Program Structure: – Duration: 6 months – Class Days: Fridays and Saturdays – Class Hours: 3 hours per day (6 hours per week) – Total Contact Hours: 144 hours – Credit Units: 18 (assuming 1 credit unit = 8 contact hours)
Course Description:
The Diploma in Health Data Science is a comprehensive 6-month program designed to equip participants with advanced skills in data analysis, machine learning, and artificial intelligence with a focus on healthcare applications. This program provides an immersive experience, beginning with foundational topics in Python programming, and progressing towards advanced health data science concepts such as precision medicine, bioinformatics, and big data analytics in healthcare. Throughout the course, participants will gain hands-on experience with real-world healthcare datasets, learning to navigate the challenges of data privacy, ethics, and interoperability. Students will develop proficiency in data wrangling, statistical analysis, data visualization, and machine learning models, applying these skills to solve critical problems in healthcare, such as disease prediction, patient segmentation, and medical image classification. The program concludes with a capstone project where participants will address a real-world healthcare problem, demonstrating their ability to derive insights from complex datasets and present solutions to stakeholders. Graduates of this course will be well-prepared for roles in healthcare organizations, pharmaceuticals, biomedical research, and public health, with the ability to harness data for improving patient outcomes and healthcare processes.
What You Will Learn:
By the end of this 6-month Diploma in Health Data Science, participants will be able to:
Master Python for Health Data Science:
- Apply advanced Python programming techniques for health data science, including object-oriented programming, data manipulation, and handling large healthcare datasets.
- Use essential Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn for data analysis and visualization specific to healthcare use cases.
Perform Data Wrangling and Visualization for Healthcare:
- Clean, preprocess, and transform healthcare datasets, dealing with missing data, outliers, and data inconsistencies.
- Create impactful visualizations to communicate findings, utilizing libraries like Matplotlib, Seaborn, and Plotly for healthcare data dashboards and reports.
Understand Statistical Analysis and Health Data Insights:
- Conduct statistical analysis of healthcare data, applying probability distributions, inferential statistics, and hypothesis testing to draw conclusions.
- Implement regression models and other statistical methods to analyze trends and patterns in patient data, epidemiological studies, and clinical trials.
Implement Machine Learning Models in Healthcare:
- Build and evaluate machine learning models, including supervised and unsupervised learning, tailored for healthcare applications such as disease classification, risk prediction, and patient clustering.
- Use advanced techniques like ensemble learning (e.g., XGBoost, LightGBM) and optimize models using cross-validation and hyperparameter tuning.
Gain Expertise in Precision Medicine and Bioinformatics:
- Understand the principles of precision medicine and how machine learning models are used to tailor treatments based on patient data.
- Analyze genomic data and apply bioinformatics techniques to identify genetic markers, analyze mutations, and predict patient outcomes in personalized medicine.
Tackle Big Data and Real-World Health Data Challenges:
- Explore and process large-scale health datasets, such as electronic health records (EHR), genomics data, and IoT healthcare devices, addressing challenges like data privacy, ethical considerations, and interoperability.
- Analyze real-world healthcare data to identify patterns, trends, and insights for improving patient care and operational efficiency.
Apply Deep Learning in Health Data Science:
- Build deep learning models using frameworks like TensorFlow and Keras, with applications in medical imaging (e.g., CNNs for image classification) and time-series health data (e.g., RNNs for patient monitoring).
- Explore advanced deep learning techniques in healthcare for tasks such as medical diagnostics, disease progression modeling, and natural language processing of medical texts.
Work on Real-World Capstone Projects:
- Complete a comprehensive capstone project focused on a real-world healthcare challenge, from data collection and preprocessing to model building and evaluation.
- Present findings through technical reports, visualizations, and oral presentations, showcasing proficiency in health data science to solve critical healthcare problems.
This diploma provides a balance of theory and hands-on practice, ensuring participants emerge as skilled health data scientists capable of tackling complex healthcare data challenges in a variety of roles within the industry.
