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CSE

Uploaded on

30 Nov 2022

Data Visualisation Tools and Techniques for 2023

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Skill-Lync

Words cannot express what images can. Images are processed by the human brain 60,000 times faster than plain text. Thus, the best approach to visualise data is through charts and graphs. 

In our current data-driven society, one of the jobs is to transform complex, overwhelming data into shareable formats. Learning to use these tools effectively is one of the best methods to find work as a data analyst. The experts in this field can earn between $60000 - $100000.

Businesses and organisations utilise various data visualisation tools to extract the most useful information from data and present it in understandable ways. You can learn how to use these data visualisation tools with or without programming experience.

Let us begin with the most popular and widely used data visualisation tool Tableau. 

Why Do Most Data Analysts Prefer Using Tableau?

The majority of enterprises have adopted Tableau for a variety of reasons, including:

  • Speed: Compared to programmes like Microsoft Excel, creating graphs in Tableau takes much less time. Additionally, unlike Excel, Tableau employs algorithms to intelligently format your graph, utilising most of the labour-intensive aspects of data research.
  • All-around Compatibility: Excel files, text files, and even tables from PDF documents from which Tableau can extract the data are just a few formats that Tableau lets you work with while working with your raw data.
  • Scalability: Tableau is reliable and doesn't crash when importing big data, whereas Excel can't handle much data. A tableau is an all-in-one tool, so you don't have to switch to another one to make data presentations. All of it is integrated into Tableau.

Additional Data Visualisation Tools and Techniques You Should Learn in 2023

Besides enlisting four of the most-used software for data analysis, we have described the data visualisation techniques deployed using each.

 

  • Datawrapper

 

The platform allows you to paste links and data from other platforms like Excel and Sheets so that anyone may build without coding or design skills. Slack and Microsoft Teams are also linked with the platform. When connected to a changing dataset, the visualisations made using this platform are likewise updated in real-time.

 

  • Qlik

 

To assist data scientists in their computations, Qlik Sense makes predictions and generates analyses and insights automatically. Qlik is particularly useful for businesses in scheduling reports and data charts since it allows users to establish alerts for monitoring business data and enables automation. 

Qlik is one of the distinctive aspects of the platform because it can be accessed on any device, including iOS and Android.

To assist data scientists in their computations, Qlik Sense makes predictions and generates analyses and insights automatically. Qlik is particularly useful for businesses in scheduling reports and data charts since it allows users to establish alerts for monitoring business data and enables automation. Qlik is one of the distinctive aspects of the platform because it can be accessed on any device, including iOS and Android.

 

  • Power BI

 

It is a potential tool because and comes with several additional standalone products, including Power BI Desktop, Mobile, and Pro, that can be integrated with other Microsoft goods. Power BI is a low-code platform comparable to Excel, reducing the learning curve and making it more user-friendly.

 

  • Google Looker

 

Looker, a component of the Google Cloud, stands out from the competition for its usability. The tool may be applied to many different custom codes and is highly customisable. 

In addition to BigQuery, Snowflake, and Redshift, the platform supports more than 50 SQLs, making it simple to link to various datasets. The data and visualisations can be exported as PDFs, Excel sheets, or picture files. Another crucial feature was the ability to schedule reports based on automatically tracked data updates.

 

  • Flourish

 

By providing several parameters to bring out the ideal design in a created graphic, Flourish, a member of the Canva family, enables the development of charts and maps. Additionally, Flourish permits free limitless public views and data uploading directly from Excel. It allows for a quick web embedding procedure and is supported on various platforms, including mobile phones.

Python Libraries Used for Data Science 

Python offers a variety of libraries with diverse functionalities for displaying data. Each of these libraries has unique features and supports various graph types.

Libraries for data science in Python include

    • Matpotlib: NumPy arrays are the foundation of the user-friendly, low-level data visualisation library known as Matplotlib. It includes several visualisations, including scatter plots, line plots, and histograms. A lot of flexibility is offered by Matplotlib.
    • Seaborn: Since Seaborn is built on top of Matplotlib, Matplotlib can also be used with it. It is relatively easy to use Matplotlib and Seaborn together. To use Matplotlib's customisation function, we simply need to call the Seaborn Plotting function as usual.
    • Bokeh: The primary reason for Bokeh's fame is its dynamic chart presentation. Using HTML and JavaScript, Bokeh creates elegant, concise, and interactive plots at a high level utilising current web browsers.
      The scatter() method of the plotting module can be used to plot a scatter plot in bokeh. The x and y coordinates should be passed here.
    • Plotly: Plotly creates interactive plots where we can zoom in on the graph or add extra information like data on hover and many other things using Javascript in the background. Here are a few additional benefits of Plotly:
    • We can identify any outliers or anomalies in many data points using Plotly's hover tool features.
    • It has a pleasing appearance and appeals to a variety of audiences.
    • It enables us to completely customise our graphs, which improves the significance and readability of our plot for others.

 

 

Conclusion

For challenges in machine learning, deep learning, artificial intelligence, and other fields, data visualisation using Matpolib is the best option. It is very understandable code making it object-oriented, simple, and user-friendly. To learn more about how you may garner the practical experience of using data visualisation tools Python and Power BI, consider enrolling in Skill-Lync’s online courses for data scientists.


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Navin Baskar


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