The IEEE Spectrum has released its ninth annual ranking of top programming languages, and Python tops the list. The study also found that employers are increasingly looking for SQL expertise, as well. The ranking is compiled using nine metrics based on information derived from a variety of sources.
so you do not have to refill the form on future articles.
See also: Why Python is Essential for Data Analysis
SQL as a plus one
In the IEEE Spectrum study, SQL stood out for its rising popularity. Interestingly, the report noted that employers were not necessarily looking for just SQL coders. While many listings would be for C++ developers or Java experts, the same was not true for SQL. Instead, many employers were looking for programming skills plus SQL experience.
That should not be a surprise in the age of cloud. Modern apps are now composites that bring together numerous elements. Many services and application elements are exposed as microservices or accessible via APIs. Businesses end up with front ends, back ends, cloud and on-premises databases, and more, making up most applications.
In such a scenario, the dominant database technology is still SQL-based. Hence, the need for expertise in SQL. Industry experts have noted: “It’s more or less acknowledged that SQL will remain a cornerstone of DBMS for the foreseeable future. Even newer machine learning-based offerings, such as MindDB’s ML framework and AWS Redshift ML, have incorporated SQL as the default querying language.”
See also: What GitOps Means for the Cloud
Why Python remains so popular
Python has many benefits for the real-time and data analytics market. One common strength frequently cited is Python’s ecosystem for analytics, AI, and ML. As we’ve written about in the past, “the Python library provides base-level items, so developers do not have to write code from scratch every time.”
Machine learning requires continuous data processing, and Python libraries allow businesses to access, process, and transform data. It offers some of the most extensive libraries available for AI and ML, including:
- Scikit-learn to handle basic ML algorithms such as clustering, logistic and linear regression, regression, and classification.
- Pandas are used for advanced structure and data analysis. It allows you to merge and filter data and collect data from other external sources (such as Excel).
- Keras is used for deep learning. In addition to the computer’s CPU, it also uses the GPU, allowing rapid calculations and prototyping.
- TensorFlow is used to manipulate deep understanding by building, training, and using artificial neural networks using substantial data sets.
A final word
Studies that try to rank the top programming languages are obviously slanted based on the business objectives and the platforms a business already uses. Many “lower” ranking languages like R, Golang, or others might not be a good match for one survey audience. But they might be ideal for data scientists, data engineers, and data analytics experts.
What’s your top programming language? Drop me a note at [email protected] and let us know your favorite language and why it is so important to your work.
Salvatore Salamone is a physicist by training who has been writing about science and information technology for more than 30 years. During that time, he has been a senior or executive editor at many industry-leading publications including High Technology, Network World, Byte Magazine, Data Communications, LAN Times, InternetWeek, Bio-IT World, and Lightwave, The Journal of Fiber Optics. He also is the author of three business technology books.