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.
Learn how to load and save datasets in CSV format using the Pandas library.
Participants will be able to read, explore, and export CSV files in Python using Pandas.
Explore techniques to filter, select, and subset rows and columns in a DataFrame.
Participants will be able to extract meaningful subsets of data for analysis.
Practice editing, adding, or deleting columns and rows within a DataFrame.
Participants will be able to clean and transform data within Pandas efficiently.
Learn to create basic visualizations directly from Pandas DataFrames.
Participants will be able to generate simple plots (line, bar, histogram, etc.) using Pandas.
An introduction to the NumPy library for numerical computing and array operations.
Participants will be able to use NumPy for mathematical operations and efficient data handling.
Get familiar with using Google Colab to write, run, and share Python code in the cloud.
Participants will be able to set up and use Google Colab as their coding environment.
A guided walkthrough of Python basics and Pandas functions inside Google Colab.
Participants will be able to execute Python and Pandas commands interactively in notebooks.
Introduction to fundamental text preprocessing techniques in Python.
Participants will be able to clean and tokenize raw text data for analysis.
Explore two key text normalization techniques used in NLP (i.e. Stemming and Lemmatization).
Participants will understand the concepts of stemming and lemmatization and their differences.
Hands-on practice applying stemming and lemmatization using Python libraries.
Participants will be able to preprocess text using stemming and lemmatization in Python code.
Learn how to represent text data as word frequency vectors.
Participants will understand the Bag of Words model and its role in NLP.
Practical implementation of Bag of Words using Python libraries.
Participants will be able to build Bag of Words representations for text datasets in Python.
Introduction to Term Frequency–Inverse Document Frequency as a text representation method.
Participants will understand how TF-IDF highlights important words in documents.
Learn to apply TF-IDF using Scikit-learn and Python tools.
Participants will be able to compute TF-IDF values for text data programmatically.
A hands-on notebook for basic working with text data.
Participants will be able to process and analyze text datasets end-to-end in Google Colab.
An introduction to two main types of machine learning tasks: regression and classification.
Participants will be able to differentiate between regression and classification problems.
Explore logistic regression as a fundamental classification algorithm.
Participants will understand how logistic regression works and when to use it.
Hands-on implementation of logistic regression using Scikit-learn.
Participants will be able to build and evaluate logistic regression models in Python.
Introduction to random forest, a powerful ensemble machine learning method.
Participants will understand the intuition and applications of random forest models.
Practical application of random forest classifiers with Scikit-learn.
Participants will be able to implement and evaluate random forest models in Python.
Learn how cross-validation helps in evaluating and improving machine learning models.
Participants will understand the concept of cross-validation and its importance in model assessment.
Apply cross-validation using Scikit-learn functions and workflows.
Participants will be able to perform cross-validation on machine learning models in Python.
A real-world project applying machine learning to predict essay scores.
Participants will be able to integrate text preprocessing and ML models into a practical educational AI application.
All notebooks as s Zip file for download and local run.