NumPy is not just more efficient; it is also more convenient. NumPy's arrays are more compact than Python lists -- a list of lists as you describe, in Python, would take at least 20 MB or so, while a NumPy 3D array with single-precision floats in the cells would fit in 4 MB.
- In Class Instruction: 4 Hours
- In Class code along Dataset: Weather Dataset
- Project Dataset: Indian Premier League
- Estimated Time to complete Project Tasks: 1 Hours
- Total sub tasks within the Project: 6
- Complexity of sub tasks : Mid to High
- Points to be scored : 700
- Why should you care about this project: This project challenges you to manipulate large datasets without using conventional programming techniques to extract business insights.
- Skills Rehearsed
- Python
- Instructor led concept onboarding
- Code Alongs
- In Class Quiz Administration
- Periodic Recap - Closer to the end of session
- In Class Assignments - Motivation
- Take Away Assignments
NumPy is a Python package to efficiently do data science. Learn to work with the NumPy array, a faster and more powerful alternative to the list, and take your first steps in data exploration.
Though some people consider OOP to be a modern programming paradigm, the roots go back to 1960s. The first programming language to use objects was Simula 67. Why use Object Oriented Programming? Because if you stick to the rules, it makes complex code easier to develop, more relieable, more maintainable, and generally better.
- Because OOP insists that you think about what you expose to the outside world, it lets you change the implementation of an object without affecting any other code. (Encapsulation)
- Because it allows you to have many different functions, all with the same name, all doing the same job, but on different data. (Polymorphism)
- Because it lets you write generic code: which will work with a range of data, so you don't have to write basic stuff over, and over again. (Generics)
- Because it lets you write a set of functions, then expand them in different direction without changing or copying them in any way. (Inheritance)
After this lesson, you'll be able to
- Implement Functions, Modules & Classes in Python
- Perform Exception Handling in Python
- Create & Manipulate NumPy arrays
- Introduction to OOP Concepts
Check the Jupyter Notebook in the top right of the screen
- Learning Python for Data Science from Scratch
- Complete Codecademy's free "Learn Python" course to practice your Python syntax. Pay close attention to lists, dictionaries, and functions.
In IPL teams representing Indian cities contend each year. Chris Gayle is the highest run scorer in IPL. Do you know who is the second highest run scorer (without using ‘for’ loop)? This module can help you determine the second highest run scorer by manipulating large data sets to extract business insights.
This project challenges you to manipulate large datasets without using conventional programming techniques to extract business insights.