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Data Institute Certificates

Certificate courses at the University of San Francisco's Data Institute provide a learning experience for those seeking to increase their breadth and depth of knowledge of data science tools and techniques.

Courses are taught by MS in Data Science faculty and expert practitioners from leading tech firms. We offer courses for analysts, managers, executives, and engineers looking to augment their skills, as well as introductory-level courses for students without a background in data science. To accommodate working adults, all certificate courses are scheduled in the evening.

Offered in Spring 2025

  • Introduction to Probability & Statistics: Build the foundation for success in data-driven fields with our Introduction to Probability & Statistics certificate course. Designed to meet the needs of aspiring graduate students and professionals alike, this comprehensive program offers a seamless pathway to mastering the statistical concepts essential for modern careers and advanced education.
  • Machine Learning Operations: Whether you're a professional seeking to expand your expertise or a student with foundational knowledge in machine learning, this course equips you with the tools and techniques to excel in the rapidly evolving field of MLOps. Learn to streamline workflows, ensure model reliability, and bring cutting-edge machine learning solutions to life.
  • Python for Data Analysis: Learn to write Python code to solve data-related problems, create and manipulate simple data structures, develop and test code, read and use programming language documentation, define Python functions and modules, and work with NumPy, pandas and Matplotlib.
  • SQL For Data Analysis: Learn to use SQL to extract and transform data from a variety of sources across different applications and industries. After completing this certificate, you will be able to write, debug and optimize SQL queries as well as understand foundational database design concepts.

Additional Certificate Courses

  • AI & Data Ethics: Explore urgent issues of data ethics, including bias and fairness, privacy and surveillance, and disinformation and manipulation, as well as foundations of ethics and connections with broader social trends and systems.
  • Applied Machine Learning: Solve practical machine learning problems using a hands-on approach in application areas such as e-commerce, business intelligence, and bioinformatics. You'll also learn to clean data, apply machine learning techniques to solve practical problems, and analyze data in supervised scenarios with an end-to-end approach.
  • Data Science for Marketing: In this course, participants will focus on generating and interpreting statistical and machine learning outputs for effective decision-making. Students will work with the Python open-source coding language to execute common data science procedures used in marketing, including correlation analysis, regression, analysis of variance, segmentation, forecasting, and conjoint analysis. Basic familiarity with Python is required for this course.
  • Fundamentals of Deep Learning: Learn the practical details of deep learning applications with hands-on model building using Pytorch. You will work on problems ranging from computer vision, natural language processing, and recommendation systems.
  • GeoPandas for Data Analysis: This course, taught by an industry expert, provides an in-depth exploration of GeoPandas, an open-source project that simplifies working with geospatial data in Python. Gain knowledge of Pandas, Python, basic plotting, and GIS fundamentals.
  • Generative AI for Coders: This course explores the process of constructing generative AI models, you'll gain the skills and knowledge necessary to leverage these models and frameworks.
  • Introduction to Machine Learning: Learn foundational issues in machine learning, such as cross-validation and the bias-variance trade-off, which are covered with a focus on the intuition behind their use. You'll also explore principal techniques including logistic regression, decision trees, classification, and clustering.
  • Large Language Models for Coders: This course is crafted to equip participants with deep, practical insights derived from real-world applications of generative AI.
  • Advanced Python for Data Analysis: This advanced course is designed for professionals and aspiring analysts who already have a foundational understanding of Python and want to deepen their expertise in tackling complex data challenges. You'll learn to work with large datasets, implement advanced analytical techniques, and leverage cutting-edge libraries to extract insights and drive data-driven decisions.