Full Stack Data Science & AI: Data Cleaning, Roles & ML Roadmap | Day 2

Day 2 of the Full Stack Data Science & AI Course deep-dives into core concepts! Learn:

Data science defined: Predicting outcomes from past data using ML/AI (admission/salary/sales/BMI examples)

Critical data cleaning: Handle missing values (zero-fill experience, mean-imputation test scores)

Role breakdown: Data analyst vs. engineer vs. scientist responsibilities clarified

Full learning path: Python → statistics → ML → AI (NLP/computer vision) → Azure/Flask deployment → Power BI visualization

Key insight: "Data preprocessing is 70% of data science work!" 🔧 Includes admission predictors, sentiment analysis tools & sales forecasters.