However, the diffusion of knowledge takes quite a while. They are quick in learning about this new trend. The diagram tells us how quickly the people in different countries learn and try to use the new technology. Some curves have quite some spikes, thus, I took the month when the limit was reached the second time, not the first time. I checked for each country, when they did reach the 1%, 10%, 20%, 50%, and 75% of the score. I wanted to get a closer look by taking a different perspective. Country Comparison in Google Trends for the search topic Project Jupyter in five countries We can see in the diagram, that the interest in the new technology grows similarly in the US, in Germany, Switzerland, and China. So, I put the Google Trends scores for various countries in one diagram to see how the interest evolves over time. I was interested which countries adapt such a new technology quickly – and which take some more time. Question 2: What can we learn about the innovation speed in the US, China, India, Germany, and Switzerland? Extendes query looking at AI and the Eclipse IDE as well We can conclude this from the fact that the Eclipse IDE is really losing a lot of attention. Second, the rise of Jupyter is not just because there are more programmers, but because of its unique functionality and use cases. I learned two things: First, AI gets (still) more attention than the term Data Science, though the interest rises slower. Comparing the search topics “Juypter Project” and “Data Science”īut is it really because of the rise of data science or is it just because more people like using IDEs? To answer this, I added two search terms to more areas of interest: AI and Eclipse (IDE). This correlates with the increasing interest in data science. The interest score doubled each year in 2015, 2016, and 2017 and a continuing strong rise in 2018, 2019 and the first half of 2020. We see, that Jupyter gets more and more attention, starting from around 2014/2015. I want to approach this question by looking how the interest in the two topics, Jupyter Notebook and Data Science, grows over time. Question 1: How does the emergence of Juypter Notebook relate to the rise of data science? Are there insights about the regional differences worth mentioning in any of these countries?.What can we learn about the innovation speed in the US, China, India, Germany, and Switzerland?.How does the emergence of Juypter Notebook relate to the rise of data science?.In the following, I answer three questions: So, I was looking around what insights from Google Trends we can get with respect to Juypter Notebook (or more precisely: the Juypter Project, which I used as the topic for my analysis).
GOOGLE TRENDS DATA SCIENCE CODE
In the end, you have a real notebook with code and explanations. You can format your comments such as making words bold and add pictures. Second, commenting codes can be done easily. You try out something and see the result immediately. First, you can work interactively like with interpreter-based languages. Most of you probably know Juypter Notebook as a tool for writing Python code in data science and analytics use cases.
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