You worked years to get here.
Do not arrive unprepared.
6-Week Live Programme for International Graduate Students in Health Sciences
For students heading to USA · UK · Canada · Australia · Europe
Starting from $159 early bird · Group, Premium + 1-on-1, or Private options
We will contact you within 24 hours to confirm your enrolment and send payment details. Check your inbox.
This is what your professor will put in front of you.
No warning. No tutorial.
Graduate programs in public health, epidemiology, and health sciences assume quantitative readiness from day one. These are real scenarios that play out every semester for international students who arrived without DataReady.
"Here is an a real dataset. I want a Table 1 by demographic groups with p-values by Thursday. Use publication-ready research tables. We will present in class."
"Run a logistic regression on diabetes risk. Exponentiate your coefficients, include confidence intervals, and give me a forest plot. Submit your R script."
"I need you to clean the real public health datasets dataset, recode the smoking variable, and give me a chi-square analysis by Friday morning. You know how to do this, right?"
The same table. One student struggled for days. One produced it in 30 minutes.
This is a real Table 1 and regression output of the kind produced in DataReady Week 3 and Week 7. By the time you finish the program, this is your baseline — not your ceiling.
| Characteristic | No Diabetes (n = 5,118) | Diabetes (n = 1,166) | p-value |
|---|---|---|---|
| Demographics | |||
| Age, mean (SD) | 42.3 (14.1) | 58.7 (12.4) | <0.001 |
| Female, n (%) | 2,661 (52%) | 604 (52%) | 0.961 |
| Clinical Measures | |||
| BMI, mean (SD) | 26.8 (5.2) | 32.1 (6.8) | <0.001 |
| Hypertension, n (%) | 1,432 (28%) | 712 (61%) | <0.001 |
| Behavioral Risk Factors | |||
| Current smoker | 921 (18%) | 280 (24%) | 0.002 |
| Regular exercise | 3,480 (68%) | 512 (44%) | <0.001 |
| SD = Standard Deviation. p-values from chi-square or independent samples t-test as appropriate. | |||
| Variable | OR | 95% CI | p-value |
|---|---|---|---|
| Adjusted Model | |||
| BMI (per unit) | 1.12 | 1.08, 1.16 | <0.001 |
| Age (per year) | 1.04 | 1.02, 1.06 | <0.001 |
| Current Smoker | 1.38 | 1.11, 1.72 | 0.004 |
| Regular Exercise | 0.67 | 0.54, 0.83 | 0.003 |
| Income >$50K | 0.74 | 0.58, 0.94 | 0.015 |
| College Degree | 0.81 | 0.62, 1.05 | 0.112 |
| Model Fit | |||
| AIC | 4,218 | — | — |
| Nagelkerke R² | 0.31 | — | — |
| OR = Odds Ratio. CI = Confidence Interval. Model adjusted for all listed covariates. Bold = statistically significant (p < 0.05). | |||
The students who came prepared. In their own words.
My supervisor handed me a dataset on day one and expected a full analysis by Friday. I did not ask for help. I did not panic. I delivered it a day early. She told me she had never seen a first-year do that without being explicitly taught. That moment defined how my entire programme went.
By week three my classmates were still trying to find their footing. I was already producing results. I could see the difference. My supervisor could see it. That gap did not close for them. It followed the entire semester.
I told myself I would figure it out when I arrived. That is what everyone tells themselves. I did not. My first semester was the hardest four months of my academic life. Not because the work was beyond me. Because I arrived unprepared for demands I could have been ready for. Do not make the decision I made.
Three Levels of Support. One Curriculum.
Every option includes the full 8-week curriculum, both datasets, all assignments, the capstone, and the certificate. Choose based on how much personalised support you need.
Live group training with 24 other students. Best for self-motivated learners.
- 18 live Zoom sessions over 6 weeks
- real US public health datasetssets
- publication-ready research tables, data visualization tools, logistic regression
- Assignments with written feedback
- Recordings for 30 days
- Capstone + certificate
Group cohort plus three private sessions focused on your specific research area.
- 3 private 1-on-1 sessions
- Tailored to your research area
- Work on your actual program dataset
- Pre-capstone personal review
- Priority assignment feedback
- All cohort content included
Fully private. Built around your research question, your timeline, your program.
- Fully private — no group
- Your pace, your schedule
- Curriculum around your research
- Work with your own datasets
- Full capstone with private coaching
- Certificate + Survival Kit
No payment until we confirm your seat. Pricing confirmed during your free consultation.
Eight weeks. Graduate-level preparation from day one.
This is not a beginner course dressed up as a graduate programme. DataReady covers the statistical methods your professors will assign, the output formats they expect, and the analytical thinking your supervisors will test from week one.
How researchers frame questions and approach data. Setting up your analytical environment. Loading, exploring, and understanding the structure of real public health datasets. Recoding variables, managing missing values, and producing a clean, analysis-ready dataset — the foundational skill your supervisor will expect before they hand you anything else.
Summary statistics, frequency distributions, and cross-tabulations. Building the publication-ready Table 1 that every methods paper opens with — you produce one from real data before this week ends. Then data visualisation: bar charts, scatter plots, histograms, and forest plots formatted to journal and coursework standards. By week four you are producing the exact outputs your professor will assign in week one of your programme.
Chi-square tests of independence, relative risk, and odds ratios. Binary logistic regression — building the model, interpreting exponentiated coefficients, producing confidence intervals, and presenting results in the format your supervisor will hand back without corrections. You learn to read and write the statistical story your analysis tells, not just run the numbers.
Simple and multiple linear regression. Selecting covariates, checking model assumptions, interpreting coefficients and confidence intervals, and presenting adjusted models. Introduction to multivariable modelling strategy — how researchers decide what to adjust for and why. This is the level your biostatistics professor will expect by midterm.
Survival analysis and time-to-event data — Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression. Poisson and negative binomial regression for count outcomes. These are the methods that appear in the research your professors publish and the assignments they set for advanced students. Arriving with exposure to these methods signals a level of preparation your cohort will not expect.
Your original analysis presentation — research question, cleaned dataset, statistical model, and publication-ready output. Presented to the cohort with instructor feedback. Plus a dedicated module on using AI tools ethically and effectively in graduate school: how to use them to debug, interpret output, and strengthen your writing without crossing academic integrity lines. You leave with a DataReady certificate of completion and a First Semester Survival Kit PDF.
Frequently Asked Questions
25 seats. May 15, 2026.
The decision is simple.
The students who enrol today will arrive producing the tables and regression output their professors assign in week one. The students who do not will spend their first semester catching up. Seats are filling. Early bird ends April 25.
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