Course SSTK6071 - Health Statistics Analysis

1. Introduction to Statistics in Health Sciences

2. Descriptive Statistics for Health Data

3. Data Tabulation and Visualization

4. Probability Distributions in Health Sciences

5. Fundamentals of Hypothesis Testing

6. Parametric and Nonparametric Statistics

7. Correlation Analysis

8. Parametric Tests for Comparing Groups

9. Nonparametric Tests for Comparing Groups

10. Regression Analysis

11. Fundamentals of Multivariate Statistics

12. Instrument Validity and Reliability

13. Categorical Data Analysis

14. Comprehensive Applications and Case Studies

Course SSTK6041 - Environmental Health Statistics 

1. Statistics in Environmental Health

2. Environmental Health Data Types and Exploratory Data Analysis (EDA)

3. Probability and Distributions for Environmental and Health Data

4. Estimation and Inference: Confidence Intervals, Hypothesis Testing, and Statistical Power

5. Linear Regression: Fundamentals of Modeling, Diagnostics, and Interpretation of Exposure Effects

6. Generalized Linear Models (GLMs): Binary Outcomes and Count Data

7. Confounding, Interaction, and Standardization in Environmental Epidemiology

8. Environmental Health Study Designs and Their Analysis

9. Survival Analysis for Environmental Exposures

10. Longitudinal and Clustered Data: Mixed-Effects Models and Generalized Estimating Equations (GEE)

11. Time-Series Analysis in Environmental Health: Trends, Seasonality, and Lagged Effects

12. Spatial and Spatiotemporal Statistics for Environmental Epidemiology

13. Measurement Error, Exposure Assessment, and Missing Data

14. Evidence Synthesis and Policy Translation: Meta-Analysis, Health Impact Assessment (HIA), and Reproducible Reporting

Course SSTK2019 - Introduction to Data Science

1. Introduction to R and Python

2. Data Exploration and Analysis (Data Frames, Central Tendency and Dispersion)

3. Data Exploration and Analysis (Distribution, Exploration, and Charts)

4. Data and Sampling Distributions (Confidence Intervals, Normal Distribution, Student’s t-distribution)

5. Data and Sampling Distributions (Binomial, Chi-Square, F-Distribution, Poisson, and Related Distributions)

6. Statistical Experiments and Significance Tests (T-Test, Multiple Testing)

7. Statistical Experiments and Significance Tests (ANOVA, chi-square, Fisher’s exact test)

8. Regression and Prediction (Simple linear regression)

9. Regression and prediction (Multiple linear regression)

10. Classification (Naive Bayes, Discriminant analysis)

11. Classification (Logistic regression)

12. Machine learning statistics (K-Nearest Neighbors)

13. Machine learning statistics (Tree Models)

14. Unsupervised learning (Principal Component Analysis, K-Means Clustering)

Course SSTK6039 - Epidemiology

1. Epidemiology as a Science of Population Inference and Health Data

2. Measures of Disease Frequency and Measures of Association

3. Epidemiological Study Designs and Their Statistical Implications

4. Exposure and Outcome Measurement: Validity, Reliability, and Measurement Bias

5. Bias, Confounding, and Effect Modification within a Causal Framework

6. Stratified Analysis and Classical Methods for Controlling Confounding

7. The Modeling Framework: Generalized Linear Models (GLMs) as a Unified Language for Epidemiological Analysis

8. Logistic Regression for Binary Outcomes: From Odds Ratios to Standardized Risks

9. Models for Count Data and Rates: Poisson Regression, Incidence Rate Ratios (IRRs), Overdispersion, and Offset Terms

10. Survival Analysis: Kaplan–Meier Estimation and Cox Models in Epidemiology

11. Randomized Controlled Trials and Experimental Designs: Analysis, Randomization, and Reporting

12. Surveillance, Outbreak Investigation, and Field Data Analysis

13. Evidence Synthesis: Systematic Reviews, Meta-Analysis, and Reporting Standards

14. Causal Epidemiology and Advanced Analytics: Target Trial Emulation, G-Methods, and Reproducibility

Course SSTK6079 - Meta Analysis

1. Fundamental Concepts of Meta-Analysis and Systematic Reviews

2. Systematic Review Planning, Study Selection, and Risk of Bias

3. Concepts of Effect Sizes, Variance, Precision, and Confidence Intervals

4. Effect Sizes for Continuous Data

5. Effect Sizes for Binary Data and Correlation Coefficients

6. Effect Size Conversion and Harmonization of Study Results

7. Fixed-Effect and Random-Effects Models in Meta-Analysis

8. Principles of Statistical Inference and Interpretation of Meta-Analysis Results

9. Heterogeneity in Meta-Analysis

10. Publication Bias, Missing Data, and Sensitivity Analysis

11. Prediction Intervals, Subgroup Analysis, and Meta-Regression

12. Concepts and Estimation in Network Meta-Analysis

13. Meta-Analysis of Diagnostic Test Accuracy

14. Interpretation, Evaluation, and Reporting of Meta-Analysis Results

A. Courses SMPA1011  I  Statistics Method
1. Concepts of Statistics and Types of Data
2. Data Collection and Data Presentation
3. Data Visualisation in Descriptive Statistics
4. Measures of Central Tendency and Dispersion
5. Probability Theory
6. Probability Distributions of Discrete Random Variables
7. Probability Distributions of Continuous Random Variables

B. Courses SSTK6056  I Machine Learning
1. Decision Tree
2. Introduction of Artificial Neural Network (ANN)
Project (UAS) Machine Learning

C. Courses SSTK2020  I Introduction of  Biostatistics
1. Biostatistics
2. Data and Number
3. Descriptive Methods
4. Probability and Life Table
5. Study Design in Epidemiology/Biostatistics
Tugas Pengantar Biostatistika.

D. Courses SSTK6044  I Demographic Statistics
1. Complete Life Table (Table Kematian Lengkap)
2. Abridge Life Table (Tabel Kematian Singkat)
3. Mathematical Methods of Population
4. Cohort-Component Method of Population Projection
UAS Statistika Kependudukan