Applied Data Science with Python (DATASCI 223)

At the conclusion of this course, students will be able to:

• Develop Proficiency in Python Programming and Data Science Tools: Equip students with the skills to proficiently use essential data science technologies and processes common in industry; including Python, Git, SQL, Pandas, and data visualization libraries, providing a strong foundation for conducting data analysis in real-world scenarios.
• Apply Machine Learning to Solve Real-World Problems: Enable students to apply machine learning techniques including data cleaning, classification and time series analysis, to real-world data science challenges, emphasizing their practical application in fields like health data science.
• Implement Advanced Data Science Concepts: Empower students to implement advanced data science solutions, including generative AI and Large Language Model (LLM) development, with a focus on their relevance and applications in healthcare and health data analysis.
• Develop Effective Data Communication Skills: Enhance students' ability to communicate data insights effectively through data visualization and storytelling, a crucial skill for conveying findings in health data science contexts.

Familiarity with programming concepts, including loops, variables, and functions. Ideally, hands-on experience writing and running scripts such as in: Python, R, Bash, or other programming languages. 

This course is part of the Health Data Science Master's and Certificate Programs and may have space limitations.  Auditing is not permitted.

Exceptions to the prerequisites may be made with the consent of the Course Director, space permitting.

This course will have lectures and hands-on exercises.

Each week will dive into a different focus area. The lecture will provide an overview of new concepts and tools introduced that week, closing with a hands-on exercise for students to apply those concepts while writing their own code. Labs will not introduce new material; instead, they provide a forum for collaboration between students and staff to help each other with the current material.

Students are encouraged to collaborate in small groups but may also work independently. The class culminates with a larger, multi-week project applying advanced techniques or combining applications from multiple focus areas.

All course materials and handouts will be posted on the course's online syllabus.

Syllabus & lecture notes/slides

Reference books (including freely available options)

Tools:

• Markdown
• Python
• Git + GitHub
• Jupyter notebooks or Google Colab
• Visual Studio Code

Final grades will be based on the class participation (40%) and submitted exercises (60%).

Only UCSF students (defined as individuals enrolled in UCSF degree or certificate programs) will receive academic credit for courses. Official transcripts are available to UCSF students only. A Certificate of Course Completion will be available upon request to individuals who are not UCSF students and satisfactorily pass all course requirements.

UCSF Graduate Division Policy on Disabilities

For UC-Affiliated learners, the course fee is $2,100

For Non-UC-Affiliated learners, the course fee is $2,500

UC-Affiliation: Concurrent enrollment in a University of California-sponsored residency or post-doctoral fellowship program that is recognized by the Office of Graduate Medical Education; or a registered student in one of the professional schools or graduate programs at the University of California (in a program other than the TICR program); or individuals who hold full-time salaried University of California faculty, academic or staff positions. Please note: Individuals will be asked to provide proof of UC status.