Introduction to Data Cleaning and Transformation
Dive deeper into data selection and manipulation, learning how to filter datasets based on specific conditions, clean data by handling missing values, and create new derived features from existing data. Each step builds your skillset, preparing you to tackle more complex data cleaning challenges.
Lessons and practices
Identifying Top Students in Math or History
Flexing with Logical Operators
Academic Prospects: Filtering Excellence
Math Grade Review with Conditional Selection
Filling the Academic Gaps
Median Touch-Up in Gradebook Data
Filling in the Blanks: Average Grade Calculation
Identifying Missing Values in Student Scores Dataset
Filling the Void: Handling Missing Grades
Cleaning Up the Data Galaxy
Inventory Reorder Indicator
Inventory Calculation Correction
Adding Stock Order Column to Inventory DataFrame
Add a Store Location Column to Inventory Data
Standardizing T-Shirt Sizes in Data Analysis
Normalize Apparel Sizes in Data Set
Outlier Detection in Fashion Retail Prices
Fashion Size Outlier Removal
Scaling Sizes in Fashion Retail
Scaling Dress Prices in Fashion Retail Data
Interested in this course? Learn and practice with Cosmo!
Practice is how you turn knowledge into actual skills.