Introduction
Data science forms the backbone of modern AI systems, providing the statistical tools and analytical methods needed to extract insights from data. This course gives participants a comprehensive introduction to the data science workflow, from data exploration to modeling and evaluation. Attendees will work with real datasets to practice essential data manipulation techniques. By developing strong analytical foundations, learners will be prepared for more advanced AI and ML topics.
Course Objectives
- Understand core data science methodologies
- Learn data cleaning, analysis, and visualization techniques
- Explore statistical foundations for AI
- Build and evaluate basic predictive models
- Develop practical analytical skills
Target Audience
- Students entering data science or AI
- Analysts wanting to improve modeling skills
- Developers transitioning into data roles
- Researchers working with datasets
- Anyone seeking foundational data skills
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
Expand all sectionsCollapse all sections
- Day 1: Data Science Foundations• What is data science?
• The data science lifecycle
• Types of data
• Exploration techniques
• Hands-on: Data inspection0 - Day 2: Data Wrangling• Data cleaning
• Handling outliers
• Merging and joining datasets
• Feature generation
• Hands-on: Data transformation0 - Day 3: Exploratory Data Analysis• Statistical summaries
• Correlations
• Visualizations
• Identifying patterns
• Hands-on: EDA on a dataset0 - Day 4: Intro to Predictive Modeling• Regression basics
• Classification basics
• Model evaluation
• Avoiding common pitfalls
• Hands-on: Build a simple model0 - Day 5: Real-World Applications• Reporting insights
• Data storytelling
• Data ethics
• Model deployment basics
• Capstone analysis0
Instructor
Lessons:
