Data Science (DATASCI)

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

Instructor(s): Gilmer ValdesGilmer Valdes also teaches: DATASCI 225, DATASCI 221

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.

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 221  Data Science Program Seminar II  (1 Units)  Fall, Winter, Spring  

Instructor(s): Gilmer ValdesGilmer Valdes also teaches: DATASCI 225, DATASCI 220

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

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 ValdesGilmer Valdes also teaches: DATASCI 220, DATASCI 221

Prerequisite(s): Biostats 213 or equivalent. Biostat 216 and Biostat 208 or equivalent

Restrictions: This course is part of the Training in Clinical Research (TICR) Program and may have space limitations. Auditing is not permited.

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.

View full course details:

  • 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? Yes
  • 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

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