Data Science and Preprocessing

Level 2: Data Science and Preprocessing

Level 2 dives into the critical role of data science and preprocessing in machine learning, providing the skills to prepare and analyze data effectively.

Level 2 Topics

This module includes:

1. Data Collection and Management: Techniques for efficient data gathering and management.
2. Data Preprocessing: Essential steps for cleaning and preparing data.
3. Exploratory Data Analysis: Tools and techniques for data exploration.
4. Feature Engineering: Strategies for creating and selecting meaningful features.
5. Data Visualization: Visualization tools and techniques to understand data better.

Level 2 Learning Outcomes

• Master data collection and management techniques.
• Perform data preprocessing and exploratory analysis.
• Apply feature engineering to improve model performance.
• Utilize data visualization tools to analyze and present data insights.

Level 2 Lecture Topics and Materials

• Guides on data collection methods and management practices.
• Tutorials on cleaning, normalizing, and transforming data.
• Workshops on exploratory data analysis and feature engineering.
• Sessions on data visualization tools and techniques.

These modules aim to build a strong foundation in AI and machine learning, preparing you for the advanced topics covered in Levels 3 and 4.