PYTHON

Why using Python for Data Science?

Have anyone wondered why we are using Python for data science? Well, here are some good reasons for choosing Python as the best programming language for data science.

Using Python for Data Science Jobs

1. Simple and Easy to Learn

There are a lot of programming languages in the world. For example, C, C++, Java, etc. and etc. Though, we are using Python for data science because of its simplicity. Yes, python is so simple that it can print a word with just one line of code. Python uses fluent and natural standards for writing the code. Because of this, python is very easy to learn. So for those who wanted to learn a programming language, Python looks more appealing. Python follows an easy to understand syntax which thus have an upper-hand over R programming language which has a bit complicated syntax.

2. Data Science Libraries Availability

Python offers a wide range of libraries for data analysis and data science operations comparing to any other programming language. Python is so robust that the python repository has libraries for most of our needs. For eg, pandas (for data analysis and manipulation), numpy (offers numerical computation tools), matplotlib (for creating interactive visualisations), scikit-learn (offers many machine learning algorithms).

3. Scalability

Python is more scalable compared to languages like R, MATLAB, etc. Because of this reason, Python is more flexible and offers wide range of ways to approach different data science problems.

4. Visualisation and Graphics

Python is a language which has an extensive set of libraries for visualisations. Matplotlib, which is a comprehensive library for creating plots, static and interactive visualisations being one of them. Seaborn, which is a library built on the top of matplotlib help us in understanding and exploring the dataset. Seaborn provides options for drawing more rich, attractive plots and visualisations.

Python also offers support for using graphical libraries (for image processing, video processing) such as Pillow, OpenCV, etc.

5. Python Community

Data science is a growing field and the python community is huge. Lot of researches are happening on the field now. And the Python community play a very leading role in taking these researches to production ready libraries and applications.

Why we chose Python for this Course?

There are many other libraries which serves data science purposes. For example, R, Matlab, Scala, etc. We chose Python over other languages because, Python can also be used as a general purpose language. With Python frameworks like Django, Flask, etc. Python can be used for developing more production ready web applications. With Django Rest Framework and Flask, we can even write scalable APIs. Throughout this course, we will build many data science projects, where this feature of Python comes handy over other languages.

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