This program provides a hands-on, intuition-focused introduction to data science methods, culminating in a student-chosen final project, and is taught at a world-class university.
The Stanford Summer Session - Principles of Data Science (DATASCI112) offers a hands-on introduction to the methods of data science. Students will learn strategies for analyzing and visualizing various types of data, including tabular, textual, hierarchical, and geospatial data. The course covers data acquisition through web scraping and REST APIs, and delves into core principles of machine learning such as supervised vs. unsupervised learning, training vs. test error, hyperparameter tuning, and ensemble methods. The emphasis is on intuition and practical implementation using Python and Jupyter notebooks with libraries like pandas and scikit-learn. The program culminates in a final project where students apply their acquired skills to a data science problem of their choice, preparing them for further study in statistics, machine learning, and artificial intelligence, or for roles in data analysis and business intelligence.
On Mondays, Wednesdays, and Fridays, students attend lectures introducing data science concepts and coding demos. On Tuesdays and Thursdays, students meet with a TA in small sections to present and discuss solutions to exercises.
This course is ideal for undergraduates, particularly freshmen and sophomores, who are considering a Data Science major, want to understand real-world applications of data science, or need practical data science skills for an internship.
Stanford University
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