Scholarship Program · August 2026 cohort

Data Analysis & AI Training Program

Delivered in Arabic·100% Online·Official Certificate

Applications close· 15 September 2026 — 5:00 PM (Riyadh)

Days

Hours

Minutes

Seconds

The curriculum

From your first data point to the final report

Everything the program gives you, from the first session to your certificate.

Live communitydirect supportReal datasetsnot toy examplesWatch & rewatchduring the training period

01.

Data basics & formats: CSV, Excel, SPSS (.sav), variables, and column structuring
Variable types: numerical vs. categorical (ICD codes, Age, BMI)
Distributions: the normal curve and the 68–95 empirical rule
Central tendency & spread: mean, median, mode, variance, SD, and IQR
Normality testing: Shapiro–Wilk, histograms, and Q–Q plots
Univariate reporting: Mean ± SD vs. Median [IQR]
Hands-on: extract and audit baseline tables from 3 published papers

02.

Matching research questions to the right statistical test
Hypothesis testing: null/alternative hypotheses, significance, and p-values
Test families: parametric & non-parametric bivariate tests, ANOVA, Chi-Square, regression
Multiple comparisons: when and how to apply post-hoc corrections
End-to-end workflow: cleaning, missing values, EDA, and execution

03.

AI chat vs. AI agents: conversational tools vs. autonomous frameworks
Agent ecosystems: Claude, Hermes, Codex, and a local OpenCode Agent setup
File formats for LLMs: why .md and .csv outperform .pdf and .docx
Coding ecosystems: the statistical roles of Python, R, and Shell
Pipeline generation: prompting agents to write, debug, and run reproducible scripts
Guided Naggar AI walkthrough (naggar.app)

04.

Live end-to-end cleaning, EDA, and statistical testing on a raw clinical dataset
OpenCode workflow: live script generation, terminal execution, and output debugging
Naggar AI workflow: the same dataset in naggar.app — speed, ease, and reporting quality compared
Post-course assessment briefing: exam guidelines, dataset delivery, submission format, and grading rubric

Instructor

Founder & Lead Biostatistician, Naggar Analytics

After the training

An independent submission after completing Session 4.

Clean a provided raw dataset and handle variable encoding

Generate a publication-ready Table 1 (baseline characteristics)

Select, justify, and execute the correct inferential models

Submit a verified, reproducible R/Python script with your findings summary

Score ≥ 80% → Official Certificate of Completion

50% discount voucher toward a Naggar AI (naggar.app) subscription

All fields marked * are required

Training starts after the September intake
Applicants are notified after the September cohort closes