In this course, you’ll explore three exploratory data analysis (EDA) practices: cleaning, joining, and validating. You'll discover the importance of these practices for data analysis, and you’ll use Python to clean, validate, and join data. By the end of this course, you will be able to: • Apply input validation skills to a dataset with Python • Explain the importance of input validation • Demonstrate how to transform categorical data into numerical data with Python • Explain the importance of categorical versus numerical data in a dataset • Explain the importance of recognizing outliers in a dataset • Demonstrate how to identify outliers in a dataset with Python • Understand when to contact stakeholders or engineers regarding missing values • Explain the importance of ethically considering missing values • Demonstrate how to identify missing data with Python
MM
From the Python course, I learned the foundational skills of programming, such as writing code, using variables, loops, and functions, and understanding how to solve problems using Python.”
TH
his course helped me understand the basics clearly and improved my practical skills. Well structured and easy to follow.
Showing: 5 of 5
From the Python course, I learned the foundational skills of programming, such as writing code, using variables, loops, and functions, and understanding how to solve problems using Python.”
It's an amazing course with so much useful and practical material. It's ideal to start your Data science/Data Analysis journey with Python.
his course helped me understand the basics clearly and improved my practical skills. Well structured and easy to follow.
good
good of coures