Why is Python a language of choice for data scientists?
September 22, 2020 | Emerging Technologies, Python, Technology
Data science has gained immense popularity in the recent past. Digitalization has made data analysis not just relevant for IT companies but it has helped industries everywhere to pierce through its targeted clients and generating insights for complex problems. It is for good reason that data science has become one of the most lucrative fields in Industry 4.0. Back in 2012, the Harvard Business Review announced that the job of a data scientist is “the sexiest job of the 21st century”. This is definitely true as data science jobs have globally skyrocketed making data scientists part of a niche club of digital-savvy experts. Data science involves collection, analysis, and exchange of volumes of data to generate insights for a wide array of issues.
Data analysis is a technical field using various tools and programming languages to analyze data. Some of these popular languages are Python, Java, C++, Hadoop, SQL and SAS programming. Amongst all these popular languages, Python has emerged as the most popular programming language with a passionate community of followers. The widespread popularity of this language is because of the streamlined and flexible nature of Python as a programming language. Python is used globally across industries for data science and has proved to be an immensely promising force in artificial intelligence, machine learning, big data and analytics. One of the main reasons for this immense popularity is the data-focused libraries and Python syntax which can be used as a robust tool to visualize data.
Are you still curious as to why Python is a language of choice for data scientists? The future of data science with Python is extremely promising as a lot of programmers prefer Python over Java, Scala, C++, and even R. This blog focuses on the benefits of learning Python for data science.
Why is Python so popular?
- A dynamic programming language: What can’t Python do? It is a complete package and a dream language for coders across the globe. Python is tailor-made for algorithmic implementation and comes with dedicated statistical libraries such as Pandas, NumPy, Matplotlib, SciPy, and scikit-learn and advanced deep learning libraries such as TensorFlow and PyBrain. This powerful and expressive programming language is the best programming language to master if you want to analyze complex data.
- Easy to learn: Python is one of the easiest programming languages to learn. It takes the least time to learn amongst all the A-list programming languages. This makes Python a natural choice for data scientists around the globe. Python comes with facilities such as Ipython that benefits teams working cohesively to analyze high-level data. It helps data scientists to work together and share codes, data, output, etc. seamlessly without any third party help.
- Scalability: Python is the best scalable programming language. It helps in building robust applications at an easy pace. This is extremely helpful for businesses looking to create data business models and applications. The scalability feature makes Python for data science increasingly popular amongst big businesses and organizations looking for a competitive edge.
- Machine Learning: Machine Learning is extremely important for data scientists. Data scientists use elements of machine learning for their day-to-day functionalities such as statistics, probability and optimization. Python comes with packages for mathematical functioning which can be utilized for precise mathematical parameters for data analysis. Machine learning is easier to implement in Python compared to other programming languages and popular for packages such as statistic modelling, symbolic algebra, linear algebra and others.
- Deep learning frameworks: Python comes with a lot of deep learning frameworks, such as Caffe, TensorFlow, PyTorch, Keras, and mxnet. You can use these to build deep learning architecture with a few lines of Python codes.
- Code readability: Code readability is one of the most important design principles of Python. Several programmers may write a lot of different programmers in Python but the easy readability feature of Python makes this code super easy to understand. This feature has made Python a universal programming language.
- Python community: Python has a well-established pool of dedicated followers and volunteers creating an expansive data science library. This widespread community is not only responsible for the creation of the most modern tools but is also easily accessible for aspiring data scientists.
- Visualization: Python comes with a lot of options for visualization. Matplotlib offers a secure and solid foundation and visualization packages help you in getting a very good sense of the data.
Glassdoor predicts data science and data analytics to make up more than 11 million jobs by 2026.
To wrap up, Python is a total pleasure to work with making it popular compared to other in-demand languages such as R. This versatile language offers the opportunity to work with various frameworks processing tons and tons of data.
The language is growing and will keep evolving in multitudes, which means that the learning will be constant. There is immense information available online with a lot of training and courses to structure a learning path for data science with Python. There are endless opportunities in this field as the demand for data scientists has skyrocketed in the recent past.
Looking where to start? Interested in starting the journey of Python for data science with us?
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