| 1 | Apply the theoretical foundations of probability theory and distribution theory* (same as MS in Biostatistics) | BIOSTAT601 |
| 2 | Apply foundational mathematical statistical concepts and skills for conducting statistical inference* (same as MS in Biostatistics) | BIOSTAT602 |
| 3 | Perform linear regression model fitting and diagnostic assessment* (same as MS in Biostatistics) | BIOSTAT650 |
| 4 | Understand the main components of generalized linear models and how to choose an appropriate model based on the outcomes and study design* (same as MS in Biostatistics) | BIOSTAT651 |
| 5 | Fit generalized linear models for various outcome types and provide correct interpretation of the results* (same as MS in Biostatistics) | BIOSTAT651 |
| 6 | Apply data science techniques in the analysis of health data, including data cleaning, exploratory data analysis, and data visualization | BIOSTAT620 |
| 7 | Apply basic informatics and computational techniques in the analysis of big health data, and interpret results of statistical analysis | BIOSTAT625 |
| 8 | Master the theoretical foundations to design and apply machine learning algorithms in biomedical applications. Understand the process of developing and assessing machine learning algorithms, including design principles, parameter estimations, and performance evaluation. Understand a diverse set of commonly used machine learning algorithms in both supervised and unsupervised learning scenarios | BIOSTAT626 |
| 9 | Apply quantitative techniques commonly used to summarize and display big public health data | BIOSTAT629 |
| 10 | Apply descriptive and inferential methodologies according to the type of study design or sampling technique for answering a particular public health question | BIOSTAT629 |
