Arrive Ready.
Perform from Day One.
Your graduate program will expect you to analyze data, run statistical models, and produce research ready tables in R. We make sure you can before your first semester begins, no matter what field you are entering.
Have questions first? Book a 30 minute callThe data skills gap your professors will never warn you about
DataReady is a 6 week live cohort program built for graduate students heading into any research-intensive program where you will be expected to analyze data. Whether your field is business, social sciences, economics, education, psychology, biology, engineering, public health, nursing, agriculture, or any data-driven discipline, you will learn R, statistical analysis, research table generation, data visualization, and AI assisted research 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 first methods course, your professor loads a dataset and asks you to run a statistical analysis. They do not teach you the software. They assume you already know how to operate in R.
By the time you realize you cannot produce a clean statistical table or interpret a regression output, 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 basic analysis and give me a summary by Friday. If you cannot deliver, the research role or funding you worked years for becomes fragile.
# What your professor expects you to produce by Week 3 of your program > nhanes_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.
Three live Zoom sessions each week over 6 weeks. You work through real 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 regression analysis, hypothesis testing, and linear and logistic models, all taught on real datasets. You learn to produce publication ready research tables with gtsummary and professional figures with ggplot2, the same output expected in graduate programs across every research discipline.
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.
You will not learn on fake or simplified data.
DataReady trains you on large, real, publicly available datasets so the skills you build transfer directly to whatever data your own field gives you. We use the National Health and Nutrition Examination Survey and the National Health Interview Survey as our teaching datasets because they are massive, well documented, and structurally similar to the survey, administrative, and research datasets used across business, social sciences, economics, psychology, education, and health disciplines. The R, statistics, and visualization skills are the same no matter what data your program hands you.
A rich, nationally representative survey combining interviews with physical and lab measurements. Over 10,000 observations and hundreds of variables. Perfect for teaching data cleaning, descriptive analysis, regression, and visualization on data that behaves the way real research data behaves.
Primary Teaching DatasetRunning since 1957, NHIS contains multi-year panel data on demographics, household structure, economics, and wellbeing. Ideal for teaching regression, cross-tabulations, and longitudinal thinking. The patterns and methods translate directly to datasets in economics, social research, and policy.
Primary Teaching DatasetIn Premium and Private tiers, your instructor adapts assignments to datasets from your own field of study whenever available.
Six weeks. Real datasets. Skills your professors expect on day one.
Every week builds on real data. By week 6, you will have produced research tables, regression output, professional figures, and a complete capstone analysis ready to showcase in any field.
How researchers think about data. Installing R and RStudio. Understanding objects, vectors, and data frames. Loading and exploring a large real dataset for the first time. The dplyr verbs that power real analysis: filter, select, mutate, group_by, summarize. Handling missing values and recoding variables.
Summary statistics, frequency tables, and publication ready output using gtsummary. By end of this week you produce a professional descriptive summary table from your own analysis, the same format required in every methods paper and thesis.
ggplot2 from the ground up. Bar charts, histograms, box plots, scatter plots, and forest plots. Publication quality figures that meet journal, conference, and coursework standards in any field.
Analyzing categorical variables. Chi-square tests of independence, calculating and interpreting odds ratios and risk ratios, and the core of linear regression. Interpreting coefficients, confidence intervals, and p-values. The foundation of quantitative research across every social, behavioral, and scientific discipline.
Binary outcomes, adjusted models, interpreting exponentiated coefficients, generating clean regression tables with gtsummary, and building forest plots to visualize your results. The analysis method used across business, social sciences, economics, psychology, and health research.
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 = nhanes_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 professor assigns an analysis of a real dataset. You open R, clean the data, run your model, and produce a clean regression table with interpretation before the deadline.
Your methods course requires a descriptive summary table grouped by your variable of interest. You produce it with gtsummary in under 30 minutes, formatted exactly the way journals and theses 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 a coefficient of 1.38 tells you about your variable, you answer without hesitation.
You know how to access, clean, and analyze real survey and research data. When your professor or supervisor hands you a dataset with thousands of observations and hundreds of variables, you already know how to approach it.
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 graduate program where you will be expected to analyze data, run statistical models, or produce research output
Your field is any research-intensive discipline, including business, economics, social sciences, psychology, education, public health, nursing, biology, agriculture, engineering, or data science
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 who needs R, regression, and data interpretation skills to advance your work
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 program already knowing how to clean data, run regressions, and produce a research table. My classmates were still installing R in Week 3. That head start changed everything about my first semester."
"My research supervisor handed me a dataset on day one and asked for a full analysis by Friday. Because of DataReady, I delivered it with clean tables and figures. 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."
"I came from an MBA background with almost no statistics. By Week 4 I was running regressions for my marketing analytics class. The live format made everything click in a way videos never did."
"Learning on real survey data before arriving meant I already understood the kind of data my professors were referencing. That familiarity gave me confidence I did not expect to have."
"The gtsummary and ggplot2 modules alone were worth the entire investment. I produced cleaner tables and figures than some second year students. My professor asked me where I learned it."
Three paths. One outcome. Pick the one that fits your situation.
The same core curriculum runs across all three tiers. The difference is how much direct access you want. Early bird pricing ends April 30. Cohort 1 begins May 1, 2026.
- 18 live Zoom sessions over 6 weeks
- Real world teaching datasets
- Full regression module and research tables
- Session recordings for 30 day review
- Weekly assignments with written feedback
- Private cohort WhatsApp community
- AI for academic work module
- Capstone presentation and certificate
- Everything in the Cohort tier
- 3 private 1-on-1 sessions with the instructor
- Personalized dataset selection from your field
- Direct code review on your capstone project
- Priority response in the cohort WhatsApp
- Curriculum Vitae review for graduate and research applications
- Extended 60 day recording access
- Full curriculum delivered 1-on-1
- Flexible scheduling built around your timezone
- Custom datasets aligned with your thesis or field
- Direct code review on every assignment
- Unlimited instructor messaging during the program
- Full capstone mentorship
- Extended 90 day recording access
Greener alumni receive 20% off any tier. Message us on WhatsApp before enrolling and we will send you an alumni Stripe link.
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.
The curriculum and core training are identical across all tiers. Cohort is the live group experience. Premium adds 3 private sessions, a Curriculum Vitae review, and personalized dataset selection. Private is fully 1-on-1, flexible around your schedule, and built around your research focus with unlimited messaging during the program.
All sessions are recorded. Cohort students get 30 day recording access, Premium gets 60 days, Private gets 90 days. 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.
Any graduate program where you will be expected to analyze data. That includes business, economics, finance, social sciences, psychology, sociology, political science, education, public policy, public health, epidemiology, biostatistics, health services research, nursing, biology, agriculture, environmental sciences, engineering, computer science, and data science. If your program involves working with data and you are not yet confident in R and statistical analysis, this program was designed for you.
Yes. DataReady covers descriptive statistics, chi-square tests, correlations, linear regression, and logistic regression. These are the exact methods your first semester statistics, methods, or quantitative research courses will cover, whether you are in business, social sciences, psychology, education, public health, or any data-driven discipline. 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.
Three live sessions per week over 6 weeks, plus one practice assignment that typically takes 60 to 90 minutes. Total approximately 5 to 6 hours per week. Designed to be intensive enough to build real skills without overwhelming someone also finishing their final semester or working.
Because practice data is clean and small, and real data is messy and large. The skills you need in your graduate program are the skills for handling real research data. We use two large, well documented public datasets for teaching because they behave the way real research datasets behave. The R skills, statistical methods, and research table techniques transfer directly to whatever data your own field uses, from business and economics to social sciences and health research.
Full refund up to 7 days before the cohort begins if you decide DataReady is not right for you. After the first live session, refunds are prorated based on sessions attended.
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 at the University of Florida who uses R, regression modeling, 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 when you enter any research-intensive 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, 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. Six weeks of live training on the exact statistical methods, research table formats, and visualization techniques that graduate programs demand across business, social sciences, economics, psychology, education, health sciences, and any quantitative discipline. Taught by someone who knows what your professors will ask for, because they produce that same output every day.
Your first semester starts before you land.
25 seats. One cohort. May 1, 2026. The students who enroll today will arrive in their programs producing clean regression tables and professional figures while their classmates are still figuring out how to install R.
See Enrollment OptionsStill weighing it up? Book a 30 minute call and we will walk you through which tier fits your situation.
