From the web, It is said that in today’s world, the ability to code continues to grow in importance. Coding is no longer the sole domain of computer scientists and programmers, but rather a useful skill to have in any career.

Kids with an eye to their future know that learning to code is important, but figuring out which one to learn can be an intimidating task. Some languages are easier to learn, while others have a wider application. But one language sits right in the sweet spot.With a balance of being both easy to learn and widely used in the real world, we suggest learning Python for kids/youth. This shows that it is very easy to learn Python.

There are lots of modules, libraries, tools to make data pratitioner life easier when they adopt python as their choice in creating ML solutions. You will use python to gather data, prepare the data, visualize the data, build Machine Learning Models, and make ML solutions available to relevant stakeholders via Web application, Dashborad as the case may be. Python Proframming allows you to create end to end solution as a data practitioner.

1. It is the most widely used Programming Language for Data Science
2. It has a wide and large Community Support so your bugs are always going to have a solution somewhere on the internet
3. It is easy to learn and provides framework for many different use case

Python does not specifically solve the problem, it allow you bring your ideas into life by enabling you to programmatically structure your ideas and present it in a mobile, web or embedded system applications.

Are you completely new to programming? If not then we presume you will be looking for information about why and how to get started with Python. Fortunately an experienced programmer in any programming language (whatever it may be) can pick up Python very quickly. It's also easy for beginners to use and learn, so jump in!.

The link above contains resources for mastering the art of using python programming to create different kinds of applications as well as how to use it for Data Science. Specifically, DataCamp Python Tutorial Unlike most other Python tutorials, this 4 hour tutorial by DataCamp focuses on Python specifically for Data Science. It has 57 interactive exercises and 11 videos. Here, you'll learn about Python Basics, Data Structures like lists, Functions and Packages and Numpy a fundamental Python package to efficiently practice data science. You can proceed to learning Python Intermediate which include modules like

Intro to Matplotlib: makes it easy to create meaningful and insightful plots. In this chapter, you’ll learn how to build various types of plots, and customize them to be more visually appealing and interpretable.

Dictionaries and Pandas: an alternative to the Python list, and the pandas DataFrame, the de facto standard to work with tabular data in Python. You will get hands-on practice with creating and manipulating datasets, and you’ll learn how to access the information you need from these data structures.

Logic Control Flow and FIltering: Boolean logic is the foundation of decision-making in Python programs. Learn about different comparison operators, how to combine them with Boolean operators, and how to use the Boolean outcomes in control structures. You'll also learn to filter data in pandas DataFrames using logic.

Loops: There are several techniques you can use to repeatedly execute Python code. While loops are like repeated if statements, the for loop iterates over all kinds of data structures. Learn all about them in this chapter.

Case Study: Hacker Statistics: This chapter will allow you to apply all the concepts you've learned in this course. You will use hacker statistics to calculate your chances of winning a bet. Use random number generators, loops, and Matplotlib to gain a competitive edge!

The Next Step to mastering Python for Data Science will include learning about Python Data Science Toolbox which is taught by Datacamp. The time to push forward and develop your Python chops skills even further has come Don't deny it. There are tons of fantastic functions in Python and its library ecosystem. However, as a data scientist, you'll constantly need to write your own functions to solve problems that are dictated by your data. You will learn the art of function writing in this first Python Data Science Toolbox course. You'll come out of this course being able to write your very own custom functions, complete with multiple parameters and multiple return values, along with default arguments and variable-length arguments. You'll gain insight into scoping in Python and be able to write lambda functions and handle errors in your function writing practice. And you'll wrap up each chapter by using your new skills to write functions that analyze Twitter DataFrames. Here you will learn

How to write your own Functions
How to specify default arguments, variable-length arguments and scope. What they mean, when to use them and why we use them
You'll also learn about Lambda Functions and how to handle errors

Then Improve your knowledge with the second part of Python Data Science Toolbox. Here you'll continue to build your Python data science skills. First, you'll learn about iterators, objects you have already encountered in the context of for loops. You'll then learn about list comprehensions, which are extremely handy tools for all data scientists working in Python. You'll end the course by working through a case study in which you'll apply all the techniques you learned in both parts of this course.

How good are your Python skills? Test and Training with more than 300 hand-picked Python puzzles here

If you are the book type and just want to read on screen and complete exercise then check this out. You can also check this resource out, Automate the Boring Stuff with Python - Practical Programming for Total Beginners by Al Sweigart is "written for office workers, students, administrators, and anyone who uses a computer to learn how to code small, practical programs to automate tasks on their computer." You can access the book here

1. Object Oriented Programming (OOP) Another trick in software is to avoid rewriting the software by using a piece that’s already been written, so called component approach which the latest term for this in the most advanced form is what’s called Object Oriented Programming. — Bill Gates.

Python for Data Science is mostly procedural especially when prototyping a solution but when it time to automate process, it is advised to split different aspect of the solution into separate scripts. To do this, the knowledge of OOP will come to a good use. Object-oriented programming (OOP) is a method of structuring a program by bundling related properties and behaviors into individual objects. In this tutorial, you’ll learn the basics of object-oriented programming in Python.

In this tutorial, you’ll learn how to:

• Create a class, which is like a blueprint for creating an object
• Use classes to create new objects
• Model systems with class inheritance

• Begineers
  • Ability to use procedural approach to write codes
  • use of notebook to create ML models
• Experts
  • ability to use wrote python codes in OOP approach
  • adhere to PEP8 guidelines

KDNuggets article on OOP explains how to use the approach in Data Science Project. Specifically, it explains how to use OOP to create a Linear Regression Model which you can easily extend to create any other algorithms