Outcomes

• Understand the Data Science lifecycle (data collection, cleaning, analysis, modeling, and deployment)

• Clean, preprocess, and transform raw data into usable formats

• Perform exploratory data analysis (EDA)

• Visualize data using charts and dashboards

• Apply statistical concepts to analyze datasets

• Explain core Artificial Intelligence and Machine Learning concepts

• Implement supervised learning algorithms (e.g., regression and classification)

• Apply unsupervised learning techniques (e.g., clustering)

• Evaluate and optimize machine learning models

• Understand the basics of deep learning and neural networks

• Write Python code for data analysis and modeling

• Use libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn

• Work with Jupyter Notebook or Google Colab environments

• Build and test small AI-based projects

• Solve real-world problems using data-driven approaches

• Interpret and communicate analytical results effectively

• Understand ethical issues and responsible AI practices

• Develop confidence to pursue advanced AI and Data Science topics