Biostatistical Data Analysis in Healthcare
( 30 Modules )

Module #1
Introduction to Biostatistics in Healthcare
Overview of biostatistics, importance in healthcare, and role of biostatisticians

Module #2
Descriptive Statistics in Healthcare
Summary statistics, graphical methods, and data visualization in healthcare

Module #3
Probability and Statistical Inference
Basic concepts of probability, hypothesis testing, and confidence intervals

Module #4
Data Types and Measurement Scales
Types of data, measurement scales, and coding schemes in healthcare

Module #5
Data Collection and Study Designs
Observational studies, experimental designs, and data collection methods

Module #6
Data Preparation and Cleaning
Handling missing data, data normalization, and data transformation

Module #7
Basic Statistical Analysis
T-tests, ANOVA, regression, and correlation analysis

Module #8
Survival Analysis in Healthcare
Introduction to survival analysis, Kaplan-Meier estimates, and Cox regression

Module #9
Logistic Regression in Healthcare
Introduction to logistic regression, odds ratios, and model building

Module #10
Generalized Linear Models
Overview of generalized linear models, Poisson regression, and gamma regression

Module #11
Time Series Analysis in Healthcare
Introduction to time series analysis, ARIMA models, and forecasting

Module #12
Machine Learning in Healthcare
Introduction to machine learning, supervised and unsupervised learning, and model evaluation

Module #13
Predictive Modeling in Healthcare
Building predictive models, model validation, and model deployment

Module #14
Cluster Analysis and Dimensionality Reduction
Introduction to cluster analysis, k-means clustering, and principal component analysis

Module #15
Advanced Topics in Biostatistics
Generalized estimating equations, mixed-effects models, and Bayesian methods

Module #16
Real-World Applications of Biostatistics
Case studies in epidemiology, clinical trials, and healthcare policy

Module #17
Communicating Biostatistical Results
Effective communication of biostatistical results, visualizing data, and reporting

Module #18
Ethics and Regulations in Biostatistics
Ethical considerations, regulatory compliance, and good clinical practice

Module #19
Big Data and Biostatistics
Handling big data, data mining, and high-performance computing in biostatistics

Module #20
Cloud Computing and Biostatistics
Cloud-based biostatistical analysis, Amazon Web Services, and cloud computing platforms

Module #21
Biostatistical Computing with R
Introduction to R, R Studio, and biostatistical computing with R

Module #22
Biostatistical Computing with Python
Introduction to Python, NumPy, and biostatistical computing with Python

Module #23
Data Visualization with Tableau
Introduction to Tableau, data visualization, and dashboard creation

Module #24
Collaboration and Teamwork in Biostatistics
Effective collaboration, communication, and project management in biostatistics

Module #25
Career Development in Biostatistics
Career paths, professional development, and staying current in biostatistics

Module #26
Project Development and Implementation
Developing and implementing a biostatistical project, from concept to completion

Module #27
Biostatistical Consulting and Communication
Effective consulting, communication, and reporting in biostatistics

Module #28
Advanced Data Analysis with SAS
Advanced data analysis techniques using SAS, including macros and ODS

Module #29
Advanced Data Analysis with SQL
Advanced data analysis techniques using SQL, including data modeling and data warehousing

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Biostatistical Data Analysis in Healthcare career

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