Best Data Visualization Tools for 2021
Data has enveloped the entire global market, every trade that happens at the global market has a relevant connection to data. Today companies are investing heavily in individuals who are capable of extracting and analyzing this vast ocean of data to draw strategies and help companies gain an edge over the competition. This article will help you to identify what are the best data visualization tools in 2021.
The foundation of any profession starts with strong basics and just like that the foundation for any successful understanding and mastery of data analytical tools starts from understanding the programming languages and to the tools which work in tandem with each other which are involved in the process or functioning of a good data analytics system.
Below are a few of the Best Data Visualization Tools for 2021 that are used in the field of data science and data analytics and how the knowledge of these programs is in high demand.
Probably the most popular and one of the best data visualization tools for 2021 is the programming language of the century, Python is a general-purpose coding language which unlike other coding languages like HTML and CSS which can only be used for web development, Python is a universal coding language which can be used in multiple domains like back end development, software development, data science/data analytics and writing scripts among other things.
Due to Python’s simplicity and readability, it boasts of a gradually low-end learning curve. This ease of learning makes it an ideal tool for many Data Scientist & Data Analyst. Python allows users to use fewer lines of coding and achieve tasks than the ones needed while using other languages. Python is open-source which means it is free and uses a public-based paradigm for advancement.
Python is an integral part of data analytics as its fabricated to carry out repetitive functions and anyone who has worked with large or big data understands the monotonous role of repetition and the importance of automating it. With a programming tool to bear the brunt of the repetition task data analyst can focus on the other curious and satisfying sections of work.
Knowledge of Python also entails knowing the multiple libraries that are present such as NumPy, Pandas, and Matplotlib which help data analysts to automate the repetitive functions involved when dealing with big data and are important tools that come in handy post-competition of a certified course in Python from Analytics Training Hub.
Below is a google trend report of Python’s steady climb to popularity over the last decade
Another one of the best data visualization tools for 2021 R Language is an open-source programming language developed by Mr. Roass Ihaka & Mr. Robert Gentleman in the summer of 1993. The language was named after the initials of their names and its most stable version was released in 2018.
R language was developed keeping statistical computing and graphics supported by the R foundation. The language is prevalent amongst number crunchers & data miners for data analysis polls, data mining surveys, and many other important functions.
Data visualization is the visual representation of data in infographics form. This allows analyzing data from angles that are not clear or in an unorganized or tabular format. R has many tools that can help in data visualization, analysis, and representation of complex data and showcase it in its true form.
Below is a google trend graph showcasing the popularity of the R language over the past 5 years.
R language has always been in demand and in the future, the need for the R language seems to be a requirement by several IT giants.
MS Excel always considers the best data visualization tool and again this is going to be one of the best data visualization tools for 2021. MS Excel is a spreadsheet application that was developed by Microsoft for iOS and windows in the years 1987 and 1988 respectively. With the introduction of VBA (Visual Basic for Application) also known as Macros in the v5.0 for Windows, endless possibilities in the automation of repetitive tasks like number crunching, automation, and presenting data for business became a smooth process.
MS Excel is a powerful spreadsheet that allows the user to do almost any level of data manipulation and analysis. There are some basic and advanced excel functions like LOOKUP FUNCTIONS, PIVOT TABLES, SLICERS, etc that helps the user to manipulate data. MS Excel is capable of handling extremely large data sets with ease. Hence it is suited for the job of a data analyst.
Excel has some great visualization options that graphically help in present information. There are basic charts like LINE graph, Column chart, etc as well as some advanced charting functions that make it the most basic and advanced visualization tool at the same time.
The most important feature of EXCEL is that it allows the user to create an interactive user interface so that the user can choose what he or she wants to see. There are some advanced excel functions like the OFFSET FUNCTION, list functionality, etc which help the user to make data models dynamic and interactive.
Finally, with in-built programming functionalities with VBA macros that allow playing with extremely large and complicated volumes of data. Some in-built functions will enable the user to perform statistical regression-based analysis as well on large data sets.
Below is a graph displaying the popularity trend of MS Excel across the globe.
Over the years, Structured Query Language or SQL has become a prevalent coding language for the management of data. Although not exclusively used for data science operations, knowledge of SQL tables and queries can help data scientists deal with database management systems. This domain-specific language is extremely convenient for storing, manipulating, and retrieving data from relational database management systems.
Almost all of the big names in the technology industry make use of SQL. Uber, Netflix, Airbnb the list goes on. Even companies like Facebook, Google, and Amazon, who have fabricated their own high-performance database management systems use SQL to query data and perform analysis. It is not just IT firms, small and mid-size enterprises (SME’s) use SQL for analysis. A quick job search on LinkedIn would show you that more and more companies are looking for skilled professionals with appropriate SQL skills rather than Python or R. SQL may be old, but it is omnipresent. Everything uses SQL or a by-product of SQL.
If you wish to get a job in the data industry, the focus should be on the skills that employers need. To understand the need, an understanding of the market is required. The understanding is about the skills that are doing good in the domain where you are looking for a job and to predict the implications of the skill in the next 20 years to ensure stability and continuity in your career. For a job profile related to data science or data analysis, there is always a demand for SQL as it is the most popular language used to query data and analysis from relational database systems.
SQL is the most popular programming language among data scientists and data analysts than Python or R. It is one of the languages highly preferred by data scientists and data analysts. Despite a lot of hype around NoSQL, Hadoop, and other technologies, SQL is one of the most popular languages not just among professionals in the data field but among all major IT giants. SQL has retained its crown among data science languages for the past many years. Which is a reconfirmation of the faith that data professionals place in SQL that it is not a dying language and would not be going anywhere for at least another decade.
Without SQL data visualization is not possible, so we have considered it as one of the best data visualization tools in 2021 that you must learn.
Below is a graph of the trend for SQL over the last 5 years, it may have declined but the need for this basic tool might rise shortly.
Trending best data visualization tools for 2021, In the ever-expanding world of data science, there are multiple options and approaches that one could take to analytics and machine learning. While most data scientists might come to a solution by using popular programming languages such as Python, R, Scala, or even Julia. There will always be some high-level implementations that in certain situations can get the job done right away. One really exemplary piece of innovation is Microsoft’s Power-Bi.
Power BI integration with the power of ‘Query Editor’ is one of the most important weapons in the arsenal of data analytic tools in the fight against big data. Power Bi has a separate fanbase as it includes powerful data modeling and transformation features. It is a normal process of cleaning a dataset before importing it to the reporting software, but with Power BI all the cleaning and transformation is done before the connection to the data source is made. Power BI’s state of the art ‘Query Editor’ helps the user define or raise queries by mashing data similar to the one is used in ‘Power Query’ by MS Excel.
While Query Editor supports a variety of data-related coding idioms making Power BI an extremely robust means of data analysis, other user-friendly features can be used to generate additional comprehensions from multiple data. The analytics windowpane gives operators the capability to include dynamic lines to visuals like a constant, min, max, average, median, percentile, or a forecast line.
M is the language at the back of every action taken in Power BI’s Query Editor and comes from the Power Query feature in Excel. It is synonymous with F# and is used to develop queries that mix data. R is a statistical analysis coding language that is a major part of a data scientist’s arsenal. However, Power BI makes use of several programming languages for users who wish to go beyond the options presented by the graphical user interface. These languages are DAX, M, and R. DAX (Data Analysis Expressions) is the query idiom originally used in Power Pivot. It is related to the MS Excel formulas but encompasses several more functions catered to relational data. Power BI can use an R script as a data source, transform tables in the Query Editor to create visuals. There are several virtual sources offered for each coding language that is used in Power BI.
Power BI since its release in 2013 has gained tremendous popularity and has moved ahead with the current trends and shows a promising future for any data professional.
Again, Tableau is going to be one of the best data visualization tools for 2021. Tableau is computer software that connects any data source be it corporate Data Warehouse, Microsoft Excel, or cloud-based data, and create data visualizations, maps, dashboards, etc. Real-time updates are always omnipresent on the web, which can also be shared through social media or with the buyer on demand. It allows the receiver of the document to access or download the file in different formats. To experience the power of tableau, the user must have access to an incredibly good data source. Tableau’s big data capabilities make the data visually appealing and one can analyze and visualize data better than any other data visualization software in the market.
Tableau is a potent and rapidly developing data visualization tool used in the Business Intelligence Industry. It helps in simplifying raw data into an extremely easy and understandable format.
Data analysis is extremely fast and precise with Tableau and the visualizations created are in the form of dashboards and worksheets. The data created using Tableau can be recognized by professionals at any level in a corporation. It even allows a non-technical user to create customized dashboards.
The data blending feature of Tableau allows the user to combine data. A query is sent to the database for each data source that is being used in the sheet. The results of the queries are sent back to Tableau as an aggregated data compilation and are presented together in the visualization.
Tableau allows the user to interact with the charts, reports, and dashboards in real-time, allowing the user to raise questions to the data about the future of business with the speed of thoughts.
Tableau has brought together an array of features that have brought about a change in the data analytics industry. One of such features is the ‘Collaboration’ feature where multiple users can collaborate on a given piece of interactive visualization and leave comments or ideas without having the access to make changes to the original sheet. This collaboration invites new ideas without losing the original data.
The wonderful thing about Tableau is that it does not require any technical or coding skills to operate. The tool has gathered global interest among people from multiple domains and sectors ranging from healthcare to sales.
Tableau was specially fabricated for long term cost-effective purposes. In Tableau Public workbooks created cannot be saved in the system and has to be published where the project is put in full display to the public or in other words it is uploaded to Tableau’s public domain where it can be viewed and accessed by anyone. There is zero secrecy to the files saved to the cloud and because of this anyone can download and access the data. This edition is the most excellent for folks who want to learn Tableau and for the ones who intend to share their data in the public domain.
Since 2003 Tableau has consistently been an extremely hot topic and is being used by several multinationals across the globe. Below is a worldwide google trend graph for Tableau over the past 5 years
Along with these popular analytic tools we also have some other analytic tools which have been around and with certain updates slowly catching on popularity. Below are a few other popular data analytic tools that are gaining popularity among data professionals.
Data has become the source of trade in today’s competitive times. If in these competitive times if a business wishes to stay ahead of its competitors it has to adopt new strategies, ideas, and sometimes even tools.
QlikView is one of the quickest advancing business intelligence and data visualization tools. It has an extremely swift deployment and withholds a uniquely intuitive interface to capture its user. QlikView has many unique features like patented technology and has in-memory data processing, which executes the outcome in a matter of seconds to the end-users and stores the data in the report itself. Data links in QlikView is automatically preserved and can be squeezed to almost 10% of their original size. Data connections are visualized using colors, one specific color is allocated to related data and a distinct color for the non-related data.
Below is a google trend graph for QlikView over the last 5 years showcasing the growing popularity of this data analytic tool, that is why we have are considering this tool as one of the best data visualization tools in 2021
In 2009 Mr. Matei Zaharia designed Apache Spark at UC Berkeley’s AMP Lab. Since it is the official release of version 0.5 in the summer of 2012, Apache Spark has gained popularity in its domain. It is a cohesive analytics software for extensive data processing. Compared to its closest competitor Hadoop it runs workloads 100x times faster because it is embedded in a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Apache Spark presents several operatives that make it effortless to build analogous apps the user can use with Scala, Python, R, and SQL shells. APACHE SPARK was fabricated keeping data science in mind and makes data science easy. APACHE SPARK is popular in the field of data pipelines and machine learning models. APACHE SPARK includes a library called ‘MLlib’ that provides a progressive set of automated algorithms for techniques that involve repetition like collaborative filtering, classification, clustering, regression, etc.
For the last 5 years Apache Spark a been on a bumpy ride with the APACHE SPARK trend reaching its peak in the summer of June 2018.
The future of APACHE SPARK seems bright since the profile of data science got termed as the sexiest job of the 21st century.
Multiple industries have used the APACHE SPARK, so this is one of best data visualization tools in 2021. Businesses like Hotel.com, Netflix, UberEATS, and many more report the many issues being solved post inclusion of APACHE SPARK in the company’s operational stream.
Doing a course in APACHE SPARK will help professionals who aspire for a future in big data and wish to be aware of the latest developments around swift and efficient processing of the ever-growing data industry using APACHE SPARK. The course is perfect for:-
SAS stands for Statistical Analytical System. It is a tool developed for advanced analytics and dense statistical operations. It is used by corporations and specialists due to its high dependability. SAS achieves statistical development through base SAS which is the main coding language that runs in the SAS environment. It is a closed-source copyrighted tool that offers a wide variety of statistical resources to perform difficult development.
However, it is not suited for beginners and independent data science enthusiasts. Because SAS is was custom made to meet industrial demands. It is a high-priced software that only large-scale conglomerates can afford. However, SAS helps and is known for its stability and efficiency. Due to this reason, despite the presence of alternative open-source tools, SAS is still the major preference.
Below is a reference to a google trend graph showcasing a slight decline in the demand for this software but predict a rising future of the same.
Ms-Excel, VBA & MySQL
Using PowerBI &Tableau
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