📊Artificial Intelligence in Data Science and Analytics

Technology & Data · 11 units · free, no membership

1 Introduction to Artificial Intelligence in Data Science and Analytics: Workflow, Roles, Boundaries, Validation and Ethics

2 Data Collection and Source Understanding: Schema, Sampling, Quality and Leak Awareness

3 Data Cleaning and Preprocessing: Missing Value, Outlier and Type Conversion

4 Exploratory Data Analysis (EDA): Distributions, Relationships, and Initial Insights

5 Feature Engineering: Generating Variants, Coding, Scaling, and Avoiding Leakage

6 Model Building: Problem Definition, Algorithm Selection, and Training/Test Split

7 Model Evaluation: Metrics, Cross-Validation, and Extreme Learning

8 Visualization and Storytelling: Accurate Graphics, Honest Graphics

9 Code Generation: AI-Assisted Analysis with Python (pandas) and SQL

10 Data Leakage and Reproducibility: Silent Disasters and Discipline

11 MLOps Foundation, Ethics, Privacy and Responsible Analytics

All trainings Start from unit 1