Lesson 1: Definition and scope of data science
Lesson 2: Applications and impact of data science in various industries Session

Lesson 1: Descriptive and inferential statistics
Lesson 2: Probability distributions and their applications

Lesson 1: Introduction to data visualization tools (Matplotlib, Seaborn)
Lesson 2: Creating effective visualizations to communicate insights

Lesson 1: Introduction to Pandas for data manipulation
Lesson 2: Handling missing data, merging datasets, and other advanced operations

Lesson 1: Exploring and understanding data distributions
Lesson 2: Identifying patterns and outliers in the data

Lesson 1: Overview of machine learning concepts
Lesson 2: Types of machine learning: supervised, unsupervised, and reinforcement learning

Lesson 1: Practical implementation of machine learning models using scikit-learn
Lesson 2: Model evaluation and performance metrics

Lesson 1: Ensemble learning: bagging and boosting
Lesson 2: Feature engineering and selection techniques

Lesson 1: Overview of artificial intelligence and its applications in data science
Lesson 2: Understanding neural networks and deep learning

Lesson 1: Defining project goals and objectives
Lesson 2: Creating a project plan and timeline

Lesson 1: Group collaboration on real-world data science projects
Lesson 2: Peer and mentor feedback on project implementation