About the Course
Organizations worldwide depend on epidemiological evidence to guide health interventions, allocate resources, and respond to disease outbreaks, yet many professionals lack the technical skills to analyze complex health data rigorously. You need to demonstrate proficiency in calculating incidence rates, conducting survival analyses, managing longitudinal datasets, applying sampling weights, and interpreting confidence intervals in epidemiological contexts. Modern epidemiological practice requires fluency with advanced Stata programming, survey data methods, time-series analysis for surveillance systems, and automated reporting workflows that integrate with electronic health records and disease registries.
This course transforms scattered statistical knowledge into a systematic approach for epidemiological data analysis using Stata's specialized epidemiological commands and functions. You will practice hands-on analysis of infectious disease surveillance data, chronic disease cohort studies, case-control investigations, and cross-sectional health surveys. You will learn to execute logistic regression for risk factor analysis, Cox proportional hazards models for survival data, Poisson regression for count outcomes, and multilevel modeling for clustered health data. The course emphasizes real-world applications: you will work with datasets from CDC surveillance systems, WHO mortality databases, and longitudinal health studies. You will master data management techniques specific to epidemiological research, including handling of exposure windows, follow-up time, and competing risks. Rather than theoretical overview, 70% of course time involves hands-on Stata programming with immediate application to epidemiological scenarios.
We acknowledge that epidemiological data analysis occurs under real constraints: incomplete surveillance systems, budget limitations for software licenses, varying data quality across regions, and pressure to produce rapid analyses during health emergencies. This course is designed for professionals who must deliver statistically sound epidemiological evidence despite these operational realities, using Stata's robust capabilities for handling messy real-world health data.
Target Audience
This course serves public health professionals, epidemiologists, biostatisticians, and health researchers who analyze population health data and need to demonstrate statistical competency in epidemiological methods using Stata.
This course is designed for:
- Epidemiologists conducting outbreak investigations and surveillance analysis
- Public Health Analysts preparing evidence for policy and intervention decisions
- Disease Surveillance Officers analyzing infectious disease trends and patterns
- Biostatisticians supporting clinical and population health research projects
- Health Services Researchers evaluating healthcare delivery and outcomes
- Environmental Health Scientists analyzing exposure-disease relationships
- Chronic Disease Prevention Specialists conducting cohort and case-control studies
- Global Health Researchers working with international health datasets
- Academic Researchers publishing epidemiological studies in peer-reviewed journals
- Health Data Scientists developing automated surveillance and reporting systems
Course Objectives
This course equips you to design, execute, and interpret epidemiological analyses that generate robust evidence for disease prevention, health policy development, and clinical decision-making using Stata's specialized epidemiological functions.
By the end of this course, you'll be able to:
- Execute descriptive epidemiological analyses including incidence rates, prevalence estimates, and standardized mortality ratios using Stata commands
- Apply logistic regression models to identify risk factors and calculate odds ratios with proper confidence interval interpretation
- Implement Cox proportional hazards models for survival analysis in cohort studies and clinical trials
- Design and analyze case-control studies including matched designs and conditional logistic regression
- Calculate sample size requirements for epidemiological studies using Stata's power analysis functions
- Construct Kaplan-Meier survival curves and conduct log-rank tests for comparing survival distributions
- Navigate complex survey data analysis including sampling weights, stratification, and cluster effects
- Synthesize epidemiological findings into publication-ready tables, graphs, and statistical reports using Stata's output formatting
Requirements & Prerequisites
Participants should have basic understanding of epidemiological concepts (study designs, measures of association) and fundamental statistical knowledge (hypothesis testing, confidence intervals). Prior experience with any statistical software is helpful but not required. Access to Stata software will be provided during training, though participants are encouraged to have Stata available for post-course practice.
Local Application and Business Return
How participants can apply the training in local operating conditions, and the return their organisation can plan for.
How participants apply this
Expected ROI
Training Methodology
This is a practical, hands-on course designed to turn epidemiological data analysis aspirations into demonstrable Stata proficiency and statistical competency through intensive practice with real health datasets.
Methodology includes:
- Hands-on Stata programming exercises using CDC surveillance datasets and WHO mortality databases
- Case-control study simulation requiring analysis of exposure-disease relationships under time constraints
- Survival analysis workshop using longitudinal cohort data with censoring and competing risks
- Complex survey analysis exercise applying sampling weights to national health survey data
- Outbreak investigation scenarios from infectious disease surveillance requiring rapid statistical analysis
- Publication-ready output workshop producing epidemiological tables and figures under journal submission standards
- Statistical interpretation challenges using real epidemiological study results to test analytical reasoning
Upcoming Sessions
Next available dates worldwide
Certification
Recognized credentials that advance your career
Participants who complete the Epidemiological Data Analysis Using Stata Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.
NITA Accredited
Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.
CPD Certified
Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.
Why this course earns its place on your CV
Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.
In-Demand Technical Mastery
- Command Stata to analyze complex epidemiological datasets with confidence and precision.
- Master survival analysis, logistic regression, and outbreak modeling in one intensive program.
- Bridge the gap between raw health data and actionable public health decisions.
Expert-Led Practical Training
- Learn from seasoned epidemiologists who apply Stata in real-world research daily.
- Work through authentic disease surveillance datasets, not textbook simulations.
- Gain reproducible analysis workflows you can deploy in your role immediately.
Career and Credibility Acceleration
- Stand out in public health roles where Stata proficiency is a hiring prerequisite.
- Strengthen grant proposals and publications with rigorous, defensible statistical analyses.
- Join a professional network of data-driven epidemiologists advancing global health outcomes.
Tools and platforms relevant to this field
Examples local teams may encounter, and that may be featured in training where they support the confirmed course scope.
These are field-relevant examples, not a promise that every tool will be covered. Exact coverage depends on the confirmed course scope, participant needs, and delivery format.
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Stata StataCorpUsed for epidemiological data cleaning, regression modeling, survival analysis, and reproducible statistical workflows in public health and research settings.
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SAS Studio SAS InstituteUsed to run and manage SAS-based analyses through a browser interface in organizations standardizing statistical work.
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RStudio PositUsed to support coding, analysis, and reporting for epidemiological and biostatistical projects in research teams.
