A curated collection of free-accessible educational resources and certificate programs offered by the University of Michigan via Coursera. These courses focus on teaching Python programming fundamentals, data structures, and computational thinking for beginners and career changers.
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The flagship specialization designed to teach beginners how to think like a computer scientist using Python. It covers data structures, web scraping, databases, and visualization, providing a comprehensive foundation for aspiring data analysts and developers.
The introductory course that serves as the starting point for the specialization. It teaches basic concepts like variables, conditional statements, and loops, making it ideal for those with no prior coding experience looking to understand computer programming logic.
An intermediate-level course that deepens understanding of fundamental Python structures like lists, dictionaries, and tuples. Students learn to solve complex problems using these data types, which are essential for efficient data processing and software development.
This course focuses on interacting with relational databases using Python and SQL. It teaches how to retrieve and manipulate data from large datasets, a critical skill for backend development, data engineering, and business intelligence roles.
Students learn to extract data from the web using regular expressions, parsing, and web scraping techniques. The course emphasizes handling unstructured data from HTML and XML, preparing learners for real-world data acquisition tasks.
The final course in the specialization, focusing on creating visual representations of data using Python libraries. It teaches how to generate charts and maps to communicate insights effectively, bridging the gap between raw data and actionable conclusions.
Part of the broader Michigan Computing specialization, this course introduces basic programming concepts for data analysis. It serves as a bridge for those interested in computational methods before diving into deeper statistical or machine learning topics.
An advanced module that expands on visualization techniques using Matplotlib and other libraries. It teaches how to create professional-grade charts and plots for academic and business presentations, emphasizing clarity and aesthetic design in data communication.
This course explores how Python can manage files and folders, handle email, and manage processes on a computer. It provides practical skills for automation and system administration, showing how Python serves as a powerful glue language for IT tasks.
Focuses on building graphical user interfaces (GUIs) for Python applications. Students learn to create interactive desktop apps, which is useful for developing internal tools, utility software, and prototypes for larger software systems.
While often hosted on edX, this foundational course concept aligns with U-M's teaching philosophy. It provides a rigorous introduction to computer science principles, algorithm design, and complexity analysis, serving as a strong academic complement to Coursera offerings.
A broader pathway that includes Python courses alongside R programming and data visualization. It offers a more generalist approach to computing, suitable for individuals who want to compare multiple programming languages and tools for data analysis.
Modules often embedded within data science specializations that focus specifically on Python's role in analytics. These segments cover NumPy and Pandas basics, teaching how to handle numerical data and prepare datasets for statistical modeling.
A dedicated course on OOP concepts using Python, teaching classes, inheritance, and polymorphism. This is essential for developers transitioning from scripting to building scalable, maintainable software applications and professional-grade codebases.
Although not exclusively a Python course, this resource is critical for any Python developer. It teaches how to track code changes, collaborate with teams, and deploy projects, which are mandatory skills for professional software engineering roles.
Often paired with Python courses to teach environment setup and scripting. Understanding the command line is vital for deploying Python scripts on servers, automating tasks, and working in high-performance computing or cloud environments.
Resources covering agile methodologies and testing frameworks like PyTest for Python projects. This knowledge helps ensure code quality, reliability, and maintainability, aligning academic learning with industry best practices for professional software delivery.
Focuses on the crucial step of preparing raw data for analysis using Python libraries. It covers handling missing values, outliers, and data normalization, ensuring that students can work with messy, real-world datasets effectively.
A comprehensive overview connecting Python programming to the broader data science workflow. It introduces the ecosystem of tools and libraries used in the field, helping learners understand where Python fits into the larger landscape of AI and analytics.
Specialized tracks within the curriculum that apply Python to scientific computing and academic research. These resources teach how to use Python for statistical analysis, simulation, and reproducible research, appealing to students in non-computing disciplines.