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:
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.
Pandas is a Python library, and has quickly become indispensable for those working in data science using the programming language. If you’re wondering about the name, it refers to “panel data”. As one commentator pointed out, Pandas has “become the backbone of most data projects”. It is open source, and we’ve seen many organizations use it successfully together with the popular libraries Matplotlib and NumPy. In 2019, it was the fourth most popular framework/library according to Stack Overflow.
Increasingly organizations are looking for the insights that machine learning algorithms can provide. Scikit-Learn is an excellent Python library with which you can get started, learn, and implement machine learning solutions. Within the teams at Belatrix, developers have commented upon its ease of use, as well as the availability of different algorithms (supervised and unsupervised), which can rapidly speed up both learning and implementation time.
Based on my own experience, the best way to learn these tools and technologies is practice, so you’ll need data and examples to learn. I recommend using Kaggle. You’ll love it because you can find challenges (competitions), datasets and notebooks available to you.
In addition to the above three, make sure to also be familiar with:
In addition to highlighting what I believe you as a data scientist will need in 2020, it’s also worth pointing out the areas where I personally recommend not focusing. Of course, this will depend on your individual situation – for example, if the organization you’re working for is already using these technologies, then you’ll likely disagree.
What do you think of my list? Whether or not you agree, we know that data science will be of ever greater importance for businesses in 2020 and beyond. Everything from helping to optimize the algorithms of a fashion retailer, to improving the supply chain management of a large manufacturer, will require individuals with data science expertise.
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