English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 115 lectures (11h 28m) | 4.73 GB
Get started with Python programming and the Pandas library while building a deep Object-Oriented Programming foundation!
This course bundle includes a Python Project and downloadable course instructor and exercise files to work with and follow along.
If you need a better tool for handling data, it’s time to get to know Python, and this amazing value three-course Python bundle is the perfect place to start.
Python is one of the most popular languages for data analysis and business intelligence, with Pandas as one of the most commonly used Python libraries. At the same time, Object-Oriented Programming is particularly important in Python since every piece of Python code interacts with objects and classes.
The introduction to Python course assumes no prior knowledge and guides you through getting set up and started, with practice exercises and a full project to complete at the end of the course.
Pandas for beginners will introduce you to the basics of data analysis and assumes no previous Pandas experience. But since Pandas is a package built for Python, you need to have a fundamental understanding of basic Python syntax, which you should learn in the first course.
To get the most out of the OOP course, you need to be at an intermediate level in Python programming.
What’s included?
Introduction to Python:
- The basic data types in Python – strings, integers, floats, and Boolean
- All about Python’s built-in functions
- How variables and functions work in Python
- How to debug errors in Python
- All about Python keywords
- How to use If-Else statements in Python
- All about storing complex data, including lists and dictionaries
- All about Python modules and how to install them
- How to install Python locally
- How to write your first script in Python
- To complete your first Python project
Object-Oriented Programming:
- Describe the meaning of the object-oriented paradigm and create class hierarchies using the object-oriented design process.
- Understand the difference between class variable and instance variable as well as the difference between class method, instance method, and static method.
- Make an object indexable (like lists), callable (like functions), and comparable (like numbers).
Design and implement Python programs for complex problems, making good use of the language’s features such as classes and - inheritance.
Pandas for Beginners:
- An overview of Pandas
- Installing Pandas on your computer
- Using the two primary Pandas data structures, Series and DataFrame
- Viewing data imported from an external source
- Organizing input data using indexing and filtering
- Using Pandas for data preprocessing
- Addressing missing values and duplicate rows
- Formatting your data most efficiently
- Processing different data types
- Data manipulation using string functions
- Date and time formatting
What you’ll learn
- The basic data types in Python – strings, integers, floats, and Boolean
- Python’s built-in functions and Python keywords
- How to write your first script in Python
- How variables and functions work in Python and how to debug errors
- How to write your first script in Python and use If-Else statements
- Storing complex data, including lists and dictionaries
- All about Python modules and how to install them
- Installing Python locally and completing your first Python project
- Describe the meaning of the object-oriented paradigm and create class hierarchies using the object-oriented design process
- Understand the difference between class variable and instance variable as well as the difference between class method, instance method, and static method.
- Make an object indexable (like lists), callable (like functions), and comparable (like numbers).
- Design and implement Python programs for complex problems, making good use of the language’s features such as classes and inheritance.
- An overview of Pandas and installing Pandas on your computer
- Using the two primary Pandas data structures, Series and DataFrame
- Viewing data imported from an external source and organizing input data using indexing and filtering
- Using Pandas for data preprocessing and addressing missing values and duplicate rows
- Formatting your data most efficiently and processing different data types
- Data manipulation using string functions and date and time formatting
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