Python development has made a remarkable progress in all the niche aspects of different industries.
In data science, a majority of the development teams prefer to work with Python.
There are many benefits of using Python for data science. For instance, Python language is developed as part of learning algorithms as well as for real world machine learning programming.
Between 2015 and 2021, more than 13000 global Python communities were created, making it one of the fastest growing programming languages.
From a career perspective, the Python data science course offers not only a lucrative salary but also supports tons and tons of research work.
One of the biggest characteristics of using Python in the commercial space refers to the development works going on in the healthcare and Canadian Pharmacy services and research organizations.
In this article, we will point to the role of Python development activities specific to healthcare and pharmacy and how data science training in Python online courses could help you make the big leap into this ecosystem successfully.
Why python’s adoption is so much more exciting?
If you follow the drug discovery and development industry, you would realize that it is a long drawn process and consumes a lot of energy, and human resources and the cost of the project often inflates due to variable factors.
According to a recent research, a high quality drug for cancer treatment could take more than a decade to develop, and usually costs the pharmaceutical companies close to $1 billion US dollars.
If you add inflation cost to it, the drug development almost triples from the time initial research begins to final completion.
Python’s application in drug development opens up so many new possibilities with its fast paced programming platform and quick outcomes.
#1 Python for Development of Ai and Machine Learning Based Healthcare Services:
Based on the statistical analysis and recent market research studies, nearly 76% of the healthcare data science projects are based on Python programming.
The philosophy driving this aggressive trend is derived from the fact that Python’s scope can be heavily expanded in an open source and then when the proofing of concept is complete, it can be brought in-house with minimum complexities.
Moreover, it is also so much more reliable for applied statistics that can be linked to different types of data and analysis specific to the healthcare industry.
A big drug manufacturing company uses Python to innovate the drug engineering and pharmacy effectiveness measurement practices.
These analyses are carried out across the domain using sophisticated computational techniques such as Linear Regression, Classification, and SVRs.
While there are many different ways to deploy Python for pharmaceutical companies, more or fewer data analysts and ML engineers working with Python are able to develop totally new techniques using existing lab models in an agile manner.
Companies save 50% of their developmental costs by simply adopting Python with AI Machine learning.
#2 Python’s Combination with Other Programming Languages Fastens the Development Process:
In pharmaceutical and healthcare research programs, the projects can continue for years and decades.
So, modern data scientists prefer to work with programming languages that are malleable and flexible.
This allows the scientists to extend the new codes appropriately combined with back-end codes.
A very powerful example of this combination can be referred to as what PyDrone/ PyDron has done.
With PyDron, coders are able to automatically refine data mining and analytics processes in a parallel annotation and computing domain.
It is a key component of parallelism that allows programmers to develop semi-automated Cloud nodes for crunching heavy data.
Moreover, the Python program fastens the development process as soon as it is moved to remote virtualized machines.
For all the right reasons, Python is considered the most portable language as it works on all the current popular operating systems (OS) such as UNIX, Linux, Windows, and MAC.
These days, it also runs on AWS and Google Cloud platforms, which makes it a very democratized and performance based programming language that combines well with C/ C++ and other traditional application development languages and libraries.
With its highly efficient integrity and intuitive object oriented programming benefits, healthcare and pharmaceutical companies are able to expand quickly in any ecosystem.
#3 Building Healthcare Database Lakes from Scratch:
Healthcare data needs a special kind of attention when it comes to the collection, mining, monitoring, and analysis.
According to recent projections, healthcare industries are under immense pressure when it comes to analyzing and reporting healthcare data.
Currently, healthcare data is growing at an annual CAGR of close to 40%, and by 2025, data scientists would be exploring projects that mine in 1500 exabytes of data annually, if not more.
In such a scenario, data analysts need a frictionless Healthcare data management strategy that covers for automation, AI based monitoring, permissions and controls management, and much more.
A centralized data lake is what the need of the hour is and that’s where the role of Python data science managers comes into the picture.
Using Python for data science, analysts are able to accurately break down the different stages of data management – warehousing, big data analysis/processing, and real time data analytics with data visualization.
Organizations that are opting for safe and sound data lake management projects, they would invariably hire a Python developer that can help them grow data sources varieties and volume across data intelligence, business intelligence, and automation using Artificial intelligence, text analysis, and NLP and machine learning algorithms.
#4 Security and Error Handling in Real Time with Python:
In healthcare, data managers have to ensure that all processes met compliance and governance policies. There is absolutely no scope for data leaks and hacks/ thefts unlike in other industries.
Python’s ease of use with libraries, functions, and classes allows analysts to work with security managers and networking architects to develop a solid value chain with superior error handling features.
There are over 50+ different types of Python modules that specifically meet the criteria of security and compliance demands from Healthcare Services and pharmaceutical companies.
These are compensated with PERL and PyDron applications. To reduce further dependence on Python in cloud security applications, many software engineers are able to develop cryptography APIs and blockchain APIs to mitigate internal and external security issues.
In 90% of the development work in this regard is carried forward from open source SDKs and analysis of these projects shows near perfect detection and reporting of basic and advanced types of security threats to Cloud and web applications.
The number of Python users is growing every day. From Google to Netflix to NASA, every major institution that mines billions of data points a day, have a Python development team and data scientist researcher to manage various types of computation and statistical programming tasks.
By 2025, it is expected that the top 100 companies in data science would employ 90% of the certified AI ML engineers who pass out from the top tier Python data science courses.
A top employer would be NVIDIA, IBM, AWS, Seagate, Tencent, HPC, and Qualcomm.