Data Science (DATASCI)

DATASCI 220  Data Science Program Seminar I  (1 Units)  Fall, Winter, Spring  

Instructor(s): John KornakJohn Kornak also teaches: DATASCI 222, DATASCI 221, DATASCI 223

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): John KornakJohn Kornak also teaches: DATASCI 222, DATASCI 220, DATASCI 223

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.

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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 222  Data Science Capstone Project  (8 Units)  Fall, Winter, Spring  

Instructor(s): John KornakJohn Kornak also teaches: DATASCI 220, DATASCI 221, DATASCI 223

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 Masters 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.

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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 223  Applied Data Science with Python  (2 Units)  Winter  

Instructor(s): John KornakJohn Kornak also teaches: DATASCI 222, DATASCI 220, DATASCI 221

Prerequisite(s): 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.

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, Workshop

Survey of Data Science methods in Python, starting with common data science tools and processes and spending one week per topics learning to build common ML/AI solutions.

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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: 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 224  Understanding Machine Learning: From Theory to Applications  (3 Units)  Spring  

Instructor(s): Jean Feng

Prerequisite(s): BIOSTAT 216

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 teaches the mathematical foundations of machine learning (ML). Each week, the course surveys a different algorithm to examine its underlying machinery, covering topics such as linear algebra, calculus, and optimization. ML algorithms range from linear models to gradient boosting and deep learning. The course also discusses newer concepts such as model fairness and ML for causal inference. Upon course completion, students should be able to learn new ML algorithms independently.

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: 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 225  Advanced Machine Learning for the Biomedical Sciences II  (3 Units)  Spring  

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  (2 Units)  Fall, Winter, Spring, Summer  

Instructor(s): Staff

Prerequisite(s): Students must have previously taken the course they EA for.

Restrictions: This course is restricted to 2nd year students in the Master's in Health Data Science program.

Activities: Lab science, Discussion

Masters in Health Data Science students are expected to act as an educational apprentice (EA). 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 EA 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