A short history of data science
Over the last few years, new technology domains have quickly taken over a lot of parts of the world. Machine learning, artificial intelligence, and data science are new fields that have entered our daily life, both in our personal lives and in our professional lives.
The topics that data scientists work on today are not new. The absolute foundation of the field is in mathematics and statistics, two fields that have existed for centuries. As an example, least squares regression was first published in 1805. With time, mathematicians and statisticians have continued working on finding other methods and models.
In the following timeline, you can see how the recent boom in technology has been able to take place. In the 1600s and 1700s, very smart people were already laying the foundations for what we still do in statistics and mathematics today. However, it was not until the invention and popularization of computing power that the field became booming.
Figure 1.2 – A timeline of the history of data
Personal computer and internet accessibility is an important reason for data science's popularity today. Almost everyone has a computer that is performant enough for fairly complex machine learning. This strongly helps computer literacy, but also, online documentation accessibility is a big booster for learning.
The availability of big data tools such as Hadoop and Spark is also an important part of the popularization of data science, as they allow practitioners to work with datasets that are larger than anyone could ever imagine before.
Lastly, cloud computing is allowing data scientists from all over the world to access very powerful hardware at low prices. Especially for big data tools, the hardware needed is still priced in a way that most students would not be able to buy it for training purposes. Cloud computing gives access to those use cases for many.
In this book, you will learn how to work with streaming data. It is important to have this short history of data science in mind, as streaming data is one of those technologies that has been disadvantaged by the need for difficult hardware and setup requirements. Streaming data is currently gaining popularity quickly in many domains and has the potential to be a big hit in the coming period. Let's now have a deeper look into the definition of streaming data.
