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6 Reasons Python Is Crucial for Data Science

By Destination Z posted Mon December 23, 2019 03:33 PM


Data science is a promising and in-demand field that extracts knowledge and insights from structured and unstructured data through algorithms, scientific methods, systems and processes. IBM reports that there were 2.35 million data analytics job openings in the U.S. in 2015 and by 2020 the same is estimated to rise to 2.72 million.

Now is the best time to take a Data Science With Python Foundation training as Python is the number one tool preferred by data analytics.

Python or R? 

Python and R are undoubtedly the best tools for data science. Both are free and open source, amazingly flexible and were developed in the early 1990s. R was basically built for data analysis while Python was developed to be a general-purpose programming language. In fact, its wide applications in data science might have come off as an absolute surprise for the earliest Python developers.

While both are absolutely necessary for anyone working with large datasets, if you are involved with machine learning or in creating complex data visualizations then we recommend that you undergo the “Data Science With Python Foundation” course

Here are six reasons why Python is crucial for data science, even more than R:

1. Python is more popular

Python is more trending and took over R in almost all platforms, evident from polls like the one conducted by KDnuggets, as seen in Figure 1.
Figure 1

Python is used more in the data science competition platform, Kaggle and 66% of all data scientists use Python. Python has a more active community and a greater number of StackOverflow questions asked.

Popularity matters because Python data scientists can discuss and solve more problems, handle errors and get better support than R. Python programmers will have more demands and job openings in data science.

Figure 2 explains how Python overtook R in 2017.

Figure 2

2. Python is a better programming language

If you have some experience with programming before, learning Python wouldn’t be hard at all!With its elegant syntax and readability, it is far superior to R which has a syntax thought to be unintuitive by most programmers. Python code can be well understood by anybody and the relatively flat learning curve makes it easier to code and helps you focus on the solution more.

Python is used more in the software industry as it helps to integrate people from different backgrounds while R is built mainly for statisticians. Also, the Python testing framework is easy, built-in and ensures good test coverage. This makes your code dependable and reusable.

3.  Python is a multi-purpose language

Python integrates a lot of components and helps to work with multiple tools in the engineering environment. It offers better integration of data analysis with web applications and insertion of code into production and other databases.

4. The Jupyter Notebook (formerly known as the python Notebook)

It is much easier to work with and share with peers without them having to install anything. This helps in better organizing and saves a lot of time making your work productive.

5. R is slow

R was built for statisticians and not for computers. This is the reason why finding R packages may be harder and codes may run slower. Python, on the other hand, is significantly faster in the command line, as well as on the basis of loading time.

6.  Libraries

Even though R has hundreds of libraries for data science, heavier works may require the help of third-party libraries. Python has Pandas, NumPy, SciPy, Matplotlib, Seaborn and many other libraries to work with large datasets. You can now filter, sort and display data easily with Python along with machine learning and data mining techniques. Python comes as a big boon to coders as they don’t have to start anything from scratch.

RHas Perks,Too! 

Although Python is better for the engineering environment, R is a solid statistical language. Visualized data can be more efficient than numbers when it comes to data. R offers better visualization libraries like ggplot2, ggvis, googleVis and rCharts. Although Matplotlib has caught up with most of the visualization techniques R is still preferred.

A computer science background is not needed for R. If you’re looking for academic research rather than applications in the industry, R might be better suited. 

We have to admit that this was a neck to neck competition indeed! But the end results depend completely on you. Python may be better for some applications while R may fare well in others. It all depends on your needs. Which one do you think is better?

Usha Sunil is currently head of content at KnowledgeHut, a leading provider of new-age learning programs for workforce transformation in today’s fast-paced industries. With a keen eye on technology, innovation and design, she brings over 15 years of experience to the content marketing domain.