📊 Foundations of Data Science – Spring 2026

Official course repository for Foundations of Data Science
University of Tehran — Spring 2026
Instructors: Prof. Yadollah Yaghoobzadeh , Dr. S.Fatemeh Razavi

Chief TAs: Mostafa Kermani Nia , Mohammad Amanlou

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📘 Course Essentials

Document	File
Course Description & Staff	Description.pdf
Assignment Guidelines	Guidelines.pdf
Detailed Calendar (Google Sheets)	Open Calendar
YouTube Channel	@BahrakCourses
Final Project Presentations	Presentations
License	LICENSE

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🧭 Quick Navigation

• 📊 Foundations of Data Science – Spring 2026
  • 📘 Course Essentials
  • 🧭 Quick Navigation
  • 📅 Weekly Schedule
  • 🎓 Lecture Slides
  • 💻 Computer Assignments
  • 🏆 Final Project
  • 📂 More Resources
    • 📊 Power BI
    • 📐 Sampling
    • 🧪 CA0 Helpers
    • 🕸️ Web Scraping
    • 🤖 MLOps
    • 🌐 Web Scraping (zip)
  • 🐍 Python for Data Science Notebooks
  • 📜 License

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📅 Weekly Schedule

Week	Topic (Slides)	Assignments & Project	Extra Resources
1	Introduction – Data Science Lifecycle Lecture 01. Data Science Lifecycle.pdf	–	Python for DS Notebooks
2	Statistical Charts Lecture 02. Statistical Charts.pdf	–	–
3	Review of Probability Theory Lecture 03. A Review of Probability Theory.pdf	CA0 released	More Resources/CA0/
4	Foundations for Inference Lecture 04. Foundations for Inference.pdf Visualization Design Principles Lecture 05. Visualization Design Principles.pdf	–	–
5	Preattentive Attributes Lecture 06. Preattentive attributes.pdf Dashboards & Storytelling Lecture 07. Dashboards and Storytelling.pdf	CA1 released	More Resources/PowerBI/ More Resources/Sampling/
6	SQL – Part 1 & 2 Lecture 09. SQL-1.pdf Lecture 09. SQL-2.pdf	CA2 released	–
7	Big Data Lecture 10. Big Data.pdf	Final Project Phase 1	–
8	Linear Regression Lecture 08. Linear Regression.pdf	–	–
9	Modeling, SLR, Loss Lecture 11. Modeling, SLR, Loss.pptx	CA3 released	–
10	Gradient Descent Lecture 12. Gradient descent.pptx Logistic Regression (intro) Lecture 13. Logistic regression.pptx	–	–
11	Regression (Advanced) Lecture 14. Regression.pdf SVM & KNN Lecture 14. SVM_KNN.pdf	CA4 released Final Project Phase 2	–
12	MLOps Lecture 15. MLOps.pptx Neural Networks Lecture 16. NNs.pptx	–	More Resources/MLOps-20260603.zip
13	CNNs & RNNs Lecture 17. CNNs RNNs.pptx LLMs Lecture 18. LLMs.pptx	–	–
14	RAG & Agents Lecture 19. RAG, Agents.pptx Unsupervised Learning Lecture 20. Dimesionality reduction, Kmeans.pptx	CA5 (optional)	–
15	Interpretability Lecture 21. Interpretablity.pptx Ethics Lecture 22. Ethics.pptx	Final Project Phase 3	–
16	Final Exam Final Exam Questions	–	Sample Questions
17	Final Project Presentations	–	–
18	Final Project Presentations	–	–

🗓️ Deadlines here are indicative. The real timeline evolved organically during the semester with several extensions.

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🎓 Lecture Slides

All slides are in the lectures/ folder:

#	File
01	Lecture 01. Data Science Lifecycle.pdf
02	Lecture 02. Statistical Charts.pdf
03	Lecture 03. A Review of Probability Theory.pdf
04	Lecture 04. Foundations for Inference.pdf
05	Lecture 05. Visualization Design Principles.pdf
06	Lecture 06. Preattentive attributes.pdf
07	Lecture 07. Dashboards and Storytelling.pdf
08	Lecture 08. Linear Regression.pdf
09	Lecture 09. SQL-1.pdf & Lecture 09. SQL-2.pdf
10	Lecture 10. Big Data.pdf
11	Lecture 11. Modeling, SLR, Loss.pptx
12	Lecture 12. Gradient descent.pptx
13	Lecture 13. Logistic regression.pptx
14	Lecture 14. Regression.pdf & Lecture 14. SVM_KNN.pdf
15	Lecture 15. MLOps.pptx
16	Lecture 16. NNs.pptx
17	Lecture 17. CNNs RNNs.pptx
18	Lecture 18. LLMs.pptx
19	Lecture 19. RAG, Agents.pptx
20	Lecture 20. Dimesionality reduction, Kmeans.pptx
21	Lecture 21. Interpretablity.pptx
22	Lecture 22. Ethics.pptx

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💻 Computer Assignments

CA	Folder	Contents
CA0	Assignments/CA0/	CA0.pdf, CA0.ipynb Datasets: 2016-general-election-trump-vs-clinton.csv, drug_safety.csv
CA1	Assignments/CA1/	CA1.pdf, codal_news_dashboard.pbix Data: khodro_final_daily.csv, Coordinate.csv, Region.csv, TB_Burden_Country.csv, column_descriptions.xlsx
CA2	Assignments/CA2/	CA2.pdf, Ashpaz.py, zomato.csv, historical_data.json JARs: jars/ (Kafka, Spark, etc.)
CA3	Assignments/CA3/	CA3.pdf + three task dataset zips
CA4	Assignments/CA4/	DS-CA4.pdf, CA4.txt, california_housing.csv, assistments2017.csv
CA5	Assignments/CA5/	CA5.pdf (optional final assignment)

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🏆 Final Project

Phase	Description	File
Phase 1	Topic proposal & data collection	Final Project/P1.pdf
Phase 2	Modeling & final report	Final Project/P2.pdf
Phase 3	Final deliverables & presentation prep	Final Project/P3.pdf
Presentations	Final delivery	Final Project/Presentations/ (placeholder)

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📂 More Resources

📊 Power BI

More Resources/PowerBI/
Sample_Dashbord.pbix, V2.mkv – sample dashboard and tutorial.

📐 Sampling

More Resources/Sampling/
sampling.pptx, sampling.mp4 – extra material for CA1.

🧪 CA0 Helpers

More Resources/CA0/
Jupyter notebooks on CLT, confidence intervals, t‑tests, power analysis, and a sample solution.

🕸️ Web Scraping

More Resources/WebScraping/
BS4.ipynb, Selenium.ipynb, clothes.zip, Data-FinalProject.mp4.

🤖 MLOps

More Resources/MLOps-20260603.zip
Workshop materials (compressed).

🌐 Web Scraping (zip)

More Resources/Web Scraping-20260603.zip
Additional packed scraping content.

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🐍 Python for Data Science Notebooks

Progressive Jupyter notebooks covering Python essentials:

Python for Data Science Notebooks/
├── 1.Data Types.ipynb
├── 2.Statements-Files.ipynb
├── 3.Functions.ipynb
├── 4.OOP.ipynb
├── 5.Exceptions.ipynb
├── 6.NumPy.ipynb
├── 7.Pandas.ipynb
└── 8.Matplotlib.ipynb

Start here if you need a Python refresher before diving into the assignments.

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📜 License

This repository is shared under the LICENSE file.
Please respect academic integrity when reusing materials.

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Made with ❤️ for students of Spring 2026