Data Science (DATASCI)
DATASCI 220 Data Science Program Seminar I (1 Units) Fall, Winter, Spring
Instructor(s): Gilmer Valdes
Instructor(s): Gilmer Valdes
Prerequisite(s): BIOSTAT 202 and BIOSTAT 213
Restrictions: This course is restricted to students enrolled in the Certificate in Health Data Science and the Master's degree in Health Data Science (first year students).
Activities: Seminar, Independent Study
This seminar series covers topics in data science algorithms, ethics, biases, and applications. Students will be exposed to current topics on Data Science and Machine Learning/Biostatistics and Health Data applications, discuss issues in data science, present their work, and learn how to critically evaluate research literature. External speakers will be invited to give presentations on potential careers in health data science across the biotech industry, government and academia.
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- School: Graduate Division
- Department: Health Data Science Program
- May the student choose the instructor for this course? No
- Does enrollment in this course require instructor approval? No
- Course Grading Convention: P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
- Graduate Division course: Yes
- Is this a web-based online course? No
- Is this an Interprofessional Education (IPE) course? No
- May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
- Repeat course for credit? Yes
DATASCI 221 Data Science Program Seminar II (1 Units) Fall, Winter, Spring
Instructor(s): Gilmer Valdes
Instructor(s): Gilmer Valdes
Prerequisite(s): DATASCI 220
Restrictions: This course is restricted to students enrolled in year 2 of the Master's in Health Data Science program.
Activities: Seminar, Independent Study
This course covers advanced topics of data science methods, ethics and biases. The focus in this second year of the seminar program will be on students presenting their research work progress from their Capstone projects. Additionally, students will also learn how to critically evaluate research literature.
View full course details:
- School: Graduate Division
- Department: Health Data Science Program
- May the student choose the instructor for this course? No
- Does enrollment in this course require instructor approval? No
- Course Grading Convention: P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
- Graduate Division course: Yes
- Is this a web-based online course? No
- Is this an Interprofessional Education (IPE) course? No
- May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
- Repeat course for credit? Yes
DATASCI 222 Data Science Capstone Project (1 Units) Fall, Winter, Spring
Instructor(s): John Kornak
Instructor(s): John Kornak
Prerequisite(s): BIOSTAT 202, BIOSTAT 213, BIOSTAT 214, BIOSTAT 216, DATASCI 220, DATASCI 225
Restrictions: This course is restricted to 2nd year students in the Master's in Health Data Science program.
Activities: Project
Capstone project requirement for students in the Master’s in Health Data Science program. Students will write a first author paper researching a problem in health data science and analyzing data using appropriate data science methodology; present their work at a scientific conference; generate a portfolio of code, analyses and data products; and write a detailed report on the background methodology and technical issues that were considered as well as implemented for the submitted publication.
View full course details:
- School: Graduate Division
- Department: Health Data Science Program
- May the student choose the instructor for this course? No
- Does enrollment in this course require instructor approval? No
- Course Grading Convention: P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
- Graduate Division course: Yes
- Is this a web-based online course? No
- Is this an Interprofessional Education (IPE) course? No
- May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
- Repeat course for credit? Yes
DATASCI 225 Advanced Machine Learning for the Biomedical Sciences II (3 Units) Spring
Instructor(s): Gilmer Valdes
Instructor(s): Gilmer Valdes
Prerequisite(s): BIOSTAT 213, BIOSTAT 216 and BIOSTAT 208. Exceptions to these prerequisites may be made with the consent of the Course Director, space permitting.
Restrictions: This course is part of the Health Data Science Masters and Certificate Program and may have space limitations. Auditing is not permitted.
Activities: Lecture, Project
This course covers the underlying formulation of machine learning algorithms. Its focus is on providing deep understanding of machine learning methodology. This is an advanced course in machine learning and its objective is to provide students with a strong foundation so that they can properly manipulate and customize black box machine learning library packages. Students will implement popular machine learning algorithms and customize them to best satisfy specific needs in medicine.
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- School: Graduate Division
- Department: Clinical Research Program
- May the student choose the instructor for this course? No
- Does enrollment in this course require instructor approval? No
- Course Grading Convention: Letter Grade, P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
- Graduate Division course: Yes
- Is this a web-based online course? No
- Is this an Interprofessional Education (IPE) course? No
- May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
- Repeat course for credit? No
DATASCI 300 Data Science Educational Practice (1 Units) Fall, Winter, Spring
Instructor(s): Staff
Instructor(s): Staff
Prerequisite(s): Students must have previously taken the course they TA for.
Restrictions: This course is restricted to 2nd year students in the Master's in Health Data Science program.
Activities: Independent Study, Lab science
Master’s in Health Data Science students are expected to act as a teaching assistant (TA). This experience involves leading a weekly small-group discussion section of 10-15 students, holding office hours and grading homework assignments and projects. This requirement will provide students with valuable teaching experience without having a significant time impact on their Capstone project work. In all cases, students will have taken the course they are asked to TA during their first year.
View full course details:
- School: Graduate Division
- Department: Health Data Science Program
- May the student choose the instructor for this course? Yes
- Does enrollment in this course require instructor approval? No
- Course Grading Convention: P/NP (Pass/Not Pass) or S/U (Satisfactory/Unsatisfactory)
- Graduate Division course: Yes
- Is this a web-based online course? No
- Is this an Interprofessional Education (IPE) course? No
- May students in the Graduate Division (i.e. pursuing Master or PhD) enroll in this course? Yes
- Repeat course for credit? No