Beginner Data Science with Python and Jupyter

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This hands-on course introduces data science fundamentals using the Python programming language and the powerful Jupyter Notebook platform. You’ll explore everything from data clea ning and machine learning to web scraping and interactive visualization—turning raw data into smart insights with real-world relevance. Beginner Data Science with Python and Jupyter Benefits In this course, you will: Learn by doing: practice with real datasets and coding exercises. Gain fluency in Jupyter Notebooks, Python libraries, and machine learning tools. Build models to solve classification problems and interpret results. Scrape data from the web and visualize it like a pro. Walk away with a toolkit to tackle data-driven problems in any domain. Prerequisites Knowledge of programming fundamentals and some experience with Python, including Python libraries, Pandas, Matplotlib, and scikit-learn. Beginner Data Science with Python and Jupyter Training Outline Learning Objectives Lesson 1: Jupyter Fundamentals Introduction to Jupyter Notebooks Data exploration using the Boston Housing Dataset First steps in predictive analytics Lesson 2: Data Cleaning & Machine Learning Data preprocessing strategies Classification models: SVM, KNN, Random Forests Model evaluation and dimensionality reduction (PCA) Lesson 3: Web Scraping & Interactive Visualizations HTTP requests & HTML parsing with Python Data scraping with Beautiful Soup Building interactive plots with Bokeh Lesson 4: Applied Projects Investigating datasets through EDA Training and tuning classification models Creating impactful, interactive visualizations Show more