Advanced Python for Data Analysis and Machine Learning
Take Your Python and Data Skills to the Next Level
Ready to move beyond the basics and dive into real data analysis and machine learning? This course is designed for students who already understand Python fundamentals and are eager to expand their skills in analyzing and visualizing data, preparing datasets, and building machine learning models.
What is it?
An advanced-level course for students aged 12–18 (with prior Python experience) to:
Strengthen programming skills with complex Python concepts.
Analyze and manipulate real-world data using NumPy and Pandas.
Create advanced visualizations using Matplotlib.
Preprocess datasets and apply machine learning models like KNN and Decision Trees.
Evaluate models and compare performance metrics.
What will you learn?
Advanced use of Python functions, data structures, and file handling.
Efficient array processing using NumPy.
In-depth data visualization with Matplotlib.
Data analysis and cleaning with Pandas.
Preprocessing techniques for machine learning.
Implementing K-Nearest Neighbors and Decision Tree classifiers.
Basic model evaluation and performance comparison.
What do you need?
A computer with internet access.
Familiarity with basic Python programming.
Python interpreter (Jupyter Notebooks recommended).
Python libraries: NumPy, Matplotlib, Pandas, Scikit-learn.
What's the workload?
Approximately 10–12 hours of learning time, including hands-on coding, data analysis.
What you’ll do:
Strengthen Your Python Skills
You’ll revisit core concepts and learn how to write more complex functions, use advanced data structures, and handle files for data input/output.
Analyze Real Data
You’ll use NumPy and Pandas to explore and clean datasets, uncover insights, and prepare them for modeling.
Build and Evaluate ML Models
You’ll apply machine learning algorithms like KNN and Decision Trees, and compare their performance using evaluation metrics like accuracy and confusion matrices.
Why this course is powerful:
From Code to Intelligence
Not just coding – this course teaches you how to make data-driven decisions and build intelligent programs.
Hands-On and Realistic
You'll work with real-world data, simulate classification problems, and evaluate model outcomes.
Pathway to Machine Learning and Beyond
This course is an ideal stepping stone for deeper study in AI, data science, and advanced programming topics.
Course Structure
Module Description
Course Information Overview of the course (what’s included, tools needed, navigation guide, Q&A forum link).
Module 1: Python Refresher and Advanced Concepts Review Python basics and introduce advanced functions, data structures, file handling, and object-oriented concepts.
Module 2: NumPy for Data Manipulation Learn to reshape, slice, and perform statistical operations on arrays using advanced NumPy techniques.
Module 3: Data Visualization with Matplotlib Create various chart types (line, scatter, bar, histogram, boxplot, heatmap) and customize plots with Matplotlib.
Module 4: Data Analysis with Pandas Learn to use DataFrames, clean data (handle missing values, remove duplicates), and perform aggregation and filtering.
Module 5: Data Preparation for Machine Learning Prepare data for machine learning by encoding, scaling, and splitting into training and testing sets.
Module 6: K-Nearest Neighbors (KNN) Classification Understand and implement the KNN algorithm using Euclidean distance to classify simple datasets.
Module 7: Decision Tree Classification Build, visualize, and interpret Decision Tree models for classification tasks.
Module 8: Model Evaluation and Selection (Introduction) Compare models using accuracy and confusion matrix. Learn how to select and evaluate models for different problems.