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BMI 5007 Methods in Health Data Science

3 semester credit hours
Lecture contact hours: 2; Lab contact hours: 3
Web-based and classroom instruction
Prerequisite: Prerequisite quiz and Consent of instructor
Lab Fee: $30

Course Description:
The course introduces methods in health data science – defining the problem, accessing, and loading the data, formatting into data structures required for analysis. This course covers the basics of computational thinking to define a computational solution, methods to access healthcare data from variety of sources (EHR data, UMLS, Medline, etc.), and in different data formats. The students will apply methods for data wrangling and data quality assessments to structure the data for analysis. The students will be introduced to basics of design and evaluation of algorithms and application of data structures for healthcare data. The course will use Python programming language and basic python libraries for data sciences such as numpy, scipy, matplotlib and pandas.

Students should expect a good amount of programming exercises for each week. This course is not an introduction to programming, and not a course to improve programming skills. Students are expected to have some experience with introductory / beginner level Python programming.

Upon successful completion of the course, students will:

  • Abstract a business need for data analysis and define appropriate computational problem
  • Design and analysis (time complexity) of simple algorithms
  • List basic data structures and their characteristics, applications in biomedicine
  • Retrieve biomedical data from multiple sources formats – specifically flat files (text), tabular data (CSV), structured data (JSON, XML)
  • Implement Python programs to load data and apply basic data wrangling to structure output.


Pre-Requisite effective Fall 2020

Students must exhibit competence in basic python programming. Students should be able to write a python scripts (.py file) and execute the file from command line.

"Basic" python programming is defined as ability to working with

  1. Variables - define, access
  2. Data Types and conversion – int, str, float, bool
  3. Use of appropriate operators – assignment, comparison, logical, arithmetic, identity and containment operators.
  4. Control flows and loops (if..else, while, for, break, continue)
  5. Lists, Dictionaries - creation, access, add or remove items
  6. File – input and output operations – open, close, read, write.
  7. Errors – try, except, troubleshoot errors.

To register for the course – complete all the steps:

  1. Register for the pre-requisite course here: https://uth.instructure.com/enroll/J8YKGE (Requires UTHealth Login)

  2. You must complete pre-requisite courses in LinkedIn Learning – 4 separate courses with completion certificates. A total of 10 hr of video tutorials + exercises. The instructions for the LinkedIn Learning are available in the pre-requisite course.

  3. You must pass a coding exam in Canvas (score 4 out of 5 points) –This will be a video proctored live coding solving Python exercises. Total time of 90 minutes. The instructions for the Coding exam are available in the pre-requisite course.

After you complete the exam, you will receive your approval code in 2 business day.

The instructions for the LinkedIn Learning, and Coding exam are available in the pre-requisite course.

Important Instructions:

  1. Plan to complete about 10 hours of video tutorial and additional exercises in LinkedIn Leaning.
  2. The grading of quiz will take 2 working days. Plan to give the quiz and obtain the approval before the registration deadline.
  3. A score of 4 and above is required to be approved for the course.