We start by selecting the keyword/keyphrase that is the most similar to the document. Then, we iteratively select new ca

The top of the list was dominated by web development languages such as Javascript, HTML/CSS with 69.7%, 62.4% respectively. Right below lies SQL with a strong 56.9%. Then on the 4th rank stands Python with 41.6% which is roughly 15% less than SQL.

In academia, there is a great emphasis on Python with regard to data science. Professors and instructors teach how to leverage Python libraries such as NumPy, Pandas, and Scikit-learn to make sense of data.

For provisioning infrastructure and maintenance, there are several cloud platform independent tools like Pulumi and Terraform are available in the market. Platform specific tools like CloudFormation (for AWS) have also seen wide acceptance.

Python received such praise and attention mainly because of its data science libraries and user-friendliness. This is no surprise since the high-level programming language uses an English-like syntax.

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ain, and having to make customer experiences more and more seamless and immersive isn’t taking off any of the pressure on companies. In that light, it’s understandable that they’re pouring billions of dollars into the development of machine learning models to improve their products.

The demand for data scientists surged as businesses realized how priceless is data science. Hence, the implementation of data-driven approaches to improve the decision-making process.

It is true that data science jobs mushroomed. But at the same time, landing a decent position in this field remains notoriously challenging especially for novices. This is because of the subtle difference between data science in theory and real-life data science that is correlated with the problems businesses deal with on a day-to-day basis.

The DevOps space has split into three in the past couple of years —core DevOps, DataOps and DevSecOps. Data Engineers are expected to know their infrastructure now. This means that whatever stack they are using, they should be able to resolve operational issues concerning the infrastructure — databases, data pipelines, data warehouses, orchestrators, storage and so on.

If you have drunk the kool-aid of a multi-cloud future, it’s better to know at least one of the two aforementioned Infrastructure-as-Code tools. IaC comes with its own benefits like the ease of implementing immutable infrastructure, increased speed of deployment and so on.

First, online courses on SQL are scarce compared to those on Python. For example, Edx produces 31 courses when typing the word “SQL” in the search bar unlike “Python” which yields 94.

Second, there is a gap in SQL courses’ levels. Simply put, either the course is too introductory or advanced. The former scratches on the surface of SQL such as (SELECT, INSERT, and UPDATE queries). Whereas, the latter might appear cryptic and hopeless to follow.

SQL again is even more popular among professionals than Python. The results of the latest 2020 StackOverflow survey conducted with the participation of 47,184 professional developer on the most important coding languages was concluded with the following:

Unfortunately, this task exceeds Python capabilities, and here when SQL (Structured Query Language) comes to play. That is why, understandably, SQL is present in almost all data science-related job posts. For example, roles as a data analyst, business analyst, and data scientist.

Recruiters, interestingly, found themselves receiving applications not only from persons with computer science-related backgrounds but also from those who are complete strangers to the field such as humanities and social science degree and non-degree holders.

While Python alone is sufficient to apply data science in some cases, unfortunately, in the corporate world, it is just a piece of the puzzle for businesses to process their large volume of data.