This hands-on course introduces participants to advanced data analytics and AI methods with a focus on applications in education. Using freely available resources and interactive Google Colab notebooks, students will gain practical skills in Python, Pandas, NumPy, and Scikit-learn. The course covers everything from fundamental data manipulation and visualization to natural language processing (NLP) techniques (such as tokenization, stemming, lemmatization, Bag of Words, and TF-IDF) and key machine learning models for prediction and classification. By working through real examples—including essay scoring with machine learning—participants will build a solid foundation in applying AI-driven data analysis to educational contexts.

By the end of this course, participants will be able to:

• Work with CSV files and perform data manipulation using Pandas and NumPy.

• Subset, clean, and modify datasets effectively for analysis.

• Create data visualizations using Pandas plotting tools.

• Use Google Colab for running Python code and notebooks in the cloud.

• Apply text preprocessing techniques such as lowercasing, punctuation removal, stopword filtering, tokenization, stemming, and lemmatization.

• Represent text data using Bag of Words and TF-IDF methods.

• Understand and implement machine learning tasks in education, including regression and classification.

• Build and evaluate predictive models using logistic regression and random forests with Scikit-learn.

• Apply cross-validation to assess and improve model performance.

• Develop a practical project (e.g., essay score prediction) that demonstrates how AI methods can be applied to real educational challenges.