Overview
Biomedical Data Science has become an integral part of biomedical research. As a result, biomedical scientists with data science knowledge are advantaged on multiple fronts. This one-year Biomedical Data Science (BMDS) Certificate Program is designed to provide students with the basic data science skill set in the context of biomedical research data. At the completion of this certificate the students will be able to:
- Communicate constructively with data scientists
- Can analyze their own data set
- Can explore the large datasets available in the public domain, therefore missing an important opportunity to mine big data resources.
Therefore, training researcher the basics of data science is crucial to advance scientific discovery.
Admissions Requirements
To apply for admission applicants must submit the following:
- Online Graduate School application
- Personal Statement: A one-page personal statement describing the applicant’s career goals and purpose for studying palliative care.
- Resume: The applicant’s current resume or curriculum vitae, including professional work/practice since graduating with a bachelor’s degree (or equivalent).
- Application Fee: A nonrefundable application fee of $50.00 (U.S. dollars). Checks or money orders should be made out to the University of Colorado.
- Transcripts: Unofficial transcripts from all post-secondary colleges and/or universities should be sent directly to:
- Electronic Transcripts should be sent to: graduate.school@cuanschutz.edu (preferred)
- If sending a physical transcript, please mail to:
University of Colorado Anschutz Medical Campus
Graduate School
Mail Stop C296
Fitzsimons Building, W5107
13001 E. 17th Place
Aurora, CO 80045
International students must meet ALL of the requirements above and those required by International Admissions.
Certificate Requirements
| Year 1 | ||
|---|---|---|
| Fall | Hours | |
| BSBT 6110 | Introduction to Biocomputing | 3 |
Must be taken together: | ||
| BSBT 6112 | Introduction to Biocomputing | 2 |
| BSBT 6113 | Data Science with R | 1 |
| Hours | 6 | |
| Spring | ||
| BIOS 6642 | Introduction to Python Programming | 3 |
| BIOL 6764 | Biological Data Analysis | 4 |
| MOLB 7900 | Practical Computational Biology for Biologists: Python | 2 |
| BIOS 6310 | Practical Clinical Research Informatics | 3 |
| BIOE 5420 | Special Topics in Bioengineering | 1-6 |
| Hours | 13-18 | |
| Summer | ||
| BSBT 6939 | Internship - Technology and Innovation | 3-6 |
| Hours | 3-6 | |
| Total Hours | 22-30 | |
Learning Objectives
- Learn the basics of computer programming.
- Locate, access, analyze and visualize biomedical data set using appropriate tools and programs.
- Understand and apply various machine learning techniques and data analytics for solving real world biological problems.
- Communicate effectively with biomedical researchers and computational data analysts in a team science environment.
Courses
Special topics of particular interest to graduate students in Bioengineering. Prereq: Graduate standing within the Department of Bioengineering or permission of instructor. Repeatable. Max hours: 12 Credits.
Grading Basis: Letter Grade
Repeatable. Max Credits: 12.
Addresses quantitative aspects of research design, data collection and analysis in the biological sciences. Emphasizes relationships among probability theory, estimation, testing, inference, and interpretation. Includes intensive computer lab using the statistical programming software R to demonstrate both traditional analytical and contemporary simulation based (permutation, bootstrap, and Bayesian) approaches for inference in biology. Restriction: Restricted to degree-granting graduate programs. Max hours: 4 Credits.
Grading Basis: Letter Grade
Restriction: Restricted to degree-granting graduate programs
This course provides students with hands on experience in clinical research informatics involving secondary use of electronic health record (EHR) data, clinical informatics databases, and basic clinical data science as preparation for more advanced informatics or data science coursework.
Grading Basis: Letter Grade
A-PUBH1 Graduate students and public health certificate students only.
Typically Offered: Spring.
Applied biostatistical methods including descriptive and statistical inference; odds ratio and relative risk, probability theory, parameter estimation, tests for comparing statistics of two or more groups, correlation and linear regression and overviews of: multiple and logistic regression and survival analysis.
Grading Basis: Letter Grade
A-PUBH1 Graduate students and public health certificate students only.
Typically Offered: Fall, Spring, Summer.
This first course in programming using Python covers basic concepts such as variables, data types, iteration, flow of control, input/output, and functions and advanced concepts such as object oriented programming. Statistics related examples, homework and projects may be used.
Grading Basis: Letter Grade
A-PUBH1 Graduate students and public health certificate students only.
Typically Offered: Spring.
This course provides students with hands on experience in basic computation, database, and programming skills set as a pre-requisite for a higher level data analysis course. The students will use example in the context of biomedical and genomic dataset. Requisite: Must be simultaneously enrolled in BSBT 6113.
Grading Basis: Letter Grade
Typically Offered: Fall.
In this 4 weeks semi-independent study course, you will learn how to use the “tidyverse” programming paradigm to perform data science operation using the programming language R. At the end of the course, you will learn the basic understanding of the fundamental elements of data science, including; wrangling, exploration, visualization and modeling.
Grading Basis: Letter Grade
Typically Offered: Fall.
The internship provides hands-on learning opportunities for graduate students in institutions related to technology/biotechnology, computer science, engineering, innovation and entrepreneurship. Requisite: (Formerly IDPT 6939) Enrollment with permission only, contact inge.wefes@ucdenver.edu. Instructor Consent required.
Grading Basis: Letter Grade with IP
Repeatable. Max Credits: 6.
A-GRAD Restricted to graduate students only.
Additional Information: Report as Full Time.
Typically Offered: Fall, Spring, Summer.
Comp. biology class aimed at biology PhD students. Topics covered include: basic practices for coding in python; analysis of standard high-throughput genomic data to study the regulation of gene expression; intro to modeling gene expression; data visualization; communicating computational analysis/results. 3 wks. lecture, lab & recitation
Grading Basis: Letter Grade
Typically Offered: Spring.
Comp. biology class aimed at biology PhD students. Topics covered include: basic practices for coding in R; analysis of standard high-throughput genomic data to study the regulation of gene expression; intro to modeling gene expression; data visualization; communicating computational analysis/results. 3 wks. lecture, lab & recitation
Grading Basis: Letter Grade
Typically Offered: Spring.