Python for Data Science – Why and How to learn

With the increasing demand for Data Scientists, the popularity and growth of Python is also on the rise, as it is preferred by most of the data scientists. The major reason behind its popularity is its ease of implementation. Python is a high-level language that provides you a wide range of functions and tools for dealing with maths, statistics, etc. In this Python data science tutorial, we will explore importance of Python for Data Science and also the various libraries offered by Python for doing Data Science.

Python For Data Science

What is Data Science?

Data Science is the process of extracting actionable insights from the data for making some valuable data-driven decisions. But extracting insights from the data is not so easy. It requires a number of activities to be performed such as data collection, data cleaning and preparation, data analysis, data visualization, and much more. A group of skilled data professionals like Data Analyst, Data Scientist, Data Engineer, etc performs the various activities involved in the entire process. All these activities can be performed by an individual as well. In this tutorial, we will learn how python helps them in doing all these activities and why mastering Python for data science is must.

Why Learn Python for Data Science?

Let us understand the various reasons why scientists prefer Data Science using Python.

1. Flexibility

Python provides you with endless opportunities for trying some new and creative ideas. It provides a wide range of easily implementable functionalities which enables you to explore the various areas of Data Science. Also, there are several complex problems that have a solution in python but not in some other programming languages like C, C++, Java, etc.

2. Ease to use

Python is commonly known for its simplicity and readability. It has a very simple syntax that resembles the words of the English language. So if you are a beginner in Data Science then you can start with learning Python. Python enables you to perform very complex tasks with just a few lines of code. In other words, it enables the programmers to focus more on logic rather than on programming.

3. Open-source language

One of the biggest advantages of Python is that it is an open-source language i.e available for free. It supports both Windows and Linux operating systems. Also, it is easily portable as it is platform-independent. It provides a wide range of open-source packages for data visualization, data manipulation, statistics, etc. So python and data science makes best combination.

4. Provides Wide range of libraries

Python offers a broad range of libraries for the implementation of Data Science, Machine learning, Artificial Intelligence, etc. Importing these libraries in your code makes your job easier and faster. Some of the popular Python libraries used by Data Scientists are Scikit-learn, Tensorflow, Numpy, Pandas, Matplotlib, etc.

5. Provides better analytical tools

Data Analytics is a very important stage of the Data Science life-cycle. Python provides better tools for analyzing data which helps in extracting insights and understanding the patterns and relationships existing in the data. This ultimately helps in making better data-driven business decisions.

6. Supports Deep learning

Some of the python packages like Tensorflow, Keras, etc help the Data Scientists in implementing certain deep learning models. Python is always considered a good option whenever it comes to implementing deep learning algorithms that are inspired by artificial neural networks.

7. Well-supported

The popularity of Python has increased in a very short span of time. There are a large number of use cases of Python in Industry as well as in academics. In any case, the python developers or users seeking any kind of help can post their queries on StackOverflow. They can also refer to the mailing lists, user-contributed code, and the official documentation. With the increasing users, a large amount of support material has become available online at no cost.

Popular Libraries in Python for Data Science

Python Libraries for Data Science

Some of the popular libraries offered by Python for supporting different Data science activities are:

1. Numpy

Numpy can be considered as an abbreviated form for Numerical Python and it is used for scientific computing. It provides a large number of functions to deal with high dimensional arrays, metrics and linear algebra. A wide range of operations can be performed on array and matrices using the methods provided by this python package. It also provides various tools for integrating the code of C, C++, and Fortron.

2. Pandas

We use Pandas package  for the manipulation and analysis of data. It provides many easy to use functions to perform data manipulation and analysis with the help of data structures. The data structures supported by Pandas are Data frames (which handles two-dimensional data) and series (which handles one-dimensional data). We also use it for tasks like data wrangling, data aggregation, etc.

3. Matplotlib

Matplotlib is a popular Python library for data visualization. The effective visualization of data is very important for understanding the clues hidden in the data. It enables you to represent the data in the form of line graphs, pie charts, histograms, image plots, etc.

4. Scipy

Python Scipy library employs in both Data Science and Scientific Computing. It includes different modules for image processing, linear algebra, integration and interpolation of data, etc.

5. Scikit-learn

Scikit-learn is a popular python library for implementing machine learning algorithms. It helps in quickly implementing several popular machine learning algorithms like linear regression, logistic regression, etc. This python library is developed around Numpy, Scipy, and Matplotlib. It provides various tools for data mining and data analysis as well.

6. Seaborn

It is built on the Matplotlib package. It is a python library that provides tools for statistical graphics. You can use both Matplotlib and Seaborn for effective data visualization.


Finally, we will sum up this entire Techvidvan Data Science tutorial with the following line:

“Data Science becomes easy with Python.”

It is a versatile language that enables the Data Scientists to perform complex operations in less time and code by providing a wide range of tools. With all these qualities, Python has become the first choice of many Data Scientists and other Data professionals for playing with data.

So now when you are familiar with Importance of Python for Data Science and why you should learn python for data science, start learning Python now through Techvidvan Python Tutorial series.

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