Python for Data Science

Python is a general-purpose interpreted, interactive, object-oriented, and high-level programming language. Guido van Rossum created it during 1985–1990.

Python is a general-purpose coding language — which means that, unlike HTML, CSS, and JavaScript, it can be used for other types of programming and software development besides web development.

Why Learn Python?

  • Python is Interpreted − Python is processed at runtime by the interpreter. You do not need to compile your program before executing it. This is similar to PERL and PHP.
  • Python is Interactive − You can actually sit at a Python prompt and interact with the interpreter directly to write your programs.
  • Python is Object-Oriented − Python supports an Object-Oriented style or technique of programming that encapsulates code within objects.
  • Python is a Beginner’s Language − Python is a great language for beginner-level programmers and supports the development of a wide range of applications from simple text processing to WWW browsers to games.

Python is quickly ascending to the forefront of the most popular programming languages in the world. The incredible growth of Python is shown very clearly by StackOverflow:

What is Python Used For?

1- General Web Development / Building Web Apps

2- Scientific Computing + Data Science

3- Machine Learning

4- Startups

5- Fintech + The Financial Industry

Python is a rising star in the programming world for two main reasons: the big range of tasks it can handle, combined with the fact that it’s actually a very beginner-friendly language. Python code syntax uses English keywords, and that makes it easy for anyone to understand and get started with the language. For example, take a look at the code you’d use to print the text “Hello World” on your screen using the programming language Java:

Now take a look at the same exercise written in Python code:

Installing Python is generally easy. Python distribution is available for a wide variety of platforms. Before getting started, you may want to find out which IDEs and text editors are tailored to make Python editing easy, browse the list of introductory books, or look at code samples that you might find helpful.

Anaconda is an integrated distribution that hosts different programming languages for Data Science and similar scientific applications. In addition to Data Science and Artificial Intelligence libraries, it has tools such as Jupyter Notebook, Jupyter Lab, and Spyder.

PyCharm is a dedicated Python Integrated Development Environment (IDE) providing a wide range of essential tools for Python developers, tightly integrated to create a convenient environment for productive Python, web, and data science development.

Data Types:

Python for Data Science

Since Python is the go-to programming language for domains such as artificial intelligence, machine learning, and deep learning, it’s no surprise that it’s also a fundamental tool for any data scientist.

With data often described as the new oil, the success of any business that works with it depends on the ability to extract insights from large databases and make strategic decisions based on them. Python allows organizations to analyze and visualize data in meaningful ways to identify patterns, track down relationships, and help solve complex business problems.

What are the best Python libraries for Data Science?

Even though it’s feasible to use Python on its own to work with data, the job becomes much easier with some of its useful libraries. Here are just some of the most common use cases and popular libraries.


  • NumPy
  • Pandas

Web scraping

  • Scrapy
  • Beautiful Soup

Distributed processing

  • PySpark
  • Dask


  • Matplotlib
  • Bokeh
  • Plotly
  • Seaborn
  • pydot

Machine learning

  • scikit-learn
  • XGBoost

Deep learning

  • TensorFlow
  • PyTorch
  • Keras

Data Science Enthusiast — For more information check out my LinkedIn page here: