Spaces:
Running
Running
| title: Introduction to Data Science | |
| emoji: 📚 | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: static | |
| pinned: true | |
| short_description: Intro to Data Science · Reichman University · 2026 | |
| thumbnail: >- | |
| https://cdn-uploads.huggingface.co/production/uploads/6a9ec4fa322229fd8f3c2b3c/NA65Xe-I4wo4z3wonvWG7.png | |
| <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-English-2da44e"> | |
| <img alt="Packages" src="https://img.shields.io/badge/Packages-python%20%C2%B7%20scikit--learn%20%C2%B7%20torch%20%C2%B7%20pandas%20%C2%B7%20%F0%9F%A4%97-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> |