Data science has become immensely popular in modern business. The collection of data in large quantities, known as big data, allows organizations to make better decisions that guide growth and efficiency.
Data science is now one of the most lucrative fields in the wider IT industry. It involves the collection and analysis of data to deliver insights based on trends and evidence. As a technical field, programming features heavily in data science. Python, SQL, SAS, Java, and C++ are just some of the languages used to collect and interpret data. Python is arguably the most important and is becoming the most popular, thanks to its flexibility and streamlined nature.
Whether used for big data analysis, machine learning, or artificial intelligence, programmers are increasingly turning to Python as a basis to visualize and interpret data. Learn why this language is so popular, and why it’s a focus for new and experienced programmers today.
What Makes Python the Most Popular Language in Data Science?
Dynamic, flexible, and scalable… Python is and will continue to be the most important language for data scientists.
- Python is Scalable – As one of the most scalable programming languages of all, Python is ideal for independent professionals and businesses looking to develop data science applications and systems that can grow alongside analytics and data collection needs.
- It is Backed by an Active Community – Community support is vast for Python programming students and professionals. An expansive network of experts and code libraries make it easy to learn and apply Python to the data science field. This makes the language accessible for programmers at any level.
- Python is Relatively Easy to Learn – Basic Python, including syntax, keywords, functions, data types, exception handling, and classes, etc. is relatively easy to learn. Experienced programmers can upskill to Python in as little as six to eight weeks in most cases.
- It is Suited to Deep Learning – Deep learning is an important subset of machine learning. It’s a type of artificial intelligence where neural networks perform tasks through experience. Unlike rule-based AI, deep learning can develop behaviors through learning algorithms. Python is one of the most important foundation languages of deep learning.
- Machine Learning is Compatible with Python – Wider machine learning applications are supported by Python. It’s easier to implement machine learning using Python because the programming language can be optimized for probability, statistics, and analysis. Python is widely used for statistical modeling and advanced forms of algebra.
- Code is Highly Readable – Python is often referred to as a universal programming language. This is because code is inherently easy to read and understand. Collaboration and project handover are made simple thanks to the streamlined structure of Python.
- Python is Simple to Visualize – There are countless options for visualizing Python code, which makes it easy to view and interpret data, even with little coding experience. This makes Python well suited to organizational use.
Many of these benefits make Python important beyond the data science field. It’s the most essential programming language for IT professionals today.
Learn Python for Data Science Today
Any programmer looking to further a career with advanced skills and a robust language can learn Python today. Formal training and recognized certifications help professionals to stand out in a competitive field. In the United States, the Computer and Information Research Scientists job market is expanding at a rate of 15% every ten years. Growth is similar throughout developed nations.
Training includes in-depth code theory, practical applications, and real-world projects to develop both foundation and applicable skills. Decide to follow a rewarding career path with Python training for data science.