are all about, or your first task to make them will come in a (short) while.

There are numerous JavaScript charting libraries out there, each with their specific pros and cons as with any tools. To make your life easier, I decided to tell you about my favorite ones. I think the following ten are the best JS libraries for creating charts and can be really helpful in solving one or another particular data visualization task, whether it’s basic or advanced. Follow me and check them out to make sure you know the basics about them and have not missed out on some good one for your current or next big project.

Without more ado, let’s go meet the top JS libraries for data visualization!


amCharts is one of the JavaScript charting libraries that are helpful when you need a simple, and at the same time, flexible data visualization solution.

Key features

  • A pretty big number of chart types including maps and Gantt charts.
  • A drill down feature along with other great interactivity options.
  • Documentation containing all the needed methods is quite well-written, but from my point of view, it’s not really convenient to use.
  • Awesome chart animation.
  • Can be integrated with React, Angular, Vue, Ember, etc.
  • A WordPress plugin is available.
  • Export as an image or PDF.
  • Live charts, full customization, and W3C-approved accessibility functions.
  • Full support with priority one for licensed customers.
  • Customers: Microsoft, Amazon, eBay, NASA, Samsung, Yandex, AT&T, etc.


Free for any use, but all charts will include a small, branded link. To remove the link, you need to purchase a paid license (from $180), which also gives you access to priority support.

Learn more about amCharts


AnyChart is a robust, lightweight and feature-rich JS chart library with rendering in SVG/VML. It actually gives web developers a great opportunity to create any different charts that will help to make decisions based on what is seen.

Key features

  • More than 80 JS chart types, including basic charts, stock charts, maps, as well as Gantt and PERT charts.
  • Many ways to set data: XML, JSON, CSV, JS API, Google Sheets, HTML Table.
  • Drill down into chart data.
  • Stock technical analysis indicators and annotations (drawing tools) out-of-the-box.
  • Rich documentation, API, and friendly support.
  • Can be integrated with Angular, Qlik, Oracle APEX, React, Elasticsearch, Vue.js, Android, iOS, etc.
  • A lot of samples and dashboards and a dedicated playground with code autocompletion.
  • Old browsers support.
  • Exporting charts to various formats including PDF; JPG, PNG, or SVG images; chart data in XSLX or CSV files.
  • Customers: Oracle, Microsoft, Citi, Samsung, Nokia, AT&T, Ford, Volkswagen, Lockheed Martin, etc.


The watermarked version is free. To get rid of the branding, as well as to use AnyChart for any commercial purpose, it’s necessary to buy a license (from $49).

Learn more about AnyChart


Chart.js is a simple yet quite flexible JavaScript library for data viz, popular among web designers and developers. It’s a great basic solution for those who don’t need lots of chart types and customization features but want their charts to look neat, clear and informative at a glance.

Key features

  • 8 chart types: line, area, bar, pie, radar, polar, bubble, and scatter.
  • All chart types can be customized and animated, and when used online, all charts are responsive.
  • Functionality can be extended through the use of plugins.
  • Documentation is good.
  • Support via Stack Overflow.
  • Browsers support IE9+.


A free open-source JS charts library. Released under the MIT license.

Learn more about Chart.js


Chartist.js is an open-source, unintrusive JS library which can also be used to create nice responsive charts. Generally, Chartist is good for those who need a very simple chart — line, bar, or pie — and who do not require much in terms of data visualization. Good appearance, no need to have many great features in this case.

Key features

  • Only 3 chart types: line, bar, and pie charts.
  • Great animation.
  • API documentation contains all the necessary information, but it’s not quite convenient to use, requiring long scrolls to navigate.
  • Allows using plugins to extend the functionality.
  • Uses SVG to draw the charts (future compatible).
  • Old browsers support.


Open source, free for all kinds of use.

Learn more about chartist.js


D3.js is a powerful open-source JavaScript library for data visualization. It has been forked more than 20,000 times on GitHub so far. Basically, D3 is more like a framework than a library. It may well be not that simple to work with, which can look quite critical at the beginning. But there are a lot of helpful information resources available out there. And at the end, you can get so awesome visualizations and graphics of any kind from scratch, making D3 totally worth it.

Key features

  • Supports numerous chart types, much more than the vast majority of the other JavaScript charting libraries (e.g. Voronoi diagrams).
  • Steep learning curve. Less clear and obvious than some commercial libraries on the list (for example, AnyChart). But there are many tutorials, and the API is truly awesome.
  • Combines powerful visualization components and a data-driven approach to DOM manipulation.
  • Easy to debug using the in-browser element inspector.
  • Hundreds of examples.
  • Curve generating functions.
  • Drag and drop.


D3 is an open source JavaScript library for charts, which is free for all kinds of use.

Learn more about D3.js


FusionCharts is another good interactive charting library with hundreds of charts ready for use out of the box. The charts accept both JSON and XML data formats and are rendered via HTML5/SVG or VML.

Key features

  • Dozens of chart types, in both 2D and 3D, and 950+ maps covering all continents.
  • Animated and fully interactive charts and maps.
  • Server-side APIs for ASP.NET, PHP, and Ruby on Rails.
  • Compatible with jQuery, Angular, PHP, ASP.NET, React Native, Django, React, Ruby on Rails, Java, etc.
  • Quite detailed user’s guide and API reference.
  • A lot of samples and dashboards to check out.
  • Old browsers support.
  • Export to PNG, JPG or PDF format.
  • Support via the knowledge base and community forum.
  • Unlimited priority support for license holders.
  • Customers: Apple, IBM, Google, Intel, Microsoft, PayPal, Oracle, Adobe, etc.


Free for non-commercial, paid for commercial use (from $497).

Learn more about FusionCharts

Google Charts

Google Charts is an excellent choice for projects that do not require complicated customization and prefer simplicity and stability.

Key features

  • The charts are based on HTML5/SVG and VML.
  • A lot of samples and dashboards to check out.
  • All charts are interactive, and some are pannable/zoomable as well.
  • Comprehensive documentation.
  • Old browsers support.
  • Support via FAQ, GitHub, and forum.


The license is free, but the library is not open source. It does not allow you to host Google’s JS files on your server, so it may not suit you if you have some sensitive data.

Learn more about Google Charts


Highcharts is one of the most comprehensive and popular JavaScript charting libraries based on HTML5, rendering in SVG/VML. It is lightweight, supports a wide range of diverse chart types, and ensures high performance.

Key features

  • Uses pure JavaScript, and data can be loaded externally.
  • Robust documentation, API reference, and community showcase.
  • Drill down into chart data and other interactivity options.
  • Can be used with React, Angular, Meteor, .NET, iOS, etc.
  • Export to PNG, JPG, PDF, or SVG format.
  • Supports free version users via forum and Stack Overflow, with premium email and Skype support only available for commercial users with the appropriate license.
  • Customers: Visa, Yahoo!, Facebook, Twitter, Groupon, Nokia, Ericsson, Mastercard, Yandex, etc.


Free for use by nonprofits. Paid for commercial use (from $50).

Learn more about Highcharts


Plotly.js is a high-level JavaScript library, free and open-source. It is built on D3.js and WebGL, so can be used to create many different chart types including 3D charts to statistical graphs.

Key features

  • 20 chart types that can be embedded in websites or used to create dynamic presentations.
  • Used as a browser-based charting library for Python, R, and MATLAB by abstracting charts to a declarative JSON structure.
  • Extensive API documentation.
  • Good animation.
  • Uses React.
  • Exporting charts to PNG and JPG; EPS, SVG, and PDF are available on subscription.
  • A lot of different samples to check out.
  • Allows using Excel spreadsheets, or connect to your database.
  • Support forum.


Open-source, free library.

Learn more about Plotly.js


ZingChart is a helpful tool for making interactive and responsive charts. This library is fast and flexible, and allows managing big data and generating charts with large amounts of data with ease.

Key features

  • Supports more than 30 chart types.
  • Fully customizable with CSS inspired styling.
  • Compatible with jQuery, Angular, Node.js, PHP, etc.
  • Real-time data, fast rendering of data sets of any size.
  • Data can be loaded via JS objects, JSON, CSV, PHP, AJAX, or MySQL.
  • Full yet quite easy-to-read API.
  • Free and premium support via ZingChart help center, Stack Overflow, email, and chat.
  • Customers: Microsoft, Boeing, Adobe, Apple, Cisco, Google, Alcatel, etc.


The branded license provides full access to the ZingChart library for free. Commercial usage requires a paid license (from $199).

Learn more about ZingChart


I have listed the best JavaScript charting libraries out there, at least those I consider the top ones. It would be hard to compare all of them comprehensively. Each one of them has its own pros and cons depending on who is going to use it and for exactly what purpose.

Of course, there are some features that make one library faster, more beautiful or flexible than the other. But in the end, no matter what libraries this list contains, the overall winner is always the one that meets your specific requirements. For different people and companies, the choice of the best JS chart library can also be different.

My advice is — check out these top libraries as whenever you need JS charts and for whatever project, chances are extremely high that you will find one or several of them to be the best fit. For a longer list, look at a comparison on Wikipedia.