Diploma in
Health Data Science

The ” Introduction to Health Data Science” module provides a comprehensive overview of the field, covering the scope, current needs, and future impact. Delving into various health data types, the module explores the Data Science Adoption Model and real-world applications, emphasizing the growing role of healthcare data analysts. The section on “Exploring Health Data” goes further, elucidating different types, applications, and outcomes of health data through case studies. “Data Science for Healthcare Problem Solving” highlights the significance of data science in healthcare, addressing personalized medicine, epidemiological analysis, EHRs, and machine learning applications. The module concludes with insights into the role of health data scientists, tools, and ethical considerations, equipping learners for impactful contributions in healthcare analytics.

The “Introduction to Biology and Concepts of Genetics/Genomics” module provides a comprehensive exploration of biological fundamentals and genetic principles. Covering cell structure, biomolecules, Mendelian inheritance, and chromosomal theory, learners gain insights into the intricate world of genetics. The module extends to genomics, molecular biology, and human genetics, unraveling the human genome’s structure and the implications of genetic disorders. Epigenomics, genomics technologies, developmental biology, and population genetics further enhance understanding. The module culminates in an overview of prevalent diseases, offering a holistic perspective on genetics in health. Participants acquire foundational knowledge and skills crucial for genetic research, healthcare, and personalized medicine.

The “Programming for Health Data” module provides a robust foundation in utilizing programming languages, R and Python, for effective health data analysis. Participants delve into coding environments setup, syntax, and fundamental data structures, engaging in practical exercises for hands-on proficiency. The module progresses to essential data manipulation stages, including cleaning and exploration, emphasizing techniques for handling missing data, outliers, and exploratory data analysis. Further, participants acquire skills in data visualization principles, crafting impactful visualizations and mastering communication strategies. The inclusion of SQL fundamentals, Power BI, and Python data exploration enhances learners’ abilities to analyze, visualize, and communicate health data insights effectively.

The “Statistics for Health Data Science” module offers a comprehensive exploration of advanced statistical methods crucial in health data analysis. Participants begin with an understanding of the role of statistics in decision-making processes, learning about variables, data types, and the significance of computer languages. Descriptive statistics, including measures of central tendency and dispersion, are covered along with data visualization techniques using programming languages like Python and R. Inferential statistics, probability distributions, regression analysis, and Bayesian statistics are thoroughly addressed, providing participants with a profound understanding of statistical methods in the context of health data. Time series analysis and advanced Bayesian topics further enrich the module, equipping learners with diverse statistical tools for healthcare applications.

The “Health Data Management” module navigates participants through the crucial aspects of handling health data in modern healthcare. Covering data collection, quality assurance, integration, analysis, and visualization, participants gain practical skills using Python with real health datasets. The module also delves into data security, governance, and ethics, ensuring a comprehensive understanding of responsible data management. By exploring various health data types and utilizing AI models, participants are equipped to perform end-to-end data analysis, fostering a deep appreciation for the ethical considerations and governance needed in health data management. This module empowers learners to navigate the intricate landscape of health data responsibly and effectively.

The “Machine Learning for Health Data Science” module provides a comprehensive exploration of machine learning (ML) applications in healthcare. Covering supervised learning algorithms like linear regression and random forests, unsupervised learning techniques including clustering and association rule mining, and reinforcement learning for treatment optimization, participants gain hands-on experience in Python. The module also delves into natural language processing (NLP) for analyzing textual health data. Through project development sessions, participants apply ML algorithms to real health and genomics datasets, fostering skills to derive valuable insights for personalized healthcare and genomic research. This module empowers learners to harness ML for impactful applications in the health domain.

The “Genomics Health Data: Practical Real-world Aspects” module offers a hands-on exploration of essential aspects in genomic data science. Participants delve into genomic technologies, learn Python for genomic data analysis, and gain insights into algorithms for DNA sequencing. Practical exercises using Galaxy server and command-line interfaces (LINUX/BASH) enhance proficiency. Bioconductor is introduced for specialized genomic data analysis. The module covers diverse Next-Generation Sequencing (NGS) data analyses, including GWAS, single-cell RNA seq, bulk RNA seq, and Whole Genome/Exome Sequencing (WGS/WES). Real-world applications and data analysis provide a comprehensive understanding, equipping learners with practical genomics skills for impactful research and healthcare applications.

The “Image Analysis for Health Data” module immerses participants in the intricate realm of medical imaging. From understanding diverse modalities like MRI and CT to mastering preprocessing techniques and segmentation methods, learners gain a comprehensive skill set. Feature extraction, image classification, and recognition using machine learning and deep learning, particularly CNNs, facilitate sophisticated image analysis. Object detection, image registration, and fusion techniques enhance proficiency. The module extends to 3D imaging, quantitative analysis, and leveraging imaging for diagnosis and prognosis. Deep learning applications, ethical considerations, and real-world clinical insights provide a holistic foundation for impactful image-based health data analysis.

The “Epidemiology for Health Data Science” module unveils the pivotal role of epidemiology in deciphering health patterns. Beginning with fundamental concepts, it navigates through study designs, measurement techniques, and crucial aspects like bias, confounding, and effect modification. Advanced topics include causal inference, clinical epidemiology, environmental and genomic epidemiology, and molecular epidemiology. Statistical methods and applications, coupled with data visualization, equip learners with analytical prowess. Embracing contemporary technologies, the module delves into machine learning and big data analytics, empowering participants to glean valuable insights from expansive health datasets. The module fosters adept epidemiologists well-versed in cutting-edge data science methodologies.

This module will be a complete hands-on training based module