Data Institute Certificates

Gain practical, career-ready expertise through certificate courses at the University of San Francisco’s Data Institute. Learn from USF faculty and industry experts from leading tech companies. Programs are designed for professionals and students who want to build or deepen their data and AI skills.

Expand your knowledge. Accelerate your career. Learn more at the USF Data Institute.

Fall 2026 Certificate Programs in Data Science & AI

• Python for Data Analysis (Sep 2 - Oct 21): Learn to write and test Python code to solve real-world data problems. Practice working with data structures and libraries such as NumPy, Pandas, and Matplotlib to prepare, analyze, and visualize data effectively.
• Mathematical Foundations for Data Science (Sep 15 - Oct 27): Develop fluency in the core mathematical concepts behind data science and machine learning. Explore calculus, linear algebra, and optimization techniques that form the analytical base for advanced AI and technical studies.
• SQL for Data Analysis (Oct 6 - Nov 17): Gain practical experience extracting, transforming, and managing data using SQL. Learn to write, debug, and optimize queries while understanding key principles of relational databases and data design.
• Introduction to Probability & Statistics (Oct 28 - Nov 17): Build a strong foundation in statistical reasoning for data and AI applications. Learn how to analyze variability, make predictions, and interpret real-world data to support evidence-based decision-making.
• Data Science for Marketing (Sep 10 - Oct 22): Use statistical and machine learning techniques to generate insights that drive marketing strategy. Apply Python to real datasets and master tools like correlation analysis, regression, segmentation, and forecasting.
• Applied Agentic AI (Online & In-Person): Learn how AI agents think, communicate, and make decisions as you design and build your own simple agents using beginner-friendly tools. Through hands-on labs, industry workshops, and a final Hackathon, you will practice constructing and evaluating agentic systems and earn a certificate upon completion.
• Advanced Python for Data Analysis (Oct 28 - Dec 16): Learn to use Python’s core data science libraries including Pandas, NumPy, JupyterLab, and Matplotlib to efficiently clean, analyze, and visualize data for real-world insights and decision-making.
• AI & Data Ethics (Dates TBA): Examine pressing issues in AI and data ethics, including bias, privacy, surveillance, and the societal impacts of automated decision-making. Learn frameworks for building fair and responsible data systems.
• Machine Learning Operations (Dates TBA): Learn to build, deploy, and manage machine learning systems that bridge the gap between model development and real-world production, optimizing AI workflows and advancing your ML career.

Additional Certificate Programs

• Secure LLM Implementation (Dates TBD): Build the skills leaders need to deploy large language models safely, responsibly, and at scale. Learn how to assess enterprise risks such as data leakage, model poisoning, and compliance gaps, and apply governance frameworks that strengthen security across the AI lifecycle. Gain practical tools for evaluating vendors, guiding technical teams, and implementing secure, resilient LLM systems within your organization.
• Applied Graph Neural Networks: Gain hands-on experience with Graph Neural Networks and master the skills needed to apply this powerful machine learning technique to real-world, cutting-edge applications.
• Chief AI Officer: Develop the strategic, technical, and organizational leadership required to guide enterprise-wide AI transformation. Learn how to define an AI vision, build governance and operating models, align teams and resources, and translate emerging capabilities into measurable business impact. Gain practical frameworks to oversee responsible adoption, manage risk, and lead cross-functional initiatives that position your organization for long-term success in an AI-driven economy.
• Digital Marketing Analytics: Master the skills to analyze marketing data, apply AI-driven insights, and implement data-informed strategies that optimize performance and fuel business growth.
• Fundamentals of Deep Learning: Get hands-on experience building deep learning models with PyTorch. Learn how neural networks power applications in computer vision, natural language processing, and recommendation systems.
• Generative AI for Coders: Learn to build and apply generative AI models through hands-on coding projects that enhance software development, enable creative innovation, and solve complex real-world problems.
• Introduction to Machine Learning: Understand the core principles that guide modern machine learning. Explore techniques such as logistic regression, decision trees, and clustering, while learning how to validate and evaluate model performance.
• Large Language Models for Coders: Explore how generative AI systems like GPT work under the hood. Learn practical approaches to fine-tuning and deploying large language models for coding and automation tasks.
• Practical Deep Reinforcement Learning: Gain hands-on experience applying deep reinforcement learning with PyTorch to real-world challenges in robotics, healthcare, supply chain optimization, and beyond, building the skills to design and deploy advanced AI systems.