Why data scientists are turning to Python
Python is known as a highly accessible programming language which is widely considered to be an essential language for data scientists.
Surveys have indicated that Python is the first choice for data professionals, ahead of SQL and R, who are themselves well ahead of traditional programming languages such as Java and C.
Python for data science
First created as a general purpose interpreted language in 1991, the popularity of Python by data professionals did not happen overnight – it has become a staple among data professionals due to its ability to easily manipulate data and use it with advanced analysis tools. or AI models.
Indeed, Seth Dobrin, vice president of IBM’s Data and AI unit and director of IBM Cloud data and cognitive software, noted that the ability to code using Python is the common thread running through it. all roles of the data science team today.
Interviewing for a position in Dobrin’s data science team involves taking on a coding challenge that candidates complete on their own, followed by a supervised coding session with a senior member of the team.
Its relevance has led to a proliferation of Python courses for data professionals. For example, the National University of Singapore offers a Python for Data course for learners who want to use Python as a data science tool for programming and business analysis.
But if Python as the flagship programming language for data science is indisputable, what are its strengths and how can organizations take advantage of them?
Designed to be easy to understand and code, probably the main attraction of Python is its simplicity. The syntax supports different coding styles, which results in better productivity compared to strongly typed languages such as Java or languages with a high learning curve such as C ++.
One of the attractions of Python for data scientists is the many libraries that Python can easily access. This includes libraries for data manipulation, mathematical and scientific calculations, and visualizations, among others.
Additionally, many AI libraries for deep neural networks, machine learning, and data mining applications are also accessible using Python. Facebook, which performs billions of inference operations per day, relies on AI models built with PyTorch.
Developed by the social media giant for applications such as computer vision and natural language processing, PyTorch offers what Facebook’s chief engineering officer Lin Qiao calls “first-class” Python integration.
Finally, Python has excellent built-in processing capabilities that cover both traditional and unstructured data. Of course, memory mapping is probably unavoidable for larger data sets on the order of 10 or 100 gigabytes. But with the right libraries, even that should be easier with Python than with any other language.
As an added bonus, the fact that Python is compatible with all major platforms means data scientists (or students) can run it on virtually any computer system, including the new ARM-based MacBook.
Practical uses of Python
What are the practical uses of Python? A recent article on Analytical analysis described a few ways that Python can be used.
- Data gathering: Identifying the right data sets to use in a model is an essential task, but it takes time. With the ability to filter and quickly retrieve relevant data, mundane data collection tasks can be automated using Python.
- Data cleaning: Cleaning up “dirty” data is considered one of the most expensive and time-consuming tasks in data science. Yet dirty data can lead to lost productivity, wasted resources, or wrong conclusions. Python scripts can be written to quickly identify errors and correct minor issues such as data formats.
- Data mining: Phyton can facilitate deeper data mining to identify patterns and draw inferences from the data. With the ability to quickly manipulate data points and their relationships, Python can make it easier to discover new information to improve results.
- Data visualization: With hundreds of data visualization libraries available, data professionals can use Python to visualize data for visual representation to identify trends and understand any data set.
Ultimately, the popularity of Python lends itself to a virtuous cycle of success. As more and more data scientists use Python, the existing code repositories, tools, and ecosystems around Python will expand, giving newcomers an even greater incentive to learn Python and use it for their businesses. data science initiatives.
Paul Mah is the editor of DSAITrends. A former systems administrator, programmer, and computer teacher, he enjoys writing both code and prose. You can reach him at [email protected].
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