No Snakes nor Beakers: Data Science with Python

Data science has seen an explosion in recent years that is quite easy to understand why. We collect, process, and store more data than ever before and with more devices, sensors, and data points coming online at an increasing rate, the amount of data we collect keeps on increasing at an exponential rate. Data is data; what truly matters is information and this is exactly why data science with Python has seen such a huge rise in popularity over the last few years.

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Information has always proved to be an advantage. As humans we are always seeking information to understand what is around and improve our quality of life. We are now in a position to do so better, more efficiently, and on a much larger scale than ever before possible and this is why courses in data science are becoming so popular. Humanity is on the brink of a technological revolution where the very fabric of life as we know it is undergoing dramatic changes the likes of which we have never seen before. To future proof their career, people are investing in education that will help them transition into the future and in assessing which technologies to invest in, Python is a name that keeps coming up over and over again.

Python is a very versatile programming language that is used in a variety of different implementations. Data science is one of them, in part thanks to Python’s philosophy that emphasizes simplicity and clarity; two very important attributes to have built in the underlying framework when dealing with potentially complex, large-scale datasets as is common in advanced data science.

So committed is python to simplicity that a document titled The Zen of Python was written to lay out the guiding principles that should be used when writing computer code issuing Python programming language. Simplicity is a recurring sentiment with the document as is ease and practicality. This philosophy which is shared extensively by the Python community is exactly why it’s popularity keeps increasing but why should one learn python for data science?

Before deciding which programming course to take, one needs to understand what it is they hope to achieve with the result of the course being undertaken. Data science always seems like a strong bet, but remaining flexible it the approach can help students embrace more opportunities in their career. Furthermore, as technologies advance, merge, and grow, it makes sense to invest in an extensible skill set that you can take with you anywhere.

This is exactly the reason why Python is so great. Python is a multi-paradigm language which means it can be used and implemented in drastically different ways; thus offering enough flexibility that programmers using this language are able to undertake a variety of projects, including data science. By being multi-paradigm it is able to support OOP (Object Oriented Programming), functional, as well as procedural programming whilst retaining its focus on clarity, logic, and readability. What this means is that Python can be used in very diverse scenarios, thus ensuring that by learning Python you are able to effectively and efficiently transition your skill set to market demands and exigencies without the need to learn a new language every time. This flexibility is why Python is consistently voted amongst the top programming languages in different categories which has the added benefit of ensuring widespread support for those undertaking this language as their primary programming system.

Data science can be quite complex. Datasets keep on increasing in size and availability and determining a competitive advantage from disparate sets of data is a challenge on its own. By using Python and its focus on simplicity, data scientists can turn their focus on the data science aspect of their job instead of the nuances of the language being used. Python’s love for readability and clarity on the other hand greatly helps in both troubleshooting as well as deriving better results for whichever task is being undertaken.

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Python data science courses can cover a wide gamut of different concepts and principles. From basic to advanced, such courses can give students a relatively comprehensive understanding of python and data science, which skills can be improved about at the workplace or by undertaking projects through the many, many resources available online.

Well-structured courses typically cover python and data science before coming full circle. A good grasp of python is well required before moving into advanced data science topics and as such python programming language is covered as a separate module giving students the opportunity to master Python before moving to data science topics.

There are essentially 3 topics covered here including probability and statistics, predictive modeling, and forecasting. Projects that simulate real-world projects are also typically undertaken to ensure students are well prepared and equipped to becoming data scientists as quickly as possible in a market that has an increasingly growing need for professionals in this sector.

Knowledge gained through any topics covered is typically reinforced using a hands-on exercise that put to practice the various subjects covered in practical terms. This is proven to not only strengthen the learning experience but provides an opportunity to iron out any concepts the student might struggle with and clarify the principles on which any given topic is built. Reputable learning service providers invariably offer this kind of education experience so always check what’s being offered before committing time and money to this wonderful programming language.

The benefits of such a curriculum have to offer are truly astounding. From wine quality prediction to predicting chronic kidney diseases and other illnesses one can very quickly understand the ramifications and positive impact students can have on both their professional’s careers and society at large. Data science can be truly astounding and when Python provides the framework on which the skill set is built, the opportunities are nearly endless.

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