In this article, we’ll walk you through the world of Data Visualisation. We’ll begin by understanding what is Data Visualisation, after which we’ll see the actual need of DV tools and some of the common Data Visualisations used in practice today. Going further, we’ll talk about the essential tools you must be aware of if you’re setting foot in the world of Visualisation of Big Data.
But before we get to that, let’s get you to understand the importance of Data Visualisation using a very common example. Take a look at the images below:
Which of the above two arrangements makes it easier for you to browse through all the books quickly and efficiently? The second one, isn’t it? That’s the power of visualisation. Now, think a step further. In our example, we were just looking at a handful of books. In the real world, on the other hand, the problem of visualisation is HUGE. There’s so much data with the organisations at present that it’s impossible to make sense of it without proper representation of it all. That’s exactly where Data Visualisation and its tools come in!
By now you’ve understood what exactly is Data Visualisation. Yet, for the sake of a formal definition, here it goes: Data Visualisation is, quite simply, the process of converting huge datasets into concise and unequivocal patterns and shapes (graphs, charts, scatter plots, and such things) to make it easier for people to understand it. Data Visualisation can be carried out in many modes, depending on the requirement. Some of them are – Graphs, Columns, Venn Diagrams, Pie charts, Network/Colour Maps, Trees, Frequency Polygons, Box-and-whisker plots; Line, Surface, and Volume Scatter Plots and so on.
Data Visualisation: Need of the hour!
Now that we know what Data Visualisation is, let’s try and understand why it is the “need of the hour”. We’ve understood it helps organisations get insights into their data – now, let’s see how!
-
Helps the organisation absorb data quickly:
Your Big Data will look gibberish to your organisation if you don’t present it in a concise and understandable way. As you know, a picture is worth a thousand words – or, in this case, worth a gazillion lines of data. Presentable display of data will help all the verticals of your organisation understand the data with utmost ease. That, in turn, will allow them to absorb the data better – without having to spend a lot of time on it.
-
Helps you plan your next steps better:
Think of DV as solving a jigsaw puzzle. If you have a thousand puzzle pieces, it’s quite a task to get going with arranging the pieces. But once you have even half of your pieces in place, you can easily figure out the next steps. Likewise, from these visual trends, you can easily figure out your next best steps without wasting too much time or energy on data analysis. You can save a lot of time and money by looking at the big picture, instead of trying to look at a thousand puzzle pieces.
Get Started in Data Science with Python
-
Get your audience interested in your data:
Nowadays, people have the attention span shorter than that of a goldfish. Keeping that in mind, it’s important for you to present your audience with something that they can grasp quickly – even with a cursory glance. Converting your data into graphics engages your audience as they now feel in control of the situation as they can understand the representation as opposed to understanding the whole datasets – “Graphs? That sounds good!”
-
Find the outliers in your dataset:
This is probably the most important use case of Data Visualisation. It helps you quickly find out the outliers, if any, in your datasets. If you get down to imagining, you’ll realise this is indeed a challenge without proper visualisation. Outliers tend to drag down data the averages in the wrong direction, so, it’s essential to find and eliminate them from your analysis when they skew the results. Graphics always make it easier to understand the presence of an outlier and take any required steps against it.
-
Act quickly on your findings:
Visualisation of data in the form of graphics helps you in making much faster decisions. By using Data Visualisations, you can review your strategies, make updates, and achieve success – all of this without wasting a lot of time and energy. Analyzing the graphical representation of any dataset will allow you to act better on your findings as compared to analyzing the whole dataset.
Exploratory Data Analysis and its Importance to Your Business
Ten Data Visualisation Tools You Should Master
QlikView
QlikView markets itself as a “business discovery platform”. Its ability to process data in-memory makes it a perfect tool for quick-and-dirty processing of data. Talking about sources, QlikView can read data from almost any source – from CSV files to SQL databases. It also performs data integration (combination of data from various sources) and generates composite data sources for better analysis. QlikView targets businesses that are looking to get deeper insights on the data generated by their endeavours.
Explore our Popular Data Science Certifications
Our learners also read: Top Python Courses for Free
upGrad’s Exclusive Data Science Webinar for you –
Watch our Webinar on The Future of Consumer Data in an Open Data Economy
Tableau
Tableau, too, is a business intelligence tool for visual ananlysis of data. It allows users to create and distribute a very intuitive dashboard which depicts all the variations, trends, and density of the data in for on charts or graphs. Tableau can read data from files, relational databases, and Big Data sources. Its unique feature is that it allows real-time collaboration. It’s put to use by academic researchers, businesses, and many government organisations.
Wolfram Alpha
You can’t talk about numbers, statistics, and visualisations without mentioning Wolfram Alpha. It is an open source statistics search/calculation engine which can also produce beautiful, informative, and customizable representations in the form of charts and graphs If you are using publicly available data in your analysis, the charts generated can very easily be uploaded to your website using widgets.
The What’s What of Data Warehousing and Data Mining
Top Data Science Skills to Learn
| SL. No | Top Data Science Skills to Learn | |
| 1 | Data Analysis Programs | Inferential Statistics Programs |
| 2 | Hypothesis Testing Programs | Logistic Regression Programs |
| 3 | Linear Regression Programs | Linear Algebra for Analysis Programs |
MS-Excel
We often forget the old warhorses in our search for specific tools. How can we talk about data visualisation and not mention the classic MS-Excel? Chances are, you’ve had some experience with Excel, irrespective of your background. Excel has stood the harsh test of time and is still extensively used. You must be aware of the famous Spreadsheet visualisation.
Excel can turn out to be quite a powerful tool – almost as powerful as the other mentions, if the requirements don’t go beyond the basics. However, a major drawback of Excel is that customised data visualisation is difficult, thus it’s a bad candidate for work that has specific requirements.
Read our popular Data Science Articles
CartoDB
All the other tools in this list talk primarily about processing quantitative data. Now suppose you have to integrate this data with maps? CartoDB is the tool you need. It allows seamless integration of data in tabular form with maps. To see the magic, you can upload a CSV file containing a list of addresses to CartoDB and it’ll convert them to latitudes and longitudes and plot them on a map. The only disadvantage is that you need to pay for it after using it for 5 times.
Apart from the tools mentioned above, there are some other tools, too, that deserve a mention:
- MatPlotLib: It is a multi-platform library built for Data Visualisation using Python.
- ChartBlocks: ChartBlocks is a web app that lets you create beautiful, customizable, and shareable charts – you can also download them as vector graphics.
- Charted: Charted automatically builds beautiful charts, you just need to provide it with the link to your data file.
- D3.JS: It is a Javascript library that helps you build visualisations using HTML and CSS.
- Dygraphs: It is a fast, open-sourced Data Visualisation library provided by Javascript.
Learn data science courses from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
Wrapping Up…
If you were paying attention, you’d have realised that data visualisation is by no means a “new” technology. We’ve been doing it for ages – take the example of a 2-D cartesian plane, or the 3-D coordinate system, which is a visualisation of data as well. It’s just that businesses are waking up to the need for Data Visualisation in context of Big Data Analytics.
So, if you’re looking to begin a career in Big Data, mastering Data Visualisation is sure to take you a long way! check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
![Data Mining Techniques & Tools: Types of Data, Methods, Applications [With Examples]](/__khugblog-next/image/?url=https%3A%2F%2Fd14b9ctw0m6fid.cloudfront.net%2Fugblog%2Fwp-content%2Fuploads%2F2020%2F04%2F437.png&w=3840&q=75)
![17 Must Read Pandas Interview Questions & Answers [For Freshers & Experienced]](/__khugblog-next/image/?url=https%3A%2F%2Fd14b9ctw0m6fid.cloudfront.net%2Fhttps%3A%2F%2Fec2-3-7-241-34.ap-south-1.compute.amazonaws.com%2Fblog%2Fwp-content%2Fuploads%2F2020%2F07%2F780.png&w=3840&q=75)
![Most Common Binary Tree Interview Questions & Answers [For Freshers & Experienced]](/__khugblog-next/image/?url=https%3A%2F%2Fd14b9ctw0m6fid.cloudfront.net%2Fugblog%2Fwp-content%2Fuploads%2F2020%2F12%2F1529.png&w=3840&q=75)
