In today’s era of modern technology, graphics have become the pinnacle of communication. Be it transmitting devices or marketing commercials, business websites, or online tutorials, everything has to be portrayed graphically for optimal satisfaction. Therefore, it comes as no surprise that data visualization has become the primary source for grabbing public attention.
Here, we shall discuss what exactly is data visualization, why it is so useful, and what are the different ways you can achieve it using Python. We have also mentioned a few helpful courses for you to brush up on the technical concepts so read on to get to know about it all.
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What is Data Visualization (DataViz)?
Data visualization, or DataViz in short, is the art of conveying important, but often boring, the information in an eye-catching manner. With the help of bright colors, unique designs, and an artistic rendition of facts & figures, data can be easily and rapidly internalized by the target audience. Think of it like this: when it comes to absorbing information, watching a video is so much more exciting and compelling than reading chunks of dry text. While both achieve the same purpose of delivering information, data visualization makes the process much more riveting.
For additional info, here is our guide on Data Visualization for Beginners.
Data visualization can be achieved in a multitude of ways. Here are some of the most commonly used types:
- Data Dashboards
How do I Achieve Data Visualization?
There are a number of software and programs that can achieve data visualization. These tools help you in converting dull text into a visually appealing matter. Out of the extensive list, today we are going to talk exclusively about Python.
What is Python?
What are Python Libraries?
Python comes armed with great libraries stocked to the brim with tons of different features. Here we shall discuss the top 3 Python libraries and their best uses:
The most frequently used library, Matplotlib is a low-level plotting interface, particularly suited for creating bar graphs, histograms, scatter graphs and line charts. It offers the greatest freedom, but the coding is somewhat long and tedious. To import it, simply type: import matplotlib.pyplot as plt
This library is a step-up of Matplotlib with a higher level API enabling lesser codes for quicker results. It can help create similar graphics as Matplotlib but in a shorter time. You can install Pandas using either pip or condo by simply typing: pip install pandas or conda install pandas
An extension of Matplotlib, Seaborn provides a high-level interface for creating elaborate graphs in just a single line of command. The graphical designing offered here is the most attractive and multi-featured of all.
Simply type: import seaborn as sns to install it.
Why is Python the Best Programming Language for Data Visualization?
Declared as the Number 1 Programming Language in 2018 by the IEEE Spectrum, Python is one of the most widely used and favored coding languages worldwide. Its extensive user community has a plethora of shared codes and examples and provides a solution to many common problems.
As far as data science is concerned, Python is one of the best coding languages for data visualization due to the following reasons:
- It is user friendly and particularly suitable for beginners.
- Python code syntax uses common English words that are easy to remember and simple to use while coding.
- Python codes are much shorter and more straightforward than other programming languages. This allows it to deliver a streamlined development time, making it more time-efficient.
- It is a versatile language that can be tailored for your specific purpose with the help of add-ons like Django and PyQt.
- You can learn Python basics for free (or sometimes for a small fee) with the help of the online courses.
Suggested Online Courses for Data Visualization with Pyhton
Udemy is a website that teaches many things, including data visualization with Python. It starts with explaining the basics of data science, the concepts behind data clustering and analysis, and finally shows how to create an active graphic display for complex data.
We recommend the following course for beginners: Data Visualization with Python: The Complete Guide.
Coursera is another online teaching website. In addition to DataViz, you can learn the concepts behind data virtualization and Matplotlib here. We recommend the following FREE course to learn the fundamentals of Data Visualization with Python.
Once you have mastered the basics in as little as 16 hours max, you can move on to advance level courses and significantly improve your programming game.
Here you will learn in-depth about the various data analysis techniques and how you can achieve data visualization with Python as well as SQL and statistics.
We recommend the following FREE intermediate-level course to start with, called Data Analysis and Visualization. Then, once you feel well versed in the basic art of coding, you can sign up for higher courses.
Once you have gone through the courses mentioned above and grasped the basics of coding graphics, it would be smart to aim higher and get an edge over other programmers. This can be accomplished by taking the FREE Improving your Data Visualizations with Python course available on Datacamp.
Summing up, data visualization is literally the practical application of the phrase ‘A picture speaks a thousand words’. Multiplex and compounded data can be coded using Python into simple-to-understand diagrams and charts, which not only display the information accurately but also interpret and project trends, establish timelines and predict future values using mathematical algorithms of analysis and data mapping. All of this and more comes together seamlessly to create a solid medium of effective data communication.
Data visualization with Python is one of the easiest yet most beneficial techniques to accomplish this feat. It has become one of the most highly sought after skills by job recruiters and potential hirers in today’s competitive market, thus making it an essential skill to possess and master for all aspiring programmers.