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README.md

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-title: README
-emoji: 📉
-colorFrom: red
+title: Introduction to Data Science
+emoji: 📚
+colorFrom: blue
 colorTo: purple
 sdk: static
 pinned: false
+short_description: Intro to Data Science · Reichman University · 2026
+thumbnail: >-
+  https://cdn-uploads.huggingface.co/production/uploads/6a9ec4fa322229fd8f3c2b3c/NA65Xe-I4wo4z3wonvWG7.png
 ---
 
-Edit this `README.md` markdown file to author your organization card.
+<div align="center">
+
+<h1>📊 Introduction to Data Science</h1>
+
+<p><b>Reichman University · Adelson School of Entrepreneurship (B.A.), 2024-2026</b></p>
+
+<p>
+  <img alt="Course 2686" src="https://img.shields.io/badge/course-2686-1f6feb">
+  <img alt="Credits 4" src="https://img.shields.io/badge/credits-4-8250df">
+  <img alt="Language English" src="https://img.shields.io/badge/taught%20in-Hebrew-2da44e">
+  <img alt="Python" src="https://img.shields.io/badge/python-scikit--learn%20·%20pandas%20·%20🤗-orange">
+</p>
+
+</div>
+
+---
+
+## 👋 About this organization
+
+This is the home of our class on Hugging Face. Students publish the **datasets, models, and Spaces** they build during the semester here, from the first EDA notebook to the final project demo.
+
+The course gives you practical data science skills for business, economics, and entrepreneurship: framing problems, preparing data, building and evaluating models, and communicating insights that drive decisions. You'll work hands-on with industry-standard tools. No prior experience is required, and prior experience will only help.
+
+## 🧭 The course in one line
+
+```
+Frame the problem → Get data → Explore → Model → Evaluate → Tell the story
+```
+
+| Phase | Units | Highlights | Graded work |
+|---|:---:|---|---|
+| 🌱 **Foundations** | 1–2 | DS lifecycle, ethics and bias, data cleaning, EDA, visual storytelling | EDA assignment · individual · **10%** |
+| ⚙️ **Core ML** | 3–6 | Classification, regression, clustering, feature engineering, cross-validation, explainability | Classification & Regression · individual · **20%** |
+| 🎯 **Applied ML** | 7 | Recommendation systems, cold start, embeddings, similarity | Recommendation · pair · **20%** |
+| 🧠 **Modern AI** | 8–11 | Deep learning, NLP, computer vision, multimodal, pre-trained models | Final project kicks off (NLP / CV / Audio) |
+| 🏁 **Wrap-up** | 12–14 | AutoML and AI agents, ethics reflection, presentations | Final Project & Oral Exam · pair, graded individually · **50%** |
+
+<details>
+<summary><b>📖 Full unit-by-unit syllabus</b></summary>
+
+<br>
+
+| Unit | Topic | What we cover |
+|:---:|---|---|
+| 1 | **Intro to DS & Lifecycle** | Statistics vs. ML vs. Deep Learning · problem framing · DS roles and industry use cases · responsible DS: ethics, bias, reproducibility |
+| 2 | **Data, EDA & Visualization** | Data sources (structured, unstructured, APIs, scraping) · cleaning: missing values, outliers, normalization · descriptive statistics and correlations · effective charts and dashboards |
+| 3 | **Classification** | Supervised learning · logistic regression, decision trees, k-NN · accuracy, precision/recall, F1, ROC/AUC · fraud, spam, churn |
+| 4 | **Regression** | Linear, Ridge and Lasso · residual diagnostics, over/underfitting · MSE, RMSE, MAE, R² · price prediction, forecasting |
+| 5 | **Clustering** | Unsupervised learning · k-means, hierarchical, DBSCAN · choosing k (Elbow, Silhouette) · customer segmentation |
+| 6 | **Machine Learning** *(guest lecture)* | Feature engineering and importance · train/test splits, cross-validation, data leakage · grid search · explainability |
+| 7 | **Recommendation Systems** | RecSys algorithms · cold start · embeddings and similarity · search engines |
+| 8 | **Deep Learning** | Neural networks vs. traditional ML · MLPs · embeddings · pre-trained models · tabular DL |
+| 9 | **Working with Text (NLP)** | Tokens, vectors, embeddings · TF-IDF vs. transformers · prompting vs. fine-tuning · ROUGE · handling hallucinations |
+| 10 | **Working with Visual Data** | How computers "see" · convolution · image classification, object detection, augmentation · pre-trained vision models |
+| 11 | **Multimodal Data** *(guest lecture)* | Text + image + audio · cloning and deepfakes · synthetic data and augmentation |
+| 12 | **Course Summary** | Recap · ethics: bias, privacy, responsible AI · AI agents, AutoML / AutoDS · retrospective |
+| 13–14 | **Presentations** | Final project demos and oral defense |
+
+*Some adjustments may occur. Guest-lecture dates are announced during the course, and advanced topics may adapt to the class's progress.*
+
+</details>
+
+## 🎯 What you'll be able to do
+
+- 🛠️ Build real AI applications and use **pre-trained models** on text, images, and more
+- 📓 Produce clean, well-documented notebooks and **reproducible pipelines**
+- 🔧 Engineer features, tune and evaluate models, and **interpret** results and metrics
+- 📐 Apply core models: classification, regression, clustering, and beyond
+- 🗣️ Communicate findings, visually and verbally, to stakeholders
+- 🔁 Run an **end-to-end workflow**: framing → data → EDA → modeling → evaluation → storytelling
+
+## 📦 What you'll find here
+
+| | |
+|---|---|
+| 🗂️ **Datasets** | Data used in assignments, demos, and projects |
+| 🤖 **Models** | Models trained or fine-tuned during the course |
+| 🚀 **Spaces** | Interactive demos and final-project apps |
+
+```python
+from datasets import load_dataset
+
+# replace ORG_NAME with this organization's name
+ds = load_dataset("ORG_NAME/dataset-name")
+```
+
+## 🤝 Publishing here: house rules
+
+A good repo is one a stranger can understand and reproduce in five minutes.
+
+1. **Name it clearly**, in lowercase with hyphens (for example `eda-airbnb-listings` or `churn-classifier`).
+2. **Write the card.** Every dataset and model needs a README covering what it is, where the data came from, its license, how it was trained or evaluated, and its known limitations and biases.
+3. **Make it reproducible.** Link the notebook or code, pin library versions, and fix random seeds.
+4. **Respect data and people.** No private data or personal information, and no data you don't have the right to share. Credit your sources and keep their licenses.
+5. **Disclose your AI use** (see the policy below). Add this block to your card:
+
+```markdown
+## AI usage disclosure
+- Tool(s) used: <e.g. Claude, ChatGPT, Gemini>
+- What it was used for: <e.g. debugging, explaining a metric, drafting text>
+- Example prompts: <the key prompts you used>
+```
+
+## 🤖 AI policy
+
+Free use of generative AI is welcome, in line with the university's policy. When you use GAI tools in an assignment or exam, you **must report it within your submission**, stating how you used them: the type of tool, or the content of the prompts. Use that violates the university's guidelines may be treated as a disciplinary offense.
+
+## 👥 Teaching team
+
+| Role | Name |
+|---|---|
+| Lecturer | Mr. Sahar Millis |
+
+**Office hours:** the lecturer is available before and after class, and online during the week on request. The tutor is available online during the week on request.
+
+Course materials, lesson recordings, and submissions live on **Moodle**. Attendance follows the Entrepreneurship School's guidelines.
+
+## 📚 Resources
+
+<details>
+<summary><b>🌐 Online tools & documentation</b></summary>
+
+<br>
+
+- [Python.org](https://www.python.org)
+- [scikit-learn](https://scikit-learn.org)
+- [pandas](https://pandas.pydata.org)
+- [Kaggle](https://www.kaggle.com): datasets and Learn
+- [Hugging Face](https://huggingface.co): datasets, models, Spaces
+- [GeeksforGeeks](https://www.geeksforgeeks.org): AI, DS, EDA, ML, DL, NLP, CV
+
+</details>
+
+<details>
+<summary><b>✨ Generative AI tools</b></summary>
+
+<br>
+
+[Groq](https://groq.com) · [Claude](https://claude.ai) · [ChatGPT](https://chatgpt.com) · [Perplexity](https://www.perplexity.ai) · [Gemini](https://gemini.google.com)
+
+</details>
+
+<details>
+<summary><b>📘 Books</b></summary>
+
+<br>
+
+1. Cole Nussbaumer Knaflic (2015), *Storytelling with Data*
+2. Kieran Healy (2018), *Data Visualization: A Practical Introduction*
+3. Andriy Burkov (2019), *The Hundred-Page Machine Learning Book*
+4. Ian Goodfellow, Yoshua Bengio, Aaron Courville (2015), *Deep Learning*
+5. Tiffany Timbers, Trevor Campbell, Melissa Lee (2024), *A First Introduction*
+6. Marek Gagolewski (2022), *Minimalist Data Wrangling with Python*
+7. Jeffrey S. Saltz, Jeffrey M. Stanton (2024), *An Introduction to Data Science*
+8. Shaun Ault, Soohyun Liao, Larry Musolino (2025), *Principles of Data Science*
+
+</details>
+
+<details>
+<summary><b>🎓 Online courses</b></summary>
+
+<br>
+
+1. IBM Technology: AI Fundamentals
+2. HarvardX: Data Science: Visualization
+3. HarvardX: Data Science: Machine Learning
+4. HarvardX: Machine Learning and AI with Python
+5. HarvardX: Introduction to Data Science with Python
+6. HarvardX: CS50's Introduction to Artificial Intelligence with Python
+7. MIT (Fall 2016): Introduction to Computational Thinking and Data Science
+
+</details>
+
+---
+
+<div align="center">
+<sub>Introduction to Data Science · Course 2686 · Reichman University, Adelson School of Entrepreneurship</sub>
+</div>