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Introduction to Data Science with Python

24 Nov 2022

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Introduction to Data Science with Python

It is a known fact and statistics have flashed the figures and facts that Python is the most preferred programming language for data science. It is the most remarkable experience for any data scientist to be able to use python for data analytics and data science. Python favors the development of complicated scientific and numeric applications.

This programming language eases the development of web and desktop applications.
The reason why python is considered the most outstanding programming language is that it can handle astounding tasks and the language is smooth and friendly to newcomers.
Let us understand how data science gels well with python.

What is Data Science?

It will be easy for you to understand more about the use of python in data science, by knowing what exactly data science is all about.

Data science is all about finding the solutions for the big business statistical issues that cause hurdles in the development process of the company. The big data problems are solved by extracting valuable information from the available huge pile of data and using the extracted data for the benefit of the company. This process is covered by data science.

For example, in identifying customer patterns from available data and analyzing the situation for a better business prospect in the future, data science thus is very essential for every industry and business. 

This was the general information about data science. Let us now know why python holds such a huge role in data science.

What Python is used for?

Python is known as a scripting and automation language. Python helps restoration for shell scripts or batch files; it is also used to automate synergy with web browsers or application GUIs. Scripting and automation represent tiny tips for Python.

Application programming with Python

You can create both command-line and cross-platform GUI applications with Python.
Python generates the binary from a script with the help of cx_Freeze and PyInstaller.
Data science and machine learning with Python

Most of the libraries used for data science or machine learning have Python interfaces, this feature makes the language a very popular sophisticated command interface for machine learning libraries and other numerical algorithms.

Why is Python so Popular?

Python has seen tremendous growth and popularity in recent times. As it is widely used not only in data science but also in AI, IoT, and other technologies. The quality tools in python from a mathematical or statistical stream make python a more significant language for data scientists around the globe.

The main reason for python to be used worldwide are:
Python is comparatively very fast and more efficient than compared to other tools.
The developers have successfully designed various applications in python which is reused by other users, which makes life for other developers easy and fruitful.
The syntax in python is easy and understandable thus building an application becomes a comfortable task with a smooth co-debase. 

Python offers many choices for web development:

  • Structured frames such as Django and Pyramid.
  • Massive frameworks such as Flask and Bottle.
  • Leading content management systems such as Plone and Django CMS.
    Python's standard library supports many Internet protocols:
  • E-mail processing.
  • Support for FTP, and IMAP protocols.
  • HTML and XML
  • JSON
And the Package Index has yet more libraries:
  • Requests, a powerful HTTP client library.
  • feed parser for parsing RSS/Atom feeds.
  • Paramiko, implementing the SSH2 protocol.
  • Twisted Python, a framework for non-parallel network programming.
  • Scientific and Numerical libraries

Some of the most favored libraries by developers are NumPy, Matplotlib, Scikit-learn, Apache Spark, pandas, TensorFlow, etc
Let us know some of them which will help you understand python better.

Python Libraries


  • It is an open-source library used for premium, high-end, and prominent calculations.
  • It is applicable in machine learning and deep learning algorithms. 
  • It is a Python library that is used to solve complex statistical calculations.


  • It is widely used in mathematical operations, linear algebraic codes, multidimensional arrays, Fourier transform, and models.
  • It is a free open-source program. 
  • The key core of NumPy is well-optimized C code. 
  • Relish the consent of Python with the speed of collected code.


  • Have your vision clear in python with matplotlib. 
  • It is a data visualization and graphical library for python. 
  • It generates a crystal clear vision for the developer in python. 
  • It builds interactive or static line graphs, boxplots, bar charts, etc. 
  • It is the developers' favorite application when it comes to adding graphical plots in the programming interfaces.
  • Matplotlib bids as an open-source alternative to MATLAB. 

Download matplotlib as a binary package from the Python Package Index (PyPI), with the command: python -m pip installs matplotlib.


  • PyTorch is one of the biggest machine-learning libraries.
  • It has premium APIs to perform tensor calculations with strong GPU acceleration. 
  • The neural network issues are efficiently solved by PyTorch.


  • “SciPy” stands for “Scientific Python”. 
  • It is an advanced open-source library used for high-tech scientific calculations. 
  • The SciPy library is built on the Numpy platform. 
  • SciPy stocks the numerical data. 
  • Numpy plots the data sorting and indexing.


  • Pandas are suitable for a wide range of data types. 
  • It is a tool that can analyze and manipulate data.
  • Data can be read in various formats.
  • Pandas are exclusively used in the production of financial applications. 
  • Merging or joining Data Frames
  • Summarizing data by pivoting or reshaping
  • The most convenient way to install pandas is to install them as part of the Anaconda distribution, a cross-platform distribution for data analysis. This is the most recommended installation method for developers.

Python fundamentals

  • The data types that you must know well are, integers (int), floats (float), strings (str), and boolean (bool).
  • know more about the compound data types (lists, tuples, and dictionaries).
  • Python uses Boolean variables to assess conditions and optimize  code.
  • Loops can help you to remove the overhead of code redundancy and perform repetitive tasks.
  • Functions are a great way to manage your code.
  • Object-oriented programming and external libraries


Let us reveal the facts as to why exactly data science is using python.

Python is a multifaceted and flexible language with easy readability. However, Python usage is comparatively new. As a result, Python libraries such as Pandas help in data cleanup and perform advanced calculations. The advanced expansion of Python in data science has simultaneously raised with that of Pandas, which opened a wider audience range for data analytics by enabling it to deal with row-and-column datasets and import CSV files. One of the main features of Python is that its flexible, easy, and understandable and enables the data scientist to use one tool at every step during the development process. Whether you have an industry unit that needs Python for advanced data analysis or an experienced developer interested in expansion, python can always be a helpful hand in providing your business with automated solutions.

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