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.
Overview
Syllabus
- Lesson 1: Deep Dive into Conditional Selection
- Identifying Top Students in Math or History
- Flexing with Logical Operators
- Academic Prospects: Filtering Excellence
- Math Grade Review with Conditional Selection
- Lesson 2: Handling Missing Values
- 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
- Lesson 3: Creating New Columns in Pandas
- Inventory Reorder Indicator
- Inventory Calculation Correction
- Adding Stock Order Column to Inventory DataFrame
- Add a Store Location Column to Inventory Data
- Lesson 4: Data Cleaning and Transformation
- 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
