Introduction to Data Science in Python
Learn Python basics and explore data manipulation using Pandas to clean, analyze, and visualize tabular data.
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Learn Python basics and explore data manipulation using Pandas to clean, analyze, and visualize tabular data.
Use Python data structures and object-oriented design to enhance creative projects and develop computational thinking for visual design.
Learn to design and use Python classes, inheritance, and automated testing to build structured and reusable code.
Create a portfolio-ready software engineering project using Python libraries for image recognition and manipulation.
Build confidence in inferential statistics using Python to estimate population values and test hypotheses with real-world data.
Retrieve and process complex web data using Python APIs, list comprehensions, and build a real-world tag recommendation system.
Discover how to use unsupervised learning techniques to find patterns in data, including clustering, topic modeling, and dimensionality reduction.
Apply debugging strategies and data science tools to analyze real-world datasets and document your coding process in a capstone project.
Learn to code visually with Python through dynamic shapes and data structures using the Processing platform.
Apply Python and scientific libraries like NumPy and SciPy to solve statistical problems and explore probability, distributions, and relationships in data.
Learn how to integrate Meta’s Llama 2 into Python workflows using prompting, quantization, and open-source LLMs for generative AI.
Learn Python from scratch and build your first programs using simple instructions—no math or coding experience required.
Use Python and NetworkX to analyze complex systems like epidemics and social media using network theory and diffusion models.
Master text cleaning, classification, and topic modeling using Python and NLP to extract meaning from large text datasets.
Learn to create meaningful data visualizations in Python using matplotlib and design principles for clarity and insight.
Apply machine learning techniques in Python to build, validate, and optimize predictive models using scikit-learn.
Learn structured debugging techniques in Python using loops, control structures, and the OILER framework to write and troubleshoot code effectively.
Learn statistical fundamentals, including study design, data visualization, and inference, while using Python tools like Pandas and Matplotlib to analyze data.
Master user-defined functions, dictionaries, and file handling to perform text analysis and compute sentiment from social media data.
Learn to access, parse, and work with web data using Python, including APIs, HTML, XML, and JSON formats.
Apply your Python skills in a final project to retrieve, process, and visualize data using real-world datasets.
Master Python fundamentals and creative coding skills to automate tasks, tell stories, and build expressive digital projects.
Learn to extract patterns from real-world datasets using data mining principles and Python for business and social insights.
Begin coding with Python 3 by learning control structures, data types, and visual programming with Turtle graphics.
Use Python to create generative art and dynamic visual designs with project-based coding in Processing.