Veri Bilimi, ML, DL, AI ve Ekonometri ile İlgili Yararlı Kaynaklar

Not 1: Bu liste belirli aralıklarla güncellenmektedir (Son Güncelleme: 30.08.2024).

Not 2: Türkçe kaynaklar farklı renkte verilmiştir.

Not 3: Çalışmayan linklerle ilgili lütfen iletişime geçiniz.

Veri Bilimi (Data Science)

1. An Introduction to R and Python For Data Analysis: A Side By Side Approach - Taylor R. Brown (URL)

2. A First Course in Quantitative Economics with Python - Thomas J. Sargent and John Stachurski (URL)

3. An Introduction to R - W. N. Venables, D. M. Smith & the R Core Team (PDF)

4. Applied Python - Bernd Klein (PDF)

5. Automate the Boring Stuff with Python - Al Sweigart (URL)

6. Big Data - Daniel Björkegren (URL)

7. Course Materials for Advanced Data Analytics in Economics - Nick Hagerty (GitHub)

8. Data Analysis Numpy, Matplotlib and Pandas - Bernd Klein (PDF)

9. Data Science for Economics and Finance - Sergio Consoli, Diego Reforgiato Recupero & Michaela Saisana (PDF)

10. Data Science for Economists (GitHub)

11. Data Structures and Algorithms in Python (freeCodeCamp.org - YouTube)

12. Data Structures and Algorithmswith Object-Oriented Design Patterns in Python - Bruno R. Preiss (URL)

13. Doing Data Science in R: An Introduction for Social Scientists - Mark Andrews (URL)

14. Free Python for Data Science (Python Coding - YouTube)

15. Forecasting: Principles and Practice - Rob J Hyndman & George Athanasopoulos (URL)

16. Foundations of Data Science - Avrim Blum, John Hopcroft, and Ravindran Kannan (PDF)

17. Hands-On Programming with R - Grolemund, G. (PDF)

18. Harvard CS50’s Introduction to Programming with Python (freeCodeCamp.org - YouTube)

19. How To Code in Python 3 - Lisa Tagliaferri (PDF)

20. Intro to Probability for Data Science - Stanley H. Chan (URL)

21. Introduction to Economic Modeling and Data Science (URL)

22. Introduction to Python for Econometrics, Statistics and Data Analysis - Kevin Sheppard (PDF)

23. Introduction to Statistical Data Analysis with R -Matthias Kohl (PDF)

24. Learn Python, Break Python: A Beginner's Guide to Programming - Scott Grant (URL)

25. Learn Python by Thinking in Types (freeCodeCamp.org - YouTube)

26. Learning R - Richard Cotton (PDF)

27. Modern Data Science with R - Benjamin S. Baumer, Daniel T. Kaplan & Nicholas J. Horton (URL)

28. Pandas & Python for Data Analysis by Example – Full Course for Beginners (freeCodeCamp.org - YouTube)

29. Python Data Science Handbook - Jake VanderPlas (URL)

30. Python for Finance - Yves Hilpisch (PDF)

31. Python Notes for Professionals Book (goalkicker)

32. Python Programming for Economics and Finance - Thomas J. Sargent and John Stachurski (URL)

33. Python Tutorial - Bernd Klein (PDF)

34. Python for Economists - Ewen Gallic (PDF)

35. R for Everyone - Jared P. Lander (PDF)

36. R Notes for Professionals Book (goalkicker)

37. R Programming for Data Science - Roger D. Peng (URL)

38. Regression Models for Data Science in R - Brian Caffo (PDF)

39. The Big Book of Small Python Projects: 81 Easy Practice Programs - Sweigart, A. (PDF)

40. The Book of R: A First Course in Programming and Statistics - Davies, T. M. (PDF)

41. Think Python - Allen B. Downey (URL)

42. Anlaşılır Ekonomi Youtube Kanalı (YouTube)

43. Uygulamalarla Temel Seviye Python - Enes Açıkgözoğlu & Ziya Dirlik (PDF)

44. Veri Bilimi için R - Garrett Grolemund & Hadley Wickham (URL)

45. Veri Okuryazarlığı - Mustafa Vahit Keskin (Geleceği Yazanlar)

46. YazBel Python Belgeleri - Fırat Özgül (PDF)

47. Yeni Başlayanlar İçin Türkçe Python Dersleri (Arin Yazilim - YouTube)

Makine Öğrenmesi (Machine Learning)

1. An Introduction to Statistical Learning with Applications in Python - James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (PDF)

2. An Introduction to Statistical Learning with Applications in R - Gareth, J., Daniela, W., Trevor, H., & Robert, T. (PDF)

3. Best-of Machine Learning with Python (GitHub)

4. Classic Machine Learning Algorithms - Johann Faouzi, Olivier Colliot (PDF)

5. Complete Machine Learning (Krish Naik - YouTube)

6. Financial Machine Learning - Bryan T. Kelly & Dacheng Xiu (PDF)

7. Financial Signal Processing and Machine Learning - Akansu, A. N., Kulkarni, S. R., & Malioutov, D. (GitHub)

8. Foundations of Machine learning - Lecture Notes (URL)

9. Hands-On Machine Learning with R - Bradley Boehmke & Brandon Greenwell (URL)

10. Intro to Machine Learning for Economics - José Barrales-Ruiz (URL)

11. Large Scale Machine Learning with Python - Sjardin, B., Massaron, L., & Boschetti, A. (GitHub)

12. Machine Learning and Big Data - Melissa Dell & Matthew Harding (URL)

13. Machine Learning (CS229) - Andrew Ng & Tengyu Ma (PDF)

14. Machine Learning & Causal Inference: A Short Course - Stanford Graduate School of Business (YouTube)

15. Machine Learning and Data Science - Daniel D. Gutierrez (GitHub)

16. Machine Learning and Econometrics - Susan Athey & Guido Imbens (URL)

17. Machine Learning for Econometricians - Emmanuel Flachaire & Ewen Gallic (URL)

18. Machine Learning Foundations - Google for Developers (YouTube)

19. Machine Learning Foundations Course – Regression Analysis (freeCodeCamp.org - YouTube)

20. Machine Learning: The Basics - Alexander Jung (PDF)

21. Machine Learning Tutorial in Python (Edureka - YouTube)

22. Machine Learning Tutorial Python | Machine Learning For Beginners (codebasics - YouTube)

23. Machine Learning Using R - Ramasubramanian, K., & Singh, A. (GitHub)

24. Machine Learning with Python and Scikit-Learn (freeCodeCamp.org - YouTube)

25. Machine Learning with Python Tutorial - Bernd Klein (PDF)

26. Master Machine Learning Algorithms - Jason Brownlee (GitHub)

27. Mathematics for Machine Learning - Deisenroth, A. Aldo Faisal, & Cheng Soon Ong (URL)

28. Open Machine Learning Course (URL)

29. Pattern Recognition and Machine Learning - Christopher M. Bishop (PDF)

30. Practical Machine Learning - Qingqing Huang, Mu Li, Alex Smola (URL)

31. Python Machine Learning - Raschka, S. (GitHub)

32. Real-World Machine Learning - Brink, H., Richards, J., & Fetherolf, M. (GitHub)

33. Supervised Machine Learning for Text Analysis in R - Emil Hvitfeldt & Julia Silge (URL)

34. The Elements of Statistical Learning: Data Mining, Inference and Prediction - Trevor Hastie, Robert Tibshirani & Jerome Friedman (PDF)

35. The Impact of Machine Learning on Economics - Susan Athey (PDF)

    
    

Derin Öğrenme (Deep Learning)

1. Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville & Francis Bach (URL)

2. Introduction to Deep Learning (URL)

3. Derin Öğrenmeye Dalış - Aston Zhang, Zachary C. Lipton, Mu Li, & Alexander J. Smola (PDF)

Yapay Zeka (Artificial Intelligence)

1. Artificial Intelligence and Machine Learning in Financial Services - CRS Report (PDF)

2. Artificial Intelligence in Central Banking - Douglas Araujo, Sebastian Doerr, Leonardo Gambacorta & Bruno Tissot (PDF)

3. Digital Economics and the Economics of Artificial Intelligence - Beraja, Farronato, Goldfarb, Tucker (URL)

4. Gen-AI: Artificial Intelligence and the Future of Work -  Cazzaniga, Mauro et al (URL)

5. Intelligent Financial System: How AI is Transforming Finance - Iñaki Aldasoro et al (URL)

6. Learn to Code using AI - ChatGPT Programming Tutorial - (freeCodeCamp.org - YouTube)

7. Sources of Artificial Intelligence - Thomas J. Sargent (PDF)

8. Veri Bilimi ve Yapay Zekaya Giriş - Mustafa Vahit Keskin (Geleceği Yazanlar)

Ekonometri (Econometrics)

1. Applied Panel Data Analysis Using Stata - Josef Brüderl & Volker Ludwig (PDF)

2. Applied Time Series Analysis - Terence C. Mills (PDF)

3. Basic Econometrics - Riccardo (Jack) Lucchetti (PDF)

4. Computational Econometrics - Kuan-Pin Lin (PDF)

5. Econometric Analysis by Examples - Kuan-Pin Lin (URL)

6. Econometrics - Bruce E. Hansen (PDF)

7. Econometrics - Michael Creel (GitHub)

8. Econometrics (Undergraduate) - William Evans (URL)

9. Econometrics with Python (GitHub)

10. Forecasting in Economics, Business, Finance and Beyond - Francis X. Diebold (PDF)

11. Introduction to Econometrics with R - Christoph Hanck, Martin Arnold, Alexander Gerber, and Martin Schmelzer (PDF)

12. Introduction to Time Series and Forecasting - Peter J. Brockwell & Richard A. Davis (PDF)

13. Introductory Econometrics for Finance: Python Guide to Accompany - Chris Brooks (URL)

14. Introductory Econometrics for Finance: R Guide to Accompany - Chris Brooks (URL)

15. Introductory Econometrics for Finance: Stata Guide to Accompany - Chris Brooks (URL)

16. Introductory Econometrics for Finance: EViews Guide to Accompany - Chris Brooks (URL)

17. Machine Learning for Econometricians - Emmanuel Flachaire & Ewen Gallic (URL)

18. MFE Financial Econometrics Course - Kevin Sheppard (URL)

19. Python for Econometrics in Economics (URL)

20. R Guide to Accompany Introductory Econometrics for Finance - Robert Wichman & Chris Brooks (URL)

21. Seattle University Econometrics Course Slides (URL)

22. Stata Guide to Accompany Introductory Econometrics for Finance - Lisa Schopohl, Robert Wichman & Chris Brooks (URL)

23. Techniques of Empirical Macroeconomics - Òscar Jordà & Karel Mertens (URL)

24. Time Series Analysis - James D. Hamilton (PDF)

25. Time-Series Econometrics A Concise Course - Francis X. Diebold (PDF)

26. Time Series for Macroeconomics and Finance - John H. Cochrane (PDF)

27. Using Julia for Introductory Econometrics - Florian Heiss & Daniel Brunner (PDF)

28. Using Python for Introductory Econometrics - Florian Heiss & Daniel Brunner (PDF)

29. Using R for Introductory Econometrics - Florian Heiss (PDF)

30. Ekonometrinin Gelişimi: İktisadın Bilim Olma Çabası - Ercan Uygur (PDF)

31. Ekonometriye Yeni Başlayanlar için Kısa Bir R Klavuzu - Mahmood Arai (GitHub)

32. Eviews ile Uygulamalı Ekonometri - Mehmet Songur (EkonMedya - YouTube)

33. Excel ile Temel Ekonometri - Mehmet Songur (EkonMedya - YouTube)

34. Eviews ile Zaman Serileri Analizi - Mehmet Songur (EkonMedya - YouTube)

İktisat (Economics)

1. Reading List for Monetary Economics Ph. D. Class - John H. Cochrane and Tom Sargent (URL)

   
   

Cheatsheet

1. ChatGPT Cheat Sheet for Data Science (URL)

2. Matplotlib - (URL)

3. Stata Cheat Sheet (URL)

4. Stanford Üniversitesi - CS 230 - Derin Öğrenme (GitHub)

5. Stanford Üniversitesi - CS 229 - Makine Öğrenmesi (GitHub)

6. Stanford Üniversitesi - CS 221 - Yapay Zeka (GitHub)