Python Essentials for Data Sci

Course Description

Data 311: Python Essentials for Data Science This course will teach students the Python programming language for data science and analysis, using an integrated approach that combines native Python functionality with easy-to-use Python packages. Python is a popular programming language for data science that combines ease-of-use, power, and efficiency. The first part of the course will teach the basics of Python, and students will gain a fundamental understanding of native programming concepts and techniques, including expressions; data types such as integers, floating-point numbers, and strings; control flow; calling and defining functions; data structures such as lists, tuples, and dictionaries; lambdas; generators; and regular expressions for pattern matching and data cleaning. Students will then learn how to use the numpy and pandas package for tabular numerical and data science programming, matplotlib for visualization, and folium for geospatial visualization. These packages extend the power and performance of native Python, allowing students to readily perform sophisticated data processing and analysis. At the same time, the foundational skills learned in the first part of the course will allow students to move beyond canned packages, when appropriate, and implement bespoke algorithms, processing, and analysis. If time allows, students will also learn how to use BeautifulSoup for web scraping, and packages for textual analysis and mining. The course will be taught in the JupyterLab interactive notebook environment. Throughout the course, students will learn to apply what they have learned via examples and exercises from the natural sciences, social sciences, and medicine, including data on topics such as social inequality and discrimination. Some prior programming experience in any language is recommended. Undergraduate

Code

DATA 311

Credits

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Subject

DATA