Program Structure:
– Duration: 3 Months (12 weeks)
– Total Hours: 72 hours (3 hours/day x 2 days/week x 12 weeks)
– Days: Fridays and Saturdays
– Credit Units: 12 Credit Units (1 credit unit = 6 hours of instruction)
The Certificate in Health Data Science provides a focused and intensive exploration of how data science techniques can be applied to health and biomedical data. Over the course of 3 months, participants will gain a strong foundation in Python programming and explore the specialized tools and methods required for managing, analyzing, and interpreting health-related data. This course covers key areas such as data visualization, statistical analysis, and machine learning, all within the context of health data applications. With hands-on exercises and real-world case studies, students will gain practical experience in working with electronic health records (EHRs), genomic data, patient health data, and medical imaging datasets. By the end of the course, participants will have the skills necessary to drive data-driven decision-making and improve healthcare outcomes through data science.
What You Will Learn:
By the end of this course, participants will be able to:
Understand the Fundamentals of Python for Health Data Science:
- Master the basics of Python programming, including variables, loops, functions, and data structures, tailored for health data applications.
- Work with essential Python libraries such as Pandas and NumPy to process and analyze health-related datasets.
Explore Health Data and Data Wrangling:
- Collect, clean, and preprocess health datasets (e.g., electronic health records, patient data).
- Handle missing values, outliers, and inconsistencies within health datasets to ensure data quality.
- Gain proficiency in data wrangling and manipulation techniques specifically for health data.
Conduct Statistical Analysis in Health Data:
- Apply descriptive and inferential statistics to analyze health data trends.
- Perform hypothesis testing, regression analysis, and probability calculations on health data.
- Understand and utilize key statistical concepts relevant to the healthcare sector
Visualize Health Data for Insights:
- Create compelling visualizations using libraries such as Matplotlib and Seaborn to convey health data insights.
- Build visual reports and dashboards that allow for effective communication of healthcare trends and findings.
- Explore visual tools to analyze patient data, population health trends, and other critical healthcare metrics.
Master Machine Learning for Health Applications:
- Build and evaluate machine learning models using health data for predictions and classifications (e.g., disease prediction, patient segmentation).
- Apply supervised and unsupervised learning techniques to solve real-world health data challenges.
- Implement feature selection and model tuning techniques to enhance machine learning models on health data.
Work with Advanced Health Data Applications:
- Gain practical experience with case studies and projects in health data science, such as analyzing EHRs, medical imaging, and genomic data.
- Understand ethical concerns and privacy regulations (e.g., HIPAA) when working with sensitive health data.
- Explore the role of AI and machine learning in advancing personalized medicine, disease diagnosis, and healthcare delivery.
Capstone Project in Health Data Science:
- Complete a capstone project where you apply the skills learned throughout the course to a comprehensive health data science problem.
- Develop a full data science solution, from data preprocessing and analysis to modeling and reporting, in the healthcare domain.
- Present findings in a professional and actionable format, showcasing the potential impact on health outcomes.
