Data Analysis Using Python

Data Analysis Using Python

For all those well versed with the universe of coding and coding languages, Python is a very common language that most coding enthusiasts want to excel at. For those without a prior programming background, learning from scratch can be an interesting process however some of the most common doubts regarding the course deal with where to begin and how much to complete in a go. The reason why Python is in huge demand is the fact that in recent years, it has become an increasingly common choice for data analysis. In the last couple of years, many libraries have reached maturation allowing R and Stata users to fully experience the flexibility of Python without compromising on the functionality that the older programs provided.

The best way to probably start with Python is to begin by grasping the basics involving its syntax and structure. Python might be highly articulated and quite difficult for beginners to comprehend. Python stands out from other programming languages due to its beautifully designed framework and its highly intuitive nature. Python was designed with a goal in mind to make computer coding understandable using simple human language and also to reduce the amount of time that goes into the actual coding process. According to recent data, around 80 percent of major computer science programs utilize Python for their course framework.

Python is a real general-purpose programming language, and most of the top companies in the world like Google and Dropbox make extensive use of this programming language. What makes Python stand out from domain-specific languages like R is that it can be applied across a wider platform, unlike R, which is purely utilized for data needing statistical analysis. The main motive of non-generalized languages is that it could easily be picked up by novice users and used for a specific purpose. Both trained and amateur users could understand the language with equal effectiveness. However, in trying to make the language accessible to non-programmers’ compromises were made in the technical integrity of it. While Python does require a little bit more training, the spectrum of work that it can achieve is endless.

If you decide to learn Python, you are prepared to understand the fundamentals of a coding language, unlike Java or C, which are facets on their own. The advantage that this provides is that you can easily dissect a code written by other users or languages and make better sense of it since Python provides a whole conceptual clarity. Python equips you with sufficient skills to work with the broader strata of programming languages. Working with a single computational language is highly appealing because it helps to unify the workflow. Python also provides a significant improvement in speed and memory usage compared to certain other languages especially Matlab and R. Not to mention Python comes equipped with its own set of tools that makes it possible to run codes at the rate similar to that of C and FORTRAN and faster than R.

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