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Data Science Visualization Tools: How to Select the Best Data Visualization Tools [Updated 2020]

What do you understand by Data Visualization?

A graphical representation of data and information is known as Data Visualization. In this, visual elements, such as graphs, charts, and maps, are used to offer an easy and accessible way of understanding and analyzing trends, patterns, and outliners in the data. On the whole, data visualization and data science are not two distinct entities. They are both bound together and are complementary to each other. In short, Data Visualization is nothing but a subset of Data Science.


How to pick the best visualization tools for data science?

Many a time, organizations run into challenges, which relate to relevant communication. If the organizations have the best visualization tools for data science, they can overcome any such challenge. These best data science visualization tools are incredibly helpful and help overcome all plausible situations of under communication, bad communication, or miscommunication.


Now, how can you pick the right and the best data visualization tool? There are a bunch of factors, which are involved in picking the right data science visualization tools.


  • Firstly, you need to start by identifying the needs of your organization.
  • This is important because it will help you in the second step, wherein you’ll compare the needs of your organization with the capabilities of the different data science visualization tools.
  • After you have worked this out, it would be easier for you to find the desired solution, i.e., the best visualization tool for data science for your organization. It would also make it easier for you to proceed further.


It is important to understand that just like any other tool selection, in data visualization, too, there’s not one tool, which will be perfect for all organizations. However, to find the best visualization tool for data science, you need to take out a moment and answer a few questions. Here are some questions which you have to consider:


Step 1: What are the different questions, which your users are asking?

Now, let’s start with the basics – why do you want the best data visualization tool? Is it your technique to find ongoing trends? Do you wish to use it to satiate the operational reporting needs of the business? Many times, businesses don’t even know the reason why they are using visualization tools.


The primary categories, which distinguish between these questions are:


  • Relationship
  • Distribution
  • Composition
  • Comparison


Now, if you wish to put forth the comparison over time, you’ll use line charts, area charts, or column charts.


More specifically, if you wish to put forth an evolution, you should use a vertical column chart, as, in it, the time can be expressed from left to right. So, in this case, horizontals wouldn’t do you any good.


Similarly, if you have to compare different items, then you can use a bar chart, table, or spider chart.

Stacked column charts or stacked area charts are ideal when you have to express the relationship of the part to the whole.


Step 2:  Identify the Objective

Now, before you start with the visualization process, do ensure that the objective of this visualization is known to you. It will make it easier for you to determine how many dimensions or values you need. This, in turn, will give you a better direction in choosing the best data science visualization tool. Further, it works best when you try to comprehend what the users wish to see.


Step 3:  Tailor the tool choices according to needs of the audience

The primary skill for any data scientist is the ability to communicate the results in a manner that they are easily understood by the stakeholders. Since the data projects are collaborative across different functions and the results of the data science are usually incorporated in a larger project, the real impact of the work of the data scientist primarily depends on how well others can comprehend the insights to take the following actions.


Step 4:  Pick tools, which minimize visual bloat and maximize clarity

Both in your writing, as well as in your day to day life, it helps to be aware of the cooperative communication principles by Grice. It involves the following:


  • Maxim of quantity – At all times, we should be informative as much as is required. However, in no way should we speak more or less than what is required.
  • Maxim of quality – As a speaker, we should always speak the truth or only put forth things, which can be proven by adequate evidence.
  • Maxim of relation – The response that you give out should be relevant to what’s being asked.
  • Maxim of manner – Do not ever be obscure or ambiguous. Whatever you say has to be clear and straightforward.


Step 5:  Pick tools, which give more value to your data

Now, under this step, you have to pick the best visualization tools for data science, which helps you put forth the data in the most meaningful format. To accentuate the effectiveness of your communication, this step should certainly be on your mind with regards to the selection of the data science visualization tools as well as through the analysis process. You need to determine the communications format, which will be helpful for the audience to ensure that your communication inspires action and does not go in vain.


Is your audience going to physically carry with them a fact sheet or an executive summary whenever they move from one customer site to the next? Are they going to remove the chapter or pages from a report, which best relates to the research? See, the purpose of the entire information depicted by the best data visualization tools is to determine the format of the piece. Don’t forget we live in a distinctively visual age, and hence visualization tools for data science are important to give value to the data.


Step 6:  Distinguish between exploratory graphics and presentation

The presentation graphics prepared by the best data visualization tools are much like the proofs of the theorems in mathematics. They do not give any clue on how this result was reached, but they do have to offer a convincing outlook as to how this conclusion was reached.


On the other hand, the explanatory data science visualization tools are to look for the outcomes. A bunch of these could be used, and at all times, they are supposed to be informative and fast and not precise or slow. These are not meant for presentation. Hence, you don’t have to put forth any lengthy captions or detailed legends.


Moreover, the primary difference between the presentation graphic and the exploratory graphic is that the former data science visualization tool will be drawn for viewing by thousands of different readers, while the latter data science visualization tool will be drawn complementary to the different data investigations of a single analyst.


Step 7:  The form of the data visualization must follow its function

The visualization, which you create with your best data visualization tools, would depend on:


  • The properties of the data
  • The questions that you intend to ask
  • How you would like to communicate and present the insights to the audience.


For instance, representing the growth in sales in each year would require a distinct data science visualization tool than depicting the connection between discounted items and the profits they fetch you. Hence, the knowledge of what you aspire to depict will determine the type of visualization tools for data science used by you.


Step 8:  Try to strike a balance between the needs and the functionality

Now, the drill of determining the best visualization tools for data science would need a certain balance between the analyst needs and the proportionate technical requirements. It would help determine the requisite training schedules and examine whether or not you should add components to the current technical architecture to cater to all the business needs. At times, you’ll find that your stipulated needs tend to compete with each other or rather put forth contradictory ideas in the represented visualization tools for data science. These should be eliminated.


Step 9:  Opt for best data visualization tools, which combine different data sources and factors nicely

Besides the trends over time, the metric values comprise distinct parts or components. For instance, revenue might be derived from different types of sales leads or various products. Being able to use the best data science visualization tools for the visualization of how the different pieces contribute to the absolute performance is the next vital step.


Step 10:  Search for features, such as dynamic data and animation

When you are looking for the best visualization tools for data science, there are certain things that you need to be careful about. It is so because picking the right visualization tools for data science will help in achieving optimal functionality and deliver excellent ease of usage. Analysts from Forrester, Noel Yuhanna, and Boris Evelson recognized six distinct traits, which help in identifying advanced tools from the use of the simple static graphs. These traits comprise visual querying, dynamic data, animation, linked multidimensional visualization, actionable alerts, and personalization.


Step 11:  Pick right colors for representation

In your used best data science visualization tools, it is important to be careful with the colors. The colors used in the best data science visualization tools should be indicative of whether something is important or not. It is believed that colors play an important role in attracting the attention of the user. For instance, you can use the “alarm colors” such as color red to represent a decline in the sales in your best data science visualization tools. Similarly, the color green can be used in the visualization tools for data science as a sign of the target achievement in the marketing campaign.


Step 12:  Try to keep it as simple as possible

In the visualization tools for data science used by you, you should curtail yourself from depicting a lot of information in the single visualization. You would not want your user to keep searching for their answer. The answer to their question should be just right there. In case, you would want your user to be able to see more information than what is available up-front, you have to make a setting in your best visualization tools for data science, wherein, your user can click onto the drill-down function from where they can enable this.


Best data science visualization tools

Here, we have come up with a list of the 8 of the best data science visualization tools, which every Data Scientist must be aware of and should certainly use them in their routine depending upon their specific visualization needs.


1. Tableau

At number one in our list of the best data visualization tools for data science is Tableau. So, how to use Tableau for data visualization? Before we get to an important question on how to use Tableau for data visualization, let’s understand a bit about Tableau.


Tableau is a visualization tool for data science that is often believed to be the most powerful tool when it comes to business intelligence. With Tableau, you can manage massive and extensive data sets, which are incorporated in the fields, such as machine learning, artificial intelligence, and business intelligence.


The good thing about this data visualization tool for data science is that they have a customer link around several IT organizations because of their utmost simplicity in solving the problems related to the data. Further, they are easy to use for the users who have enough knowledge on how to use Tableau for data visualization.


Now, let’s get to the big question – how to use Tableau for data visualization? With Tableau, you can import almost every size of data, and it makes it easier for you to manage the metadata. In this, the data is extracted from distinct sources across different platforms. The data is connected with the help of a data desktop and is published onto the server of Tableau. Using the Tableau reader, the user is better equipped at both reading and viewing the file. Tableau has a bunch of data connectors, and it serves a massive community of users. Hopefully, this clears your doubts on how to use Tableau for data visualization, and now you’ll be better equipped at using this data visualization tool for data science.


2. Power BI

Second data visualization tools for data science, which enjoys equal, if not more, popularity is the Power BI. So, how to visualize data in power BI? Again, we’ll first give you a bit of background about Power BI before we help you understand how to visualize data in power bi.


Power BI is a cloud-based business analytics tool from Microsoft, which helps you analyze and visualize the data with better efficiency and speed. It is an incredibly powerful and flexible tool, which can be used for connecting and analyzing a variety of distinct data. Many data scientists and businesses regard it as indispensable, especially when it comes to the work related to data science.


So, now, let’s answer the big question – how to visualize data in power BI? Well, representation of data with the use of Power BI is easy, and it is so because of its simple and easy to use drag and drop feature. With this feature, performing tasks, such as comparing, sorting, and analysing, is incredibly fast and easy. Power BI is a great visualization tool, which is compatible with an array of sources, such as SQL Server, Excel, and cloud-based data repositories. This makes it the number one choice for Data Scientists. So, hopefully, now you know how to visualize data in power BI.


3. Qlikview

Now, after learning how to visualize data in power BI, let’s move on to the next visualization tool, which is Qlikview. It is brought to you by Qlik and is an easy and simple tool, which helps put your business in absolute control. Using this tool, you can easily visualize, search, consolidate, and analyze the different data sources for extracting useful insights about the business.


It is an exceptionally useful tool to get answers to the most difficult business questions in no time. This one is certainly one of the most used tools and has always received top reviews and rankings in terms of performance, customer loyalty, quality, and features. Qlikview also offers a consolidated BI platform along with incredibly insightful demos, tutorials, and training manuals. All of this helps the users to get familiarized with the tool quickly. Now, let us move on to the next tool, and learn how to visualize data in kibana.


4. Kibana

Now, before we get to how to visualize data in kibana, let’s understand a few basics about it. For many businesses or entrepreneurs, Kibana is nothing short of a dream come true. It is an ideal tool for anyone who wishes to visualize their data in the most accurate, efficient, and quick manner. So, let’s get to the key question – how to visualize data in kibana. With this program, you get a good deal of freedom, and that makes it easy for the users to select the manner in which they would want the data to be displayed. Hence, anyone who doesn’t even know how to visualize data in kibana can easily figure out the same, owing to the interactive visualizations that are offered by this program.


With Kibana, you get line graphs, histograms, pie charts, geospatial map displays, sunbursts, along with a bunch of other common alternatives of visualization. Furthermore, you also have an option to create your unique option for visualization. With Kibana, it is also easy for the users to analyze or spot the relationships in Elasticsearch data and detect and explore anomalies in the Elasticsearch data with various machine learning features. So, hope that clears your doubt on how to visualize data in kibana.


5. Plotly

Plotly is an open-source and interactive visualization tool with only a few lines of code to write. It is much like the Python charting library. So, now, let us understand how to visualize data in python?


Plotly is a vivid open source script that is easy to modify. The objects of the Plotly offer layout components and data components. With this visualization tool, you get high-level API wrappers, which certainly helps you save some time.


With this tool, you get alternatives, such as statistical tools, graphics, along with an easy to use interactive python library, which will make it easier for you to understand how to visualize data in python. The tool is built on the plotly.js, which is a JavaScript library. All the charts and the graphs of the tool bring forth spectacular visualization, which will work incredibly for presentations. The python library of Plotly employs the use of declarative programming along with an absolute framework for easy implementation.


6. Sisense

It is a business intelligence-based data visualization tool that offers different tools, which help the data scientists and data analysts to uncomplicate the complex data and attain useful insights for both the outsiders and the organization. The founding fathers of Sisense believe that with time, every company out there will be data-driven, and every product will be related to data in one way or the other.


It is for this reason why it tries incredibly hard to offer different data analytics tools to both business teams and the data analytics to help them make their companies the data-driven companies of the future. The biggest benefit of Sisense is its ease of learning and installation.


It wouldn’t take you over a minute to set up the tool. Moreover, for data analysts, it is easy to get their work done and attain results in a quick and hassle-free manner. With this tool, the users can easily export their files in different formats, such as PDF, MS Word, PPT, MS Excel, etc.  Another benefit of this tool is that, with it, you get 24/7 customer support services. So, anytime there’s an issue, you can contact the team, and they’ll help you with your issue. To need a price quotation, all you need to do is submit a form.


7. Domo

The next data visualization tool is Domo. It lets you see through the data from different corners of your business in an easy-to-use platform. However, it does not end there. Domo is exceptionally incredible and connects directly to the data exactly where it lives. Furthermore, it nourishes critical metrics with real-time information. All of this helps you make quicker and more well-informed information. So, how to visualize data in Domo?


Build yourself a dream dashboard

With the Domo dashboard, you can pull all the critical information into an intuitive and a single dashboard.


See the bigger picture

Now, you have to see through the data from all sources in a singular platform. This will help you make better and more informed decisions.


Automatize Reporting

With Domo, you no longer need to cobble together reports manually rather you can easily enjoy automatized reporting in real-time metrics.


Go Mobile

With the mobile application of Domo, you get a seamless experience, irrespective of wherever you are.


Now, let us move to the last tool, which is the MS Excel, and understand how to visualize data in excel.


8. Excel

Today everybody around the world is using MS Excel. So, naturally, you don’t need to know how to visualize data in excel, because that’s something you are well aware of. There isn’t any analytic professional out there who isn’t familiar with Excel.


In all honesty, a lot of data scientists are using Excel for their day to day tasks. The prime benefit of Excel is that it is just everywhere. Another significant thing about Excel is that almost everyone knows how to visualize data in excel. So, whenever you wish to share some work with somebody via Excel, you don’t even have to think about whether they’d be able to open the file or not, because it is quite obvious that they know how to visualize data in excel.


Furthermore, ad-hoc tasks, such as tweaking a number here and there is also easy with Excel. Everything with this visualization tool is simple and transparent. You can easily see through what is happening in different cells. Hence, given the fact that you know how to visualize data in excel, you could certainly start with this epic data visualization tool.