Python emerged in the late 1980s and was published in the early 90s. The version and interface of the language have come a long way since then. But the ease and advantages remain the same. The language emerged as a successor to the phenomenal and popular ABC programming language and after its emergence, the same was promptly replaced by Python. The nomenclature of Python has nothing to do with reptiles. Rather, it is inspired by Monty Python’s flying circus. The nomenclature suggests, that the language is developed for everyone and is easy to learn.
In early 2020 the world learned a lesson n readiness. Covid19 emerged with promises of devastation and many strong world economies collapsed. And with them, the dependent It and tech sectors also collapsed. Therefore, for tech professionals unemployment became the norm. And thankfully at the same time, the importance of data drastically increased. Institutes and commercial entities started utilizing huge amounts of data to chart a safe path toward a sustainable future. Therefore the demand for adept data professionals increased in all sectors drastically. But the supply of adept professionals was dwindling. Python is an easy and quick-to-comprehend language and thus became more popular. And brilliant minds started to learn Python for data science and make quick and fulfilling diversifications.
As the trends show the dependency on data will only increase with time. And the rewards and responsibilities for data professionals will only increase. Therefore, more and more budding data enthusiasts are expected to gradually lean towards the blessings of Python. And the language will only become more popular among professionals of all stature and experience levels.
The advantages granted by Python
It is impossible to comprehend the power that Python can grant. And why the same is ideal for the requirement of a fast-paced and dynamic world without discussing the many advantages it comes with. And many of the same are aligned with the needs of modern-day data scientists.
The syntax is not at all taxing
The syntax of python resembles the human tongue. And can be understood by human beings easily without much hassle. Therefore, comprehension and improvisations in Python are an easy affair. Needless to say, the same is extremely easy to learn and execute.
The libraries are abundant and well-aligned
The volume of data a data scientist handles in 2023 is gargantuan. And often the same is impossible to process with human effort alone. Therefore, to accumulate, format, and process data, automation tools are essential. And deployment of these machine learning and artificial intelligence entities is dependent on the programming adeptness of a data analyst. Libraries of a language are prewritten codes, tried, tested, and optimized by many coders and programmers. And the same can be added to an IDE easily and the entire code can be led. Python is rich with these libraries, and there are quite a few that align with the needs of data scientists. Tensorflow, Mtplotlib, NumPy, SciPy, and Keras are the most commonly used and popular libraries of Python for data science.
A flourishing community
The Python community is huge and is constantly growing since its inception in the early 90s. The language is around for a long time and the users can be segregated into distinct groups, as per their experience and even age. Many of them are still active in the professional realm. And are very generous in helping budding professionals. In online forums and discussion platforms, python users are the most active and numerically dominant. Therefore, for a beginner, it is remarkably easy to get the necessary guidance and gradually become more adept with the language.
Python is free
Python comes free with Linux-based operating systems. And the updates are also easy to access and install. For windows computers, the same can be installed for free as well. The IDEs that can run Python are also free. And require modest configurations. Therefore, it is easy to get started with Python. And the requirements are extremely affordable for most enthusiasts and students.
Why Python for data science?
The answer is time! Time is the most valuable possession at any phase of life. And Python saves most of it. The syntax as we discussed, is easy to comprehend and improvise. And the learning curve in the case of Python is surprisingly steep. Right now, the data industry is craving more analysts and data professionals who can help alleviate the needs of the contemporary industry. And through learning Python, professionals from many fields can easily switch to data science with ease and within very little time. Something, the contemporary industry needs desperately.
Why study data science?
Presence of a dedicated industry
Studying data science in 2023 is full of possibilities. All sectors, regardless of genre and status are using massive amounts of data. And data science is a liberal discipline, and professionals from a plethora of genres can be inducted into the same. Therefore, the discipline will only keep on flourishing.
The massive need is also helping in the flourishment of an entire industry dedicated to data. As every venture can't hire a data team and acquire the infrastructure needed for harnessing its power, the data industry continues to grow. And at pace with the same, the need for adept professionals is also growing. This thriving industry is an ideal breeding ground for flexible and versatile data professionals. Due to the diversity of their clients, employees in this industry get to deal with clients from a wide diversity of genres. And are thus better prepared for the changing markets. The presence of this independent but very diverse industry is thus a driving force that attracts brilliant minds to the discipline.
The opportunities
- The healthcare sector is utilizing huge amounts of data for the development of personalized medicine. And for training automation tools designed to take over remote and Histo-metabolic diagnostics.
- The disaster management sector is utilizing huge amounts of data for saving lives. By evacuating entire at-risk populations to safety. And data-driven insights are making it possible to plan the mitigation approaches so that the response is ready long before the onset of a calamity.
- In marketing, huge amounts of data are being used for pointing out the most potential customers. People who are willing to invest in a product! And then, they are engaged by automation tools that are trained for suggestion enticement and support.
- The product managers in 2023 get in touch with the demands and expectations of their customers through data. And then they plan the upgradation process in detail, by analyzing huge amounts of internal and external data.
- In agriculture, huge amounts of data are analyzed to determine the outcome of a plantation. Even before the plantation, data can help decide about the soil, the breed, and the supplements. And help a farmer prepare for inevitable calamities.
- In crime control and investigations, data is changing the entire game. Investigators in our time can predict a crime before the same is even committed. By analyzing data from all domains, including from the markets and financial domains. Investigators can understand the patterns and the irregularities. And eventually, identify the source of the same.
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