--- 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 ---

📚📉 Introduction to Data Science 📈📚

Reichman University · Adelson School of Entrepreneurship (B.A.), 2024-2026

Course 2686 Credits 4 Language English Packages

--- ## 👋 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%** |
📖 Full unit-by-unit syllabus
| 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.*
## 🎯 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: - What it was used for: - Example prompts: ``` ## 🤖 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
🌐 Online tools & documentation
- [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
✨ Generative AI tools
[Groq](https://groq.com) · [Claude](https://claude.ai) · [ChatGPT](https://chatgpt.com) · [Perplexity](https://www.perplexity.ai) · [Gemini](https://gemini.google.com)
📘 Books
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*
🎓 Online courses
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
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Introduction to Data Science · Course 2686 · Reichman University, Adelson School of Entrepreneurship