Python for AI and Data Science

Week 1: Deep Dive into Python Functions and Modules

• Advanced function concepts: decorators, closures, and higher-order functions
• Creating and using custom modules
• Activity: Build a custom Python module with reusable functions for data manipulation.

Week 2: Error Handling and Debugging

• Exception handling with try/except/else/finally
• Debugging techniques and using logging
• Mini Project: Create a file-processing script that handles various file types and includes robust error handling.

Week 3: Working with Data Structures and Algorithms

• Advanced data structures: stacks, queues, and heaps
• Implementing common algorithms (e.g., sorting, searching)
• Activity: Write a program that implements different sorting algorithms and compares their performance.

Week 4: Introduction to Data Manipulation with pandas

• Loading, cleaning, and transforming data with pandas
• Basic data analysis and exploration techniques
• Milestone Project: Develop a data-cleaning script that processes a messy dataset, removes duplicates, and formats data for analysis.

Week 5: Data Analysis with pandas and numpy

• Data wrangling and manipulation using pandas
• Using numpy for numerical operations and array handling
• Activity: Analyze a real-world dataset to identify trends and generate insights.

Week 6: Data Visualization with matplotlib and seaborn

• Creating and customizing visualizations (bar charts, line graphs, histograms)
• Using seaborn for advanced visualizations
• Mini Project: Build a data visualization dashboard that displays insights from a chosen dataset.

Week 7: Introduction to Exploratory Data Analysis (EDA)

• Identifying and handling missing data, outliers, and anomalies
• Summary statistics and visualization techniques for EDA
• Activity: Perform EDA on a public dataset (e.g., COVID-19 data) to identify key patterns.

Week 8: Milestone Project

• Project: Conduct a full exploratory data analysis (EDA) on a dataset of choice, presenting findings through a series of visualizations and insights.

Week 9: Basics of Machine Learning with scikit-learn

• Overview of machine learning concepts: supervised and unsupervised learning
• Training and evaluating simple models (e.g., linear regression)
• Activity: Build a linear regression model to predict a numerical outcome, like housing prices or sales.

Week 10: Classification Models and Evaluation Metrics

• Building classification models (e.g., decision trees, k-nearest neighbors)
• Model evaluation with metrics like accuracy, precision, recall, and F1-score
• Mini Project: Develop a classification model to predict an outcome (e.g., classify email as spam or not spam) and evaluate its performance.

Week 11: Introduction to Neural Networks and Deep Learning

• Basics of neural networks and deep learning concepts
• Building simple neural networks using TensorFlow or Keras
• Activity: Create a neural network model to classify images from a small dataset (e.g., digit recognition).

Week 12: Final Project and Presentation

• Final Project: Develop a complete machine learning project, such as a predictive model that analyzes text, images, or numerical data.
• Presentation Day: Students present their machine learning projects, explaining the model selection, training process, and key insights.