Arrive Ready.
Perform from Day One.
Your US graduate program expects you to run regressions, interpret odds ratios, and produce research tables in R. We make sure you can before your first semester begins.
The data skills gap your professors will never warn you about
DataReady is an 8 week live cohort program built for international graduate students in public health, epidemiology, and health sciences heading to US programs. You learn R, statistical analysis, research table generation, data visualization, and AI assisted research using real US public health datasets before your first semester begins.
Your admission letter said nothing about this.
Three realities that hit international graduate students in their first semester. DataReady solves all of them before you ever land.
Week one of your biostatistics course, your professor loads a BRFSS extract and asks you to run a logistic regression. They do not teach you how. They assume you already know.
By the time you realize you cannot interpret an odds ratio or generate a clean gtsummary table, the semester is already four weeks in. Catching up while keeping up is a position most students never recover from.
Your supervisor hands you a dataset and says run a chi-square analysis and give me a forest plot by Friday. If you cannot deliver, the funding you worked years for becomes fragile.
# What your professor expects you to produce by Week 3 of your program > brfss_clean %>% + tbl_summary( + by = diabetes_status, + include = c(age, bmi, smoking, exercise, income), + statistic = list( + all_continuous() ~ "{mean} ({sd})", + all_categorical() ~ "{n} ({p}%)" + )) %>% + add_p() %>% + bold_p() %>% + bold_labels() Characteristic No Diabetes Diabetes p-value ------------------------------------------------------- Age, mean (SD) 42.3 (14.1) 58.7 (12.4) <0.001 BMI, mean (SD) 26.8 (5.2) 32.1 (6.8) <0.001 Current smoker 1,284 (18%) 487 (24%) 0.002 Regular exercise 4,891 (68%) 892 (44%) <0.001 Income >$50K 3,412 (48%) 641 (32%) <0.001
Three steps from enrolled to prepared
DataReady is a live cohort, not a self paced course. Every student moves through the same program together with live instruction and real feedback.
You receive your welcome packet, datasets, R installation guide, and access to the private cohort WhatsApp group before the first session.
Two 90 minute live Zoom sessions every week. You work through real US public health datasets, complete assignments, and get direct feedback from your instructor.
On the final session you present an original analysis with research tables, regression output, and a forest plot. You receive your certificate and a First Semester Survival Kit.
Three pillars. One program built for your exact situation.
DataReady is not a generic R course. It was built around three pillars that address everything a student needs to arrive ready for US graduate school.
The core of the program. From data cleaning through logistic regression, chi-square tests, odds ratios, and linear regression, all taught on real US public health datasets. You learn to produce publication ready research tables with gtsummary and forest plots with ggplot2.
A dedicated module on using ChatGPT and Claude ethically and effectively for graduate school. How to debug code with AI, use it to understand statistical output, and get writing feedback without crossing academic integrity lines.
The invisible knowledge most students learn the hard way. Advisor relationships, office hours culture, how to communicate with professors, managing imposter syndrome, and building your peer network before you land.
The same datasets your professors will use on day one.
You will not learn on fake data. DataReady trains you on six real, publicly available US public health datasets. These are the exact ones referenced in graduate syllabi, research papers, and RA assignments across American universities.
The largest continuously conducted health survey in the world. Over 400,000 respondents annually. Covers chronic disease, behavioral risk factors, and preventive health practices across all 50 states.
Primary DatasetCombines interviews with physical examinations and lab tests. The gold standard for nutritional and health status data in the US population. Used extensively in epidemiology coursework.
Used in TrainingMortality data, birth records, cancer statistics, and environmental health data. The database most commonly used for mortality analysis and trend studies in US public health programs.
Used in TrainingThe most complete source of data on healthcare costs, insurance coverage, and utilization in the United States. Essential for health services research and health policy coursework.
Used in TrainingRunning since 1957, NHIS provides annual data on health conditions, healthcare access, and health behaviors. One of the most cited datasets in American public health literature.
Used in TrainingMonitors health risk behaviors among youth and young adults including substance use, sexual behavior, diet, and physical activity. Used in adolescent health and behavioral epidemiology courses.
Used in TrainingEight weeks. Real datasets. Skills your professors expect on day one.
Every week builds on real US public health data. By week 8, you will have produced research tables, regression output, forest plots, and a complete capstone analysis.
How researchers think about data. Installing R and RStudio. Understanding objects, vectors, and data frames. Loading and exploring the BRFSS dataset for the first time. Introduction to the datasets you will encounter in your US program.
The dplyr verbs that power real analysis: filter, select, mutate, group_by, summarize. Handling missing values and recoding variables. Preparing raw survey data for analysis, the step professors assume you already know.
Summary statistics, frequency tables, and publication ready output using gtsummary. By end of this week you produce a professional Table 1 from your own BRFSS analysis, the same format required in every methods paper.
ggplot2 from the ground up. Bar charts, histograms, box plots, scatter plots, and forest plots. Publication quality figures from your BRFSS data that meet journal and coursework standards.
Analyzing categorical variables. Chi-square tests of independence, calculating and interpreting odds ratios, building cross tabulations, and understanding when to use each test. The foundation of epidemiological analysis.
Simple and multiple linear regression. Interpreting coefficients, confidence intervals, and p-values. Model diagnostics and assumptions. How to present regression results in a format your professor expects.
Binary outcomes, adjusted models, interpreting exponentiated coefficients as odds ratios, generating clean regression tables with gtsummary, and building forest plots to visualize your results. The analysis method used in most epidemiology courses.
Ethical AI use for academic and research work. Then the capstone symposium: each student presents one original analysis with a research table, regression output, and a forest plot. You leave as a researcher, not just a course completer.
| Variable | OR | 95% CI | p-value |
|---|---|---|---|
| BMI (per unit increase) | 1.12 | 1.08, 1.16 | <0.001 |
| Age (per year) | 1.04 | 1.02, 1.06 | <0.001 |
| Current Smoker (vs Non-Smoker) | 1.38 | 1.11, 1.72 | 0.004 |
| Regular Exercise (vs None) | 0.67 | 0.54, 0.83 | 0.003 |
| College Degree (vs No Degree) | 0.81 | 0.62, 1.05 | 0.112 |
| Income >$50K (vs <$50K) | 0.74 | 0.58, 0.94 | 0.015 |
| OR = Odds Ratio; CI = Confidence Interval. Model adjusted for all listed covariates. Bold indicates statistical significance (p < 0.05). | |||
# The R code that produces the table above > model <- glm(diabetes ~ bmi + age + smoking + exercise + + college_degree + high_income, + data = brfss_clean, family = binomial()) > tbl_regression(model, exponentiate = TRUE) %>% + bold_p() %>% + bold_labels() %>% + as_gt() %>% + gt::gtsave("table2_diabetes_risk_factors.docx")
What you will walk into your program able to do.
These are not theoretical outcomes. These are the exact tasks your courses, professors, and supervisors will assign you in your first semester.
Your epi professor assigns a BRFSS analysis on diabetes risk factors. You open R, clean the data, run glm(), and produce a clean regression table before the deadline.
Your methods course requires a descriptive summary table by demographic groups. You produce it with gtsummary in under 30 minutes, formatted exactly the way journals expect.
Your supervisor asks for a visual summary of your adjusted model. You produce a clean forest plot in ggplot2 that shows odds ratios with confidence intervals, ready for a presentation or paper.
When your professor asks what a p-value of 0.003 means in context, or what an OR of 1.38 tells you about the exposure, you answer without hesitation.
BRFSS, NHANES, CDC WONDER. You know how to access them, clean them, and use them for analysis. When your RA supervisor mentions any of these, you already know the data structure.
You know how to use ChatGPT and Claude to debug R code, explain statistical concepts, and get feedback on your writing without violating your university's academic integrity policy.
Built for a very specific student at a very specific moment.
DataReady is not for everyone. It was built for students at one particular stage of their journey, and it is exactly right for that stage.
You have been admitted to a US graduate program in public health, epidemiology, health sciences, global health, or a related field
You are starting in Fall 2026 or January 2027 and want to arrive prepared, not spend your first semester catching up
Your data analysis skills are limited or rusty and you want to fix that before you land, not after your first midterm
You want live instruction with real feedback, not a video course you will abandon by Week 2
You are an early career researcher or professional in health sciences who needs R, regression, and data interpretation skills to advance your work
You are a Greener student heading to the US who wants to go further than just getting admitted
You are already comfortable running regressions, interpreting odds ratios, and generating research tables in R. DataReady is designed for students building these skills from the ground up. If you already produce gtsummary tables and forest plots in your sleep, this program will feel slow for you.
Real Students. Real Preparation.
"I arrived at my MPH program already knowing how to clean data, run regressions, and produce a Table 1. My classmates were still installing R in Week 3. That head start changed everything about my first semester."
"My RA supervisor handed me a BRFSS extract on day one and asked for a logistic regression by Friday. Because of DataReady, I delivered it with a forest plot. She told me she had never seen a first year do that."
"I thought I would pick up R when I got there. I did not. The first semester was brutal. I wish DataReady had existed when I was preparing to leave for the US."
"The live format made all the difference. When I could not interpret an odds ratio, the instructor walked me through it in real time. No pre recorded course does that for you."
"Learning on BRFSS and NHANES before arriving meant I already understood the data my professors were referencing. That familiarity gave me confidence I did not expect to have."
"The gtsummary module alone was worth the entire investment. I produced cleaner tables than some second year students. My professor asked me where I learned it."
One price. Every student. No tiers.
We kept this simple because the program is the same for everyone. Full access, full experience, from day one.
Cohort 1 starts May 1, 2026
Sessions: Tuesdays and Thursdays, 7pm WAT
Duration: 8 weeks (16 live sessions)
Maximum: 25 students
Greener alumni receive 20% off. Message us on WhatsApp before enrolling
- 16 live Zoom sessions over 8 weeks
- 6 real US public health datasets (BRFSS, NHANES, CDC WONDER, MEPS, NHIS, YRBSS)
- Full regression module: linear, logistic, chi-square, odds ratios
- Research table generation with gtsummary
- Data visualization and forest plots with ggplot2
- Session recordings for 30 day review
- Weekly assignments with written feedback
- Private cohort WhatsApp community
- AI for academic work module
- Graduate school survival in the US module
- Capstone symposium presentation
- Branded DataReady certificate of completion
- First Semester Survival Kit PDF
Frequently Asked Questions
No. DataReady starts from the very beginning. The only requirement is that you can operate a computer and you are willing to show up and practice. Students with zero prior exposure to R have completed this program and arrived confident in their first semester.
All sessions are recorded and available for 30 days after the program ends. We strongly encourage live attendance because the interaction and real time feedback are where most of the learning happens, but we understand schedules sometimes conflict.
DataReady is built for students in public health, epidemiology, biostatistics, health services research, global health, health policy, and closely related fields. If your program involves working with data and you are not yet confident in that area, this program was designed for you.
Yes. DataReady covers descriptive statistics, chi-square tests, odds ratios, linear regression, and logistic regression. These are the exact methods your first semester biostatistics and epidemiology courses will cover. You will arrive knowing the R code and the interpretation, not just the theory.
Because live instruction with real feedback only works at a scale where your instructor can actually see you, respond to your questions, and know where you are struggling. Large cohorts produce passive students. DataReady is built for active learners who want genuine engagement.
Two live sessions of 90 minutes each, plus one practice assignment that typically takes 60 to 90 minutes. Total approximately 4 to 5 hours per week. Designed to be intensive enough to build real skills without overwhelming someone also finishing their final semester or working.
Because your professors will hand you BRFSS, NHANES, or CDC WONDER data on your first day. If you have already cleaned, analyzed, and presented findings from these exact datasets, you walk into your program with an advantage no textbook can give you.
Yes. DataReady is built around the US graduate school context but the R skills, regression analysis, research table generation, and AI literacy module are valuable for any researcher or early career professional in health sciences anywhere in the world.
Built by someone who runs regressions every day.
DataReady was created by a doctoral researcher in health services research at the University of Florida who uses R, logistic regression, and gtsummary in active research daily. This is not someone who learned R to teach it. This is someone who lives inside the tool and built a program around what actually matters in a US graduate program.
Since founding Greener Educational Consult in 2018, over 500 students across 20 countries have been guided into funded graduate programs in the US, UK, Canada, China, and Europe. The one consistent gap that emerges after admission is data skills. Students arrive to their programs and find themselves behind from the first week, unable to run a basic regression or produce the tables their professors expect.
DataReady is the direct answer to that gap. Eight weeks of live training on the exact datasets, statistical methods, and output formats that US public health programs demand. Taught by someone who knows what your professors will ask for, because they work alongside them.
Your first semester starts before you land.
25 seats. One cohort. May 1, 2026. The students who enroll today will arrive in the US producing regression tables and forest plots while their classmates are still figuring out how to install R.
Reserve Your SeatQuestions? Message us on WhatsApp or book a free consultation above