In recent years, we have experienced an exponential growth of data. As the amount of data grows, the need for developers with knowledge of data analytics and especially data visualization spikes. Data visualizations help in getting a clear and concise view of the data, making it more tangible for (non-technical) audiences.
MATLAB and R are the two available languages that have been traditionally used for data science and data visualization. However, Python is the most requested and used language in the industry. Its ease of use and the speed at which you can manipulate and visualize data combined with the number of available libraries makes Python the best choice. So Data visualization seems easy, doesn’t it? However, there are a lot of myths surrounding it. Let us have a look at some of them.
Myth 1: Data visualizations are just for data scientists
Today’s data visualization libraries are very convenient, so any person can create meaningful visualizations in just a few minutes.
Myth 2: Data visualization technologies are difficult to learn
Of course, building and designing sophisticated data visualizations will take some work and learning but with very little knowledge of the libraries and what they are capable of, you can create simple visualizations that will help you get valuable insights into your data. Python is a comparably easy language. The “pythonic” approach is also used when building visualization libraries for Python which makes them easy to understand and use.
Myth 3: Data visualization isn’t needed for data insights
Imagine having a table of data with 20 columns and several thousand rows. What do you think will give you better insights into this data? Just looking at the table and trying to make sense of all the columns and values in them, or creating some simple plots that visualize the content of this table? Of course, you could force yourself to get insights without visualizations, but the key is to work smarter, not harder.
Myth 4: Data visualization takes a lot of time
If you have a basic understanding of your data, you can create some basic visualizations in no time. There are a lot of libraries, which will be covered in this course, that allow you to simply import some data and build visualizations in a few lines of code. The more difficult part is creating visualizations which are descriptive and display the concepts you wanted to show but don’t worry, this will be discussed in the course in detail as well.
Amidst all the myths, Data visualization in combination with Python is an essential skill when working with data. When properly utilized, it is a powerful combination that not only enables you to get better insights into your data but also gives you the tool to communicate results better. Head over to our course titled ‘Data Visualization with Python’, to use Python with NumPy, Pandas, Matplotlib, and Seaborn to create impactful data visualizations with the real world, public data.
About Tim and Mario
Tim Großmann is a CS student with interest in diverse topics ranging from AI to IoT. He previously worked at the Bosch Center for Artificial Intelligence in Silicon Valley in the field of big data engineering. He’s highly involved in different Open Source projects and actively speaks at meetups and conferences about his projects and experiences.
Mario Döbler is a graduate student with a focus in deep learning and AI. He previously worked at the Bosch Center for Artificial Intelligence in Silicon Valley in the field of deep learning. Currently, he dedicates himself to apply deep learning to medical data to make health care accessible to everyone.
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