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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 Fall 2022

  • 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.
  • 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.
  •  Pandas and Numpy for Data AnalysisMaster the foundations of Pandas and NumPy for Python and learn to use these packages to import, organize, and clean data from files, websites, and databases. Also learn to manage missing data and create data visualizations. Participants should have basic experience with either Python or SQL.

Additional Certificate Courses

  • Deep Learning Part I: Learn the latest deep learning techniques in a practical, "top-down" way, including how to create state-of-the-art models in computer vision, natural language processing (NLP), recommendation systems, and tabular and time series data analysis, as well as how to use the brand new fastai v2 library along with PyTorch (the most popular software amongst top deep learning researchers).
  • Deep Learning Part II: Tackle more complex problems that require integrating multiple deep learning techniques. You will also combine classic machine learning techniques with deep learning, all within the context of solving end-to-end real-world modeling problems.
  • 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.
  • 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.
  • Intro to 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.