Program Overview

Data Science and Machine Learning for Biotechnology Professionals

Online Certificate Program

The Data Science and Machine Learning for Biotechnology Professionals certificate program at San Francisco State University is designed to advance the careers of biotechnology professionals. This program, developed in partnership with Genentech scientists, provides practical skills in programming, data science, and machine learning, with a focus on real applications in biotech and pharmaceuticals. Participants will learn to analyze complex data, apply machine learning methods, and communicate results across multidisciplinary teams. All courses are online and structured to support working professionals.

About the Program

Audience

This program is designed for biotechnology professionals identified and sponsored by Genentech. Participants are selected by Genentech and invited to enroll at San Francisco State University.

What You Will Learn

By completing this program, you will be able to:

• Use the fundamentals of computer programming, data science, and machine learning for biotechnology, including theory, tools, and techniques.
• Apply programming and data science concepts to address complex challenges in biotechnology and create real-world solutions.
• Communicate scientific information clearly in writing and speech to audiences from different areas of the biotechnology field.

Cost and Length

• Cost: $1,500 per course, $7,500 total program.
• Length: 11 months.

Genentech Partnership

This program is offered in partnership with Genentech.

Course Descriptions

CSC 306: An Interdisciplinary Approach to Computer Programming

This course offers an interdisciplinary approach to computer programming by exploring programming concepts and techniques in the context of biology, chemistry, and biochemistry. The course aims to teach students how to use programming as a tool to solve complex problems in various domains, to analyze and visualize data, and to automate repetitive tasks. Students will learn fundamental programming concepts such as variables, loops, conditionals, and functions using Python. No previous coding experience is needed.

Course Learning Outcomes

• Define and implement the basic building blocks of programming with Python (variables, control statements, loops, functions, and more).
• Recognize and execute the essential scientific Python programming tools like Google Colab, NumPy, and Pandas.
• Interpret computational problems and solve them in Python.
• Distinguish how scientists use programming to work with large datasets and to simulate scientific phenomena.

CSC 311: Data Structures for Data Science Application Development

This course focuses on two key areas of data science: data structures and data visualization. Students will learn how to organize and manipulate data using various data structures such as arrays, lists, trees, and graphs. They will also learn about the importance of data visualization in data analysis and exploration and learn how to use popular visualization tools such as Matplotlib and Seaborn.

Prerequisites: CSC 306 Course Learning Outcomes

• Create and modify data structures for data ingestion and visualization.
• Develop fast and efficient ways for data acquisition and processing algorithms.
• Execute data science applications through use cases relevant to daily problems.

CSC 408: Machine Learning and Data Science for Personalized Medicine

This course provides an introduction to machine learning and data science, with a focus on their applications in personalized medicine. Students will learn the basic concepts and computational techniques of machine learning and data science, including linear and logistic regression, classification, decision trees, random forests, and gradient-boosted trees.

Prerequisites: CSC 311 Course Learning Outcomes

• Implement, compare, and contrast common supervised Machine Learning Models and describe and interpret their results.
• Recognize and describe common types of genetic units and apply machine learning and data science to genomic data.
• Apply and explain data science and machine learning-related code in Python.
• Use standard methods for communication of data sets, machine learning models, results, interpretation, and caveats.
• Recognize and describe possible ethical issues around machine learning and genetics in medicine.

CSC 411: Introduction to Machine Learning for Interdisciplinary Data Scientists

This course covers intermediate machine learning concepts and tools, focusing on application development, linear models, deep neural networks, and transfer learning using Python, TensorFlow, and Keras.

Prerequisites: CSC 220 or CSC 311 or equivalent; a college-level biology course; or permission of the instructor. Course Learning Outcomes

• Identify and frame real-life problems with substantial scope and complexity as an applied machine learning project.
• Utilize open-source toolkits, packages, and libraries for machine learning algorithms, data visualization, and general programming for a project.
• Measure, compare, and analyze the performance of the implemented machine learning solutions.

CSC 509: Data Science and Machine Learning for Medical Image Analysis

This course is designed for students who have a solid understanding of machine learning concepts and techniques and are interested in their applications to medical image analysis. The course explores the application of state-of-the-art deep learning models to biomedical image analysis.

Prerequisites: CSC 9008 Course Learning Outcomes

• Learn the fundamentals of biomedical imaging with a focus on different imaging technologies and their applications in clinical practice and clinical research.
• Understand the basics of image processing such as slices and 3D volumes, regions of interest, overlays, and masks, segmentation basics.
• Understand why deep learning/CNNs are specifically useful for medical imaging problems and what kinds of problems are currently being addressed using deep learning for medical imaging.

Faculty

• Noelle Anderson: Instructor, CSC 408: Machine Learning and Data Science for Personalized Medicine.
• Yuting Gao: Instructor, CSC 408: Machine Learning and Data Science for Personalized Medicine.
• Anagha Kulkarni, Ph.D.: Professor and Associate Chair of Computer Science, CSC 411: Introduction to Machine Learning for Interdisciplinary Data Scientists.
• Jennifer Nelson: Instructor, CSC 306: An Interdisciplinary Approach to Computer Programming.
• Vanessa Wei: Instructor, CSC 311: Data Structures for Data Science Application Development.
• Ilmi Yoon: Professor of Computer Science, CSC 509: Data Science and Machine Learning for Medical Image Analysis.