There are a lot of tools for data scientists. Some of these tools are losing popularity and some are becoming better known and more widely used. If I were to prepare a complete list of them, it would be very extensive, and probably not very useful.
So, to prepare a more useful list for 2020, I decided to consider these market-driven criteria:
The skills requested in job portalsCompany requirements identified in round tables and meetings with Belatrix’s clients.Tool popularity identified by developer surveys and developer portals, such asStack Overflow and others.
If you are considering learning about data science projects or wanting to improve your use of data in your organization, there are a lot of reasons why you should concentrate on the following three technologies that are in demand and growing:
While several programming languages have become key to data science, there is probably no other tool or language that has become as core to the topic, as has Python. Its popularity also continues to increase, with little sign that this is going to change in the near future. So if you’re a student wanting to get started on the topic, you’ll be doing little wrong by focusing on it. Among the benefits it offers are the ability to deal with the statistical functions, while it also has numerous libraries available (I’ll discuss some of them below). Python was the 2nd most loved technology in Stack Overflow’s 2019 survey.