Why is Python becoming a Trend among Data Scientists?

Internet technology has set the world on fire. New revolutions are always around the corner. But did you ever notice that nowadays new revolutions are mostly based on technology and driven by data. It is data that is being generated everywhere via the internet. So what’s big deal about it? Well, the data we get from Internet is big data. Websites, social media, servers and so on…all contribute for data. It is data that is driving the demand-supply chain that serves the human race. Since we have been generating humongous amount of data every day, we have data scientists who drive value from it, so that humans can lead life of meaning and purpose and of convenient.

We now got hunch that Python has something to do with big data and work profile of data scientists. Now let’s get back to the point and seek answers as why data scientists are loving languages like Python and R over the traditional programming languages.

Python and R do not give unnecessary headache to coders. They are clutter free, and easy to code with minimum syntax involved.

Well, do mind that Python and R are preferred languages of data scientists but they differ, especially with their libraries. Let’s see some other aspects of Python which makes life easy for data scientists.

Python is full of important and time-saving numerical and statistical packages/libraries. The list is long, well we can at least see some good ones.

IPython Notebook: it stands for ‘Interactive Python’ and another name is Jupyter Notebook. This web-based tool allows coders to visualize data while coding. You can put rich text, see graphs and embed logos and pictures and video links. Using IPython means having the visualization of your coding.

Numpy and pandas: they work together, kind of inter operates, and make it easy for the data scientists to analyse, import and work flexibly on data sets. Numpy and pandas do exist in python library.

Matplotlib: it is a 2D plotting library used for data visualization. Supports various kinds of 2D charts that you need for data visualization, such as line plot, sub plot, histograms, stream plot and so on.

Scikit-learn: this toolkit allows you to apply ML (machine learning) algorithms to data in order to have predictions.

PyMySQL: through this library, you can work on MySQL database, have run your queries and use the database.

Cython: use Cython to improve the performance of the model by converting the code to another environment, you can run your code in C environment.

Beautiful Soup: this library is used for pulling out data from HTML and XML files. Basically, it does the work of parsing.

If you get into PIP, you will find number of Python libraries which in turn make programming easy for data scientists.

By now you must have understood that why data scientists prefer using Python over other languages.

Can I Become a Data Scientist without learning Python?

Well, not to be felt discouraged, the answer is No. Coding is an essential part of a data scientist’s life. You may have the knowledge of various tools for data analyzing and visualization, but still at the end of day you will crave for Python coding. On a positive note, if you already know even one language, be it Java, C or R, well then Python will be a cake walk for you. Coding is very simple in Python. If you won’t learn Python, and you are good at R, probably you will be able to analyze data but more than that cleaning matters.

Next, confusion is, which one is a better language for data scientists: R or Python?

The answer depends on your roles and responsibilities, in nutshell on your needs. Both languages are short in coding and syntax. Let’s see some basic ‘agree’ and ‘agree not’ points in these two languages.

If your data has been cleaned and organized and is pending for analysis, well then R is best. Otherwise you need Python, since it is well-known for cleaning and organizing data, along with analysis. Python has got better IDE, like with Jupyter it becomes interactive. Other useful ones are Pycharm, Atom (kind of code editor) and Rodeo.

Well, R has this plain one: Rstudio. Python is a high-level language and is considerably fast. In R plotting gigs make the difference. Well, Matplotlib with Python is another gem in the crown. Though Matplotlib can be used with R and other programming languages as well. R has many third-party packages which are good for data manipulation, while Python is full of libraries. At this point both seem to be in tied position. Python is used for ML and Deep Learning algorithms. The popularity of Python and R is increasing like never before. Well, there can be many more reasons that make Python a preferred coding language for data scientists, and why it is different from R.

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