Before computers were created, many data visualization were done by hands, and it would often take a long period of time just to process and analyze the data. With the goal of trying understand bigger datasets and making better visualizations. Humans started to use computers and invent programming languages to compute data.

Process

During the research of this project, I knew I wanted to focus on the technology of data visualization. I only knew that Python was a great programming language that some people use to create visualization, but then while researching I realized that there are actually even better tools out there to do such tasks. I started by researching for software and tools that people use today for data visualization, and found out that R and Tableau are very popular and easy to use. Then I started thinking if I can traceback from there to see what people use when computers were first created back in the 1900s.

Software

The software that was used in this project was Timeline.JS by KnightLab. It’s a timeline generator tool that uses a template on Google Sheets. This tool is very straight forward and easy to use, and still provide some pretty good customization options as well.

From the name of this tool, at first I thought it was some kind of JavaScript library that one would import to a JavaScript project. However, it turns out that it was much simpler than that, anyone without programming background could use this tool to create such interactive timeline.

Results

To think that the first data visualization using programming was not even more than 100 years ago is mind-blowing. It was also interesting to see that visualization tools have come such a long way and became so much more powerful. It was also fascinating to find out that some of these tools like R was also based off of the pervious S language.

Reflection

Challenges

1. Programming Language vs Visualization Tools/Language

It was actually somewhat difficult to choose which language to include into the timeline. For example, there are frameworks like Spark, and languages like Python, but these are more data analyzation tools, not so much for creating visualizations. Python does have libraries like Matplotlib for creating visualizations, but the creation of Python wasn’t just for data visualization, therefore it was not included into the timeline.

2. Choosing Feature Image

To make the timeline easy to read it is good to have some kind of visual image to balance the description text, especially the long ones. However, eye-catching images were not so easy to find since these topics are more about the programming technologies. For example, googling images for S language did not result in a lot of good images but book covers and snippet of codes.

Future Direction

1. Include other data analysis tools

Since data analyzation is very closely related to data visualization, it might actually be better to include those historical events into this timeline as well. There are definitely several notable frameworks and libraries such as Apache Spark, Excel, SQL, Scala, etc that could be added to the list.

2. Machine Learning and Data Mining

As machine learning, data mining, and artificial intelligence are getting more and more popular in the technology world today, taking a step further with this research and focus not just data analysis but also using the data to create AI/ML models could be a very interesting topic to explore about. It would definitely be interesting to see what simple data can do so much.