# 🎓 QueryPilot v9 — Data Science Learning Hub

> **The complete reference for the new in-app learning curriculum.** Built for absolute beginners (market traders, secondary teachers, business analysts, career switchers) and engineered to take you from "what is data?" to "I deploy ML models for a living."

> *Curriculum authored by **[Adewale Samson Adeagbo](AUTHOR.md)** — 15+ years teaching Mathematics, Further Maths, Physics, Chemistry and CS in Nigerian classrooms; 12 deployed ML/EdTech projects across 7 industries; founder of [HMG Concepts](https://hmgconcepts.pages.dev), Lagos, Nigeria.*

---

## 📋 Table of Contents

1. [Why this curriculum exists](#why-this-curriculum-exists)
2. [Who it's for](#who-its-for)
3. [Pedagogical principles](#pedagogical-principles)
4. [The full structure — 9 modules, 118 lessons](#the-full-structure)
5. [Module-by-module breakdown](#module-by-module-breakdown)
6. [How to use the Hub (in-app)](#how-to-use-the-hub)
7. [Progress tracking & certificate](#progress-tracking--certificate)
8. [Lesson types explained](#lesson-types-explained)
9. [Time estimates & study plans](#time-estimates--study-plans)
10. [What you'll be able to do at the end](#what-youll-be-able-to-do-at-the-end)
11. [Recommended companion resources](#recommended-companion-resources)
12. [Career pathways after completion](#career-pathways-after-completion)
13. [For instructors](#for-instructors)

---

## Why this curriculum exists

There are thousands of data-science courses online. Most fail Nigerian (and broader African) learners for one or more of these reasons:

- **Too expensive.** $30-$300/month subscriptions are unsustainable on a Nigerian junior salary.
- **No Nigerian context.** Every example uses Boston housing or US churn data. Learners can't connect.
- **Assumed background.** "You should know Python" — but you don't, and the course never teaches it.
- **No completion.** 90% drop out by week 3 because content lacks structure or interactivity.
- **No deployment.** You finish with a Jupyter notebook nobody else can use.
- **No honesty.** They show only the successes; never the data leakage fix, the model that flopped, the embarrassing bias.

**This curriculum is the answer.** It is:

✅ **Free forever** — no subscription, no API, no ads, no upsell.
✅ **Offline-capable** — installable as a PWA; works on entry-level Android tablets.
✅ **Nigerian context** — jollof prices, JAMB scores, BVN, Lagos traffic, market sales — examples that connect.
✅ **Zero prerequisites** — Module 1 starts with "what is data?" Anyone literate can begin.
✅ **Structured & progressive** — 9 modules, 118 lessons, simple → intermediate → complex.
✅ **Interactive** — quizzes, exercises, "try in QueryPilot" buttons, projects every module.
✅ **Ends with deployment** — every learner ships a real Streamlit app to their GitHub.
✅ **Honestly disclosed** — limitations, ethics, what doesn't work, what's beyond scope.

---

## Who it's for

| Audience | What they'll gain |
|---|---|
| **Complete beginner / non-technical adult** | Vocabulary, mental model, ability to read any data report. By M3 — basic SQL analyst. By M8 — junior data scientist. |
| **Nigerian secondary student (SS2 / SS3)** | Real CS / DS exposure beyond the curriculum. Foundation for university CS / data degrees. |
| **University CS / Statistics undergraduate** | Practical bridge between theory and deployable skills. Portfolio-building projects. |
| **Business analyst / accountant** | Levelling up from Excel to SQL + Python + ML. Career-pivot path. |
| **Self-taught coder** | Structured DS curriculum to fill the gaps left by random YouTube tutorials. |
| **Teacher / instructor** | Ready-made syllabus with worked examples and projects (see [For instructors](#for-instructors)). |
| **Career switcher** | Realistic, time-bounded path with deployment evidence for CVs. |

---

## Pedagogical principles

Every lesson follows these rules (informed by Adewale's 15+ classroom years):

1. **No assumed prior knowledge.** If a term is used, it's defined.
2. **Concrete before abstract.** Real examples (jollof, JAMB, BVN) first; formulas after.
3. **Spiral curriculum.** Concepts reappear at increasing depth (mean appears in M2, formalised in M4, used in ML loss functions in M8).
4. **One idea per lesson.** Each lesson covers ~10-15 minutes of single-concept learning.
5. **Active learning.** Every lesson has a "Try It" button, exercise, quiz or project.
6. **Honest documentation.** What works AND what doesn't (data leakage, overfitting, p-hacking, bias).
7. **Vocabulary-first.** Every glossary term defined in plain English before use.
8. **Nigerian context default.** Examples ground learners in their own reality, not Silicon Valley's.
9. **Show, don't just tell.** Every concept demonstrated with code, output, or chart.
10. **Build for portfolio.** Every project produces an artifact you can show recruiters.

---

## The full structure

| # | Module | Icon | Level | Lessons | Weeks |
|---|---|---|---|---|---|
| **M1** | What is Data Science? | 🌱 | Beginner | 8 | 1 |
| **M2** | Data Literacy & Spreadsheet Thinking | 📊 | Beginner | 12 | 2 |
| **M3** | SQL for Data Analysis | 🔍 | Beginner → Intermediate | 25 | 3 |
| **M4** | Statistics — The Honest Version | 📈 | Intermediate | 14 | 3 |
| **M5** | Python for Data Science | 🐍 | Intermediate | 14 | 3 |
| **M6** | Data Wrangling with Pandas | 🐼 | Intermediate | 12 | 3 |
| **M7** | Data Visualization & Storytelling | 📉 | Intermediate | 10 | 2 |
| **M8** | Machine Learning Foundations | 🤖 | Intermediate → Advanced | 18 | 5 |
| **CAP** | Capstone — Build & Deploy | 🏆 | Capstone | 5 | 2 |
| | **TOTAL** | | | **118** | **24** |

**24 weeks at a comfortable pace** (one module per 2-3 weeks, evenings + weekends).
**12 weeks if you sprint** (full-time, 6 hours/day).
**Pace yourself** — the curriculum is here forever.

---

## Module-by-module breakdown

### 🌱 Module 1 — What is Data Science? (8 lessons · 1 week)

**Mission:** Build the mental model. No code. No math. Just clear thinking.

1. What is Data? (it's not what you think)
2. What is a Data Scientist?
3. The Data Science Workflow (5 stages)
4. Data Analysis vs Data Science vs ML vs AI — clear up the jargon
5. Tools of the Trade — what you'll learn (and what you won't need)
6. Real Nigerian Data Science Use Cases (banking, fintech, agric, telecom, health, education, government)
7. Ethics — Just Because You Can, Doesn't Mean You Should (NDPR, NDPA, bias, consent)
8. **Project:** Your First Data Audit (no-code, 30 min)

**Outcome:** You can explain to a non-technical friend what data science is, what a data scientist does, and why it matters for Nigeria.

---

### 📊 Module 2 — Data Literacy & Spreadsheet Thinking (12 lessons · 2 weeks)

**Mission:** Master the most-used data tool on Earth — the spreadsheet. Think in rows, columns, tables.

1. Anatomy of a Dataset — Rows, Columns, Cells
2. Data Types — what kind of value is in each cell?
3. CSV — the universal data format
4. **Exercise:** Your first spreadsheet exercise (Google Sheets)
5. The 6 essential spreadsheet formulas (SUM, AVERAGE, IF, VLOOKUP, SUMIFS, COUNTIF)
6. Pivot Tables — magic in 30 seconds
7. Data Cleaning — the 80% job (the 7 common quality issues)
8. Conditional formatting — let colours tell the story
9. Charts — pick the right one (7 chart types + flowchart)
10. **Exercise:** Build a 3-chart dashboard
11. Tidy Data — the format ML and SQL prefer
12. **Project:** Clean & analyse a real Nigerian dataset (NBS-style)

**Outcome:** You can clean any messy dataset, build a pivot table, choose the right chart, and produce a 1-page insights document. **Already employable as a junior data analyst at this point.**

---

### 🔍 Module 3 — SQL for Data Analysis (25 lessons · 3 weeks)

**Mission:** Fluency in SQL — the language of nearly every Nigerian bank, telecom, fintech and government database. Practiced live in QueryPilot.

1. SELECT & FROM — getting columns
2. WHERE — filtering rows
3. ORDER BY & LIMIT — sorting and pagination
4. Aggregates — COUNT, SUM, AVG, MAX, MIN
5. GROUP BY — aggregates per category (= pivot table in SQL)
6. HAVING — filtering after aggregation
7. JOINs — combining tables (the big one!)
8. Subqueries — queries inside queries
9. CTEs — readable subqueries
10. Window functions — analytics without losing rows
11. CASE WHEN — IF-statements inside SQL
12. NULL handling — the silent destroyer
13. INSERT, UPDATE, DELETE — modifying data
14. Dates and times — calendar arithmetic
15. UNION & INTERSECT — combining result sets
16. Indexes — making queries fast
17. Query performance — reading EXPLAIN plans
18. Recursive CTEs — for hierarchies (org charts, trees)
19. JSON in SQL — querying nested data
20. Transactions & ACID
21. Views & materialised views
22. Stored procedures — saved logic on the server
23. Pivoting & unpivoting in SQL
24. SQL anti-patterns — common mistakes to avoid
25. **Project:** Real analytics on real schema (10-query portfolio)

**Outcome:** You can write any business SQL query. You understand performance, security, and design trade-offs. **Junior SQL developer / analyst capability.**

---

### 📈 Module 4 — Statistics — The Honest Version (14 lessons · 3 weeks)

**Mission:** The intuition, not the calculus. Tell signal from noise. Don't get fooled by randomness.

1. Why statistics? The 'is it real or random?' problem
2. Mean, median, mode — three kinds of 'average'
3. Spread — variance, standard deviation, IQR
4. Distributions — how values are spread out (normal, skewed, bimodal)
5. Probability basics — the language of uncertainty
6. Sampling — why we can't measure everyone
7. Confidence intervals — honest uncertainty
8. Hypothesis testing & p-values (with the 3 deadly myths)
9. Correlation — measuring relationships (and the 'correlation ≠ causation' bible)
10. Regression — predicting one variable from another
11. A/B testing — the gold standard for causation
12. Bias & fairness — when statistics goes wrong (NDPR, NDPA, algorithmic fairness)
13. Statistical pitfalls — Simpson's paradox, regression to mean, p-hacking, Goodhart's Law
14. **Project:** Analyse a real survey with honest interpretation

**Outcome:** You speak statistics fluently and HONESTLY. You will never be the analyst who claims '20% improvement' without quoting a confidence interval.

---

### 🐍 Module 5 — Python for Data Science (14 lessons · 3 weeks)

**Mission:** Python from absolute zero. Variables, types, control flow, functions, libraries. Practiced in free Google Colab.

1. Why Python? Installing your environment
2. Variables & basic types
3. Strings — text manipulation
4. Lists & tuples — ordered collections
5. Dictionaries & sets
6. If-else & comparison operators
7. Loops — for & while
8. Functions — reusable code
9. Modules & libraries — importing power
10. File I/O — reading and writing files
11. NumPy — fast number crunching
12. Error handling — try/except
13. List comprehensions — Pythonic shortcuts
14. **Project:** Analyse Nigerian states data in Python

**Outcome:** You can write Python scripts and notebooks. You understand the standard library, NumPy, and basic file handling. Ready for Pandas (Module 6) and Scikit-learn (Module 8).

---

### 🐼 Module 6 — Data Wrangling with Pandas (12 lessons · 3 weeks)

**Mission:** Master Pandas — the data scientist's spreadsheet on steroids. Handle millions of rows, do everything SQL does plus more.

1. Pandas in 60 seconds — your new best friend
2. Loading data — CSV, Excel, SQL, JSON, web
3. Inspecting a DataFrame — first things to do
4. Selecting & filtering — Pandas like SQL
5. GroupBy — Pandas pivot tables
6. Cleaning — missing values & duplicates
7. Creating new columns & transformations
8. Merging DataFrames — Pandas JOIN
9. Reshape — pivot, melt, stack
10. Working with dates & time series (resample, rolling, shift)
11. Pandas vs SQL — same patterns, different syntax (full translation table)
12. **Project:** Pandas analysis of 5,000-row Lagos sales dataset

**Outcome:** You can do anything in Pandas you can do in SQL or Excel, plus much more. **Junior data analyst capability with Python toolchain.**

---

### 📉 Module 7 — Data Visualization & Storytelling (10 lessons · 2 weeks)

**Mission:** Charts that tell stories. Dashboards that drive decisions. Free tools: Matplotlib, Seaborn, Plotly, Streamlit.

1. Why visualisation? The 'show, don't tell' principle (Anscombe's quartet, Minard's Napoleon map)
2. Matplotlib basics — the grandfather of Python plotting
3. Seaborn — beautiful charts in one line
4. Choosing the right chart — the decision guide
5. Plotly — interactive charts for the web
6. Streamlit — turn analysis into a free web app
7. The principles of good charts (Tufte's data-ink ratio, the 8 rules)
8. Dashboards — design for the executive
9. Storytelling with data — 3-act structure (Cole Nussbaumer Knaflic's bible, summarised)
10. **Project:** Build a DEPLOYED Streamlit dashboard (live URL, GitHub repo)

**Outcome:** You build executive-grade charts and deploy them to public URLs. **A portfolio-piece deployed dashboard.**

---

### 🤖 Module 8 — Machine Learning Foundations (18 lessons · 5 weeks)

**Mission:** The longest module, honestly. From "what is ML?" to deployed model. Mirrors Adewale's 12-project portfolio.

1. What ML really is — learning rules from examples
2. The ML workflow — 7 stages
3. Train/test split & cross-validation
4. Linear regression — the gateway model
5. Logistic regression — binary classification baseline
6. Decision trees & random forests — non-linear power
7. Gradient boosting & XGBoost — competition-grade
8. Evaluation metrics — beyond accuracy (precision, recall, F1, AUC, MAE, RMSE, R²)
9. Overfitting & regularisation
10. Feature engineering — the secret sauce
11. Hyperparameter tuning — squeezing performance
12. Unsupervised learning — clustering & PCA
13. Model interpretability — SHAP & feature importance
14. Handling imbalanced classes (SMOTE done right)
15. Deploying a model — Streamlit + Joblib (the full deployment pipeline)
16. ML ethics — what NOT to do (Apple Card, Amazon hiring AI, proxy discrimination)
17. What's next? Deep learning & beyond (NLP, CV, time series, MLOps, RL, GenAI)
18. **Project:** End-to-end ML pipeline (deployed app + GitHub + README)

**Outcome:** You can build, evaluate, explain, and deploy ML models for tabular data. **Junior data scientist / ML engineer capability.**

---

### 🏆 Capstone — Build & Deploy (5 lessons · 2 weeks)

**Mission:** Pick a real problem. Build it. Deploy it. Document it. Share it. This is your portfolio piece.

1. Pick the right capstone problem (10 Nigerian-context ideas)
2. Project planning — the 2-week sprint
3. The portfolio README that gets you hired (Adewale's pattern)
4. Sharing your work — LinkedIn, X, GitHub
5. **The Capstone:** your own 2-week project

**Outcome:** You have **one complete, deployed, public** data-science project on GitHub with a live URL. Recruiter-ready.

---

## How to use the Hub

### Opening the Hub

Three ways:

1. Click the **🎓 Hub** tab in the main tab bar (top of the app).
2. Click the **🎓 0%** chip in the topbar (shows your overall progress).
3. Press the URL anchor `#mode=hub` to deep-link.

### Navigation flow

```
Hub Home (module grid + stats + search)
    └── Module page (lesson list, prereq, summary)
            └── Lesson page (learn / example / try / quiz / exercise / glossary / takeaway)
                    ├── Previous / Next lesson buttons
                    ├── Mark as Done button
                    └── Try in QueryPilot button (when applicable)
```

### Searching

The hub home page has a search bar. Type 2+ characters to find lessons across all 9 modules by title or content. Click any result to jump.

### "Try in QueryPilot" buttons

Selected lessons (mostly in M3 — SQL) have a yellow **▶ Try** banner. Clicking it switches to chat mode and pre-fills the example query, so you can run it instantly. This makes SQL lessons truly hands-on.

---

## Progress tracking & certificate

### Progress storage

Your completed lessons are stored locally in `localStorage` under the key `qp_hub_progress`. Cross-device sync requires manual export/import via the Enterprise → Backup & Restore feature (see [ENTERPRISE.md](ENTERPRISE.md)).

### Marking lessons done

On every lesson page, click **✓ Mark as done** to record completion. Click again to undo.

### Module-level progress

Each module card shows a coloured progress bar. The hub home page shows overall progress at the top.

### The completion certificate

When you reach **80% overall completion**, a **🏆 View Your Certificate** button appears on the hub home page. Click it to:

1. Be prompted for your name (stored locally in `qp_hub_profile`).
2. See a printable, gold-bordered certificate with your name, lesson count, module count, completion %, and date.
3. **Print / Save as PDF** via your browser.
4. **Share on LinkedIn** with one click.

⚠️ **Honest disclosure** (shown on the certificate page): this is a free open-source completion certificate, not an accredited qualification. It demonstrates self-directed learning. For accredited Nigerian DS programmes, see DSN, 3MTT, WorldQuant University, IBM SkillsBuild, Kodecamp.

---

## Lesson types explained

Each lesson is one of these types (shown by a coloured badge):

| Badge | Type | What it contains | Typical time |
|---|---|---|---|
| 🔵 **CONCEPT** | Explanation + example + glossary | Theory, vocabulary, key principles | 7-12 min |
| 🟢 **EXERCISE** | Hands-on task with steps | A practical task in Sheets / Colab / QueryPilot | 15-30 min |
| 🟡 **QUIZ** | Embedded multiple-choice | Self-check with explanation | 2-3 min |
| 🟣 **PROJECT** | Multi-step deliverable | A real project — 30 min to 3 hours | 30 min - 3 hr |
| 🟠 **PLAYGROUND** | (Future) interactive code editor | Run sample Python in-browser | varies |

Most lessons combine multiple types — e.g. a concept lesson with a quiz at the end.

---

## Time estimates & study plans

### Sprint plan (full-time, 6 hrs/day) — **12 weeks**

| Weeks | Modules |
|---|---|
| 1 | M1 + M2 |
| 2-3 | M3 |
| 4 | M4 (start) |
| 5 | M4 (finish) + M5 (start) |
| 6 | M5 (finish) |
| 7-8 | M6 |
| 9 | M7 |
| 10-11 | M8 |
| 12 | Capstone |

### Comfortable plan (evenings + weekends) — **24 weeks**

| Weeks | Modules |
|---|---|
| 1 | M1 |
| 2-3 | M2 |
| 4-6 | M3 |
| 7-9 | M4 |
| 10-12 | M5 |
| 13-15 | M6 |
| 16-17 | M7 |
| 18-22 | M8 |
| 23-24 | Capstone |

### Casual plan (1-2 hours/week) — **12 months**

Pace yourself. The curriculum is free and lives in your browser forever. Better to finish slowly than burn out fast.

---

## What you'll be able to do at the end

After completing all 9 modules + capstone, you can credibly apply for these roles in the Nigerian (and global) market:

- **Junior Data Analyst** (₦200k-₦500k/month in Lagos as of 2026)
- **Junior Data Scientist** (₦400k-₦800k/month)
- **Junior ML Engineer** (₦500k-₦1M/month)
- **Business Intelligence Analyst** (₦300k-₦700k/month)
- **Analytics Engineer**
- **Data Engineer (with additional SQL / Spark study)**

You will be able to:

✅ Read any business data spreadsheet and find insights
✅ Write SQL for any business analytics question
✅ Build Pandas analyses on million-row datasets
✅ Produce executive-grade visualisations and dashboards
✅ Train and evaluate ML models (classification, regression, clustering)
✅ Explain ML predictions with SHAP
✅ Deploy models as Streamlit apps with public URLs
✅ Discuss ethics, bias, and limits of statistics & ML
✅ Communicate findings with the 3-act story structure
✅ Build and share a portfolio of deployed projects

---

## Recommended companion resources

While this curriculum is self-contained, these **free** resources deepen specific areas:

### Books (free / inexpensive)
- *Python for Data Analysis* by Wes McKinney (the creator of Pandas)
- *An Introduction to Statistical Learning* (statlearning.com — free PDF)
- *Storytelling with Data* by Cole Nussbaumer Knaflic
- *The Visual Display of Quantitative Information* by Edward Tufte

### Online courses (free)
- **Kaggle Learn** — kaggle.com/learn (micro-courses, free, with hands-on)
- **fast.ai** — practical deep learning
- **Andrew Ng's Machine Learning Specialization** (Coursera audit = free)
- **Google Data Analytics Certificate** (Coursera, 7-day trial)

### Nigerian programmes (free for selected applicants)
- **3MTT** (3mtt.nitda.gov.ng) — the catalyst for Adewale's transition; cohorts open periodically
- **DSN x Google.org DeepTech_Ready** — applications open via DSN portal
- **IBM SkillsBuild** — skillsbuild.org — free certificates
- **Kodecamp** — kodecamp.dev
- **Sail Innovation Lab** — sail.dev

### YouTube channels
- **HMG Concepts** (youtube.com/@hmgconcepts) — Adewale's own walkthroughs, Nigerian-context
- **StatQuest with Josh Starmer** — best statistics explanations on the internet
- **Krish Naik** — practical ML and DS in plain language
- **3Blue1Brown** — beautiful visual mathematics

---

## Career pathways after completion

```
                 ┌─────────────────────────────────┐
                 │ Curriculum Complete (this Hub) │
                 └──────────────┬──────────────────┘
                                │
        ┌───────────────────────┼───────────────────────┐
        │                       │                       │
   Analyst path           DS / ML path           Engineer path
   ───────────           ─────────────           ──────────────
   • SQL                 • Scikit-learn          • Python + Java
   • Power BI / Tableau  • XGBoost               • Apache Airflow
   • Excel + Sheets      • TensorFlow / PyTorch  • dbt
   • Storytelling        • MLOps                 • Spark
        │                       │                       │
   Jr Data Analyst         Jr Data Scientist        Data Engineer
   BI Analyst              Jr ML Engineer            Analytics Engineer
        │                       │                       │
   Senior Analyst         Sr DS / ML Engineer        Sr Data Engineer
   Analytics Manager      DS Lead / Manager           DataOps Lead
```

### Specialty branches (after 1-2 years)

- **NLP / LLM Engineer** — Hugging Face, transformers, fine-tuning
- **Computer Vision Engineer** — CNNs, YOLO, segmentation
- **Time-series specialist** — Prophet, ARIMA, LSTM for forecasting
- **Recommender Systems** — matrix factorisation, deep recommenders
- **MLOps / Platform Engineer** — Kubernetes, MLflow, monitoring
- **Causal Inference** — A/B testing at scale, observational studies
- **Data Engineering Manager** — leading teams

---

## For instructors

This curriculum is **MIT-licensed** and free for instructional use. You may:

✅ Use it as the syllabus for a school / university / bootcamp course.
✅ Print lesson content for in-classroom distribution.
✅ Adapt lesson content to other languages.
✅ Add it to your institution's learning management system.

**Please retain attribution to Adewale Samson Adeagbo / HMG Concepts.** A link to https://hmgconcepts.pages.dev in your acknowledgements section is sufficient.

### Suggested syllabus mapping

**Secondary school CS elective (12 weeks):** M1 + M2 + M3 (basics 1-10) + capstone (mini)

**University DS introductory course (16 weeks):** M1 + M2 + M3 + M4 + M5

**Bootcamp / 6-month programme:** All 9 modules

**Corporate analyst training (4 weeks):** M2 + M3 + M7

### Bring Adewale in

For institutions wanting **live instructor-led delivery** of this curriculum, guest lectures, or curriculum customisation, contact Adewale Samson Adeagbo directly:

- 💬 WhatsApp: [+234 810 086 6322](https://wa.me/2348100866322)
- ✉️ Email: [buildingmyictcareer@gmail.com](mailto:buildingmyictcareer@gmail.com) or [hismarvellousgrace@gmail.com](mailto:hismarvellousgrace@gmail.com)
- 🌐 HMG Concepts: https://hmgconcepts.pages.dev

---

## Honest disclosure

This is the curriculum I (Adewale) wish had existed when I was transitioning from full-time secondary teaching into data science in 2024-2025. It reflects:

- 15+ years observing where understanding actually breaks down in Nigerian classrooms.
- 12 deployed ML projects across 7 industries — and the mistakes I made on each.
- Three current advanced learning programmes I'm enrolled in (DSN × 3MTT × Google.org DeepTech_Ready, WorldQuant University ADS Lab, Kodecamp Cohort 6).
- My ongoing work as IBM SkillsBuild Hub Ambassador (Cohort 4, Apr-Jun 2026).

**The curriculum is not perfect.** Topics I'd like to add over time:

- Deep Learning module (full PyTorch fundamentals)
- NLP module (Hugging Face, sentiment analysis, fine-tuning)
- MLOps module (production pipelines, monitoring, A/B testing models)
- Causal Inference module
- Big Data tooling (Spark, dbt)

These will arrive in future versions. Contributions are welcome via pull request — see [CONTRIBUTING.md](CONTRIBUTING.md).

**If this curriculum helps you get your first DS job, please tell me.** Message me on WhatsApp; your story may inspire the next learner.

---

> *"I did not wait to become a developer. I used clear thinking and AI leverage to build the tools my students needed. That working method is the real skill — not any specific technology."*
> — Adewale Samson Adeagbo

> *QueryPilot v9 Learning Hub — built deliberately by Adewale Samson Adeagbo, Lagos, Nigeria. Released under MIT. Use it. Improve it. Pass it on.*