Python Programming For Data Science and Machine Learning

Python is a general-purpose, high-level, object-oriented, and easy to learn programming language. It was created by Guido van Rossum, who is known as the godfather of “Python”.

                         

Python is a popular programming language because of its simplicity, ease of use, open-source licensing and accessibility — the foundation of its renowned community which provides great support and help in creating tons of packages, tutorials, and sample programs.

Python can be used to develop a wide variety of applications — ranging from Web, Desktop GUI based programs/applications to science and mathematics programs, and Machine learning and other big data computing systems.

Let’s explore the use of Python in Machine Learning, Data Science and Data Engineering.

Machine Learning

Machine learning is a relatively new and evolving system development paradigm that has quickly become a mandatory requirement for companies and programmers to understand and use. See our previous article on Machine Learning for the background. Due to the complex, scientific computing nature of machine learning applications, Python is considered the most suitable programming language. This is because of its extensive and mature collection of mathematics and statistics libraries, extensibility, ease of use and wide adoption within the scientific community. As a result, Python has become the recommended programming language for machine learning systems development.

Data Science

Data science combines cutting edge computer and storage technologies with data representation and transformation algorithms and scientific methodology to develop solutions for a variety of complex data analysis problems encompassing raw and structured data in any format. A Data Scientist possesses knowledge of solutions to various classes of data-oriented problems. And expertise in applying the necessary algorithms, statistics, and mathematic models, to create the required solutions. Python is recognized among the most effective and popular tools for solving data science-related problems.

Data Engineering

Data Engineers build the foundations for Data Science and Machine Learning systems and solutions. Data Engineers are technology experts who start with the requirements identified by the data scientist. These requirements drive the development of data platforms that leverage complex data extraction, loading, and transformation to deliver structured datasets that allow the Data Scientist to focus on solving the business problem. Again, Python is an essential tool in the Data Engineer’s Toolbox — one that is used every day to architect and operate the big data infrastructure that is leveraged by the data scientist.

Use cases for Python, Data Science and Machine Learning

Here are some example Data Science and Machine Learning applications that leverage Python.

• Netflix uses data science too, to understand user viewing patterns and behavioral drivers. This, in turn, helps Netflix too, understand user likes/dislikes and predict and suggest relevant items to view.
• Amazon, Walmart, and Target are heavily using data science, data mining and machine learning to understand the user's preferences and shopping behavior. This assists in predicting demands to drive inventory management and to suggest relevant products to online users or via email marketing.
• Spotify uses data science and machine learning to make music recommendations to its users.
• Spam programs are making use of data science and machine learning algorithm(s) to detect and prevent spam emails.  

This article provides an overview of Python and its application to Data Science and Machine Learning and why it is important.

 Aezion Inc. Solution Architects, Engineers, and Custom Software Developers can assist you in exploring Python-based solutions for your Data Science and Machine Learning applications. Contact us to learn more.

Overview of Python Programming Language

Python can be used to develop a wide variety of applications — ranging from Web, Desktop GUI based programs/applications to science and mathematics programs, and Machine learning and other big data computing systems.

Let’s explore some key aspects of the Python programming language to get a feel for core features and Python’s user-friendly syntax.

                         

Python – A Multi-Paradigm Language

Python is a multi-paradigm programming language. Meaning it supports different styles of writing code. One can write Python code in a procedural, object-oriented, functional or imperative manner. For this reason, Python is considered a “swiss army knife” in the developer's toolbox.

As an “object-oriented” programming language, Python supports all the core features of OOP such as abstraction, encapsulation, polymorphism, inheritance, etc. The foundation of OOP is a Class. A class is defined to represent an object that can later be programmatically created and manipulated. The class definition consists of attributes (data) and methods (functions) that collectively define the configuration and behavior of an object.

A Python program consists of a collection of classes representing key elements and their behaviors within a system.

Encapsulation

Encapsulation allows one to hide the internal details or implementation of one object from other objects (s). This protects against accidental or direct access and modifications to the data. Python relies on Encapsulation and other conventions to manage access and usage of class variables or methods. One can restrict access to variables/methods with public, private and protected. A variable or method that is prefixed with double underscore “__“ is treated as private in Python.

Polymorphism

The word “Polymorphism” means many forms. Polymorphism is a feature of OOP that allows one to define multiple forms for a common interface. Python’s implementation of polymorphism is similar to other object-oriented programming languages such as C++, Java or C#. Method overloading and overriding are two different ways with which one can achieve polymorphism in the Python programming language.

Defining a class

A class is defined using a keyword – class. Here’s an example of a student class — consisting of the constructor __init__ — a specific method that gets invoked as part of the object creation.

The code snippet above and below are reused from Reference: Python Class – https://pymbook.readthedocs.io/en/latest/classes.html

Notice how the methods are defined using the keyword – def. Notice also, that the ‘print_details’ method takes ‘self’ as a parameter and prints the student name, branch and year.

Inheritance 

Inheritance is a core object-oriented concept in the Python programming language. It allows one to reuse or extend the functionality or features of one or more classes that are “inherited” in a new class. The concept of inheritance in Python is similar to other high-level programming languages such as C#, Java, etc. 

Let’s define a Person class with a method to get the person’s details. 

Now, let us revise the “Student” class to inherit from “Person” so it can get all the features of the person. 

Once we inherit from the person, the student gets the behavior “get_details”. Notice, however, that the revised Student class overrides the behavior of “get_details” and adds its own functionality.

Note – It’s up to the implementor to decide whether to override a method or not.

This article provides an overview of the Python programming language, its core features, and simple, accessible syntax. Hopefully, this brief introduction provides a glimpse into Python’s simplicity and encourages you to explore it as a potential addition to your programmer's toolbox. 

Aezion Inc. Solution Architects, Engineers, and Custom Software Developers can assist you in exploring Python-based solutions for your Data Science and Machine Learning applications. Contact us to learn more.