Sheida's study: honor is related to moral clarity and especially the honor integrity component rather than the honor status component
Think of the next steps: why would this be?
11 Temmuz 2019 Perşembe
Background readings on shame
Sznycer, D., Tooby, J., Cosmides, L., Porat, R., Shalvi, S., & Halperin, E. (2016). Shame closely tracks the threat of devaluation by others, even across cultures. Proceedings of the National Academy of Sciences, 113(10), 2625-2630.
Sznycer, D., Takemura, K., Delton, A. W., Sato, K., Robertson, T., Cosmides, L., & Tooby, J. (2012). Cross-cultural differences and similarities in proneness to shame: An adaptationist and ecological approach. Evolutionary Psychology, 10(2), 147470491201000213.
Sznycer, D., Xygalatas, D., Agey, E., Alami, S., An, X. F., Ananyeva, K. I., ... & Fukushima, S. (2018). Cross-cultural invariances in the architecture of shame. Proceedings of the National Academy of Sciences, 115(39), 9702-9707.
Sznycer, D., Al-Shawaf, L., Bereby-Meyer, Y., Curry, O. S., De Smet, D., Ermer, E., ... & McClung, J. (2017). Cross-cultural regularities in the cognitive architecture of pride. Proceedings of the National Academy of Sciences, 114(8), 1874-1879.
Robertson, T. E., Sznycer, D., Delton, A. W., Tooby, J., & Cosmides, L. (2018). The true trigger of shame: Social devaluation is sufficient, wrongdoing is unnecessary. Evolution and Human Behavior, 39(5), 566-573.
Breugelmans, S. M., & Poortinga, Y. H. (2006). Emotion without a word: Shame and guilt among Rarámuri Indians and rural Javanese. Journal of Personality and Social Psychology, 91(6), 1111.
Goetz, J. L., & Keltner, D. (2007). Shifting meanings of self-conscious emotions across cultures. The self-conscious emotions: Theory and research. New York, NY: Guilford.
Fessler, D. (2004). Shame in two cultures: Implications for evolutionary approaches. Journal of Cognition and Culture, 4(2), 207-262.
Sznycer, D., Cosmides, L., & Tooby, J. (2017). Adaptationism carves emotions at their functional joints. Psychological Inquiry, 28(1), 56-62.
Sznycer, D., Takemura, K., Delton, A. W., Sato, K., Robertson, T., Cosmides, L., & Tooby, J. (2012). Cross-cultural differences and similarities in proneness to shame: An adaptationist and ecological approach. Evolutionary Psychology, 10(2), 147470491201000213.
Sznycer, D., Xygalatas, D., Agey, E., Alami, S., An, X. F., Ananyeva, K. I., ... & Fukushima, S. (2018). Cross-cultural invariances in the architecture of shame. Proceedings of the National Academy of Sciences, 115(39), 9702-9707.
Sznycer, D., Al-Shawaf, L., Bereby-Meyer, Y., Curry, O. S., De Smet, D., Ermer, E., ... & McClung, J. (2017). Cross-cultural regularities in the cognitive architecture of pride. Proceedings of the National Academy of Sciences, 114(8), 1874-1879.
Robertson, T. E., Sznycer, D., Delton, A. W., Tooby, J., & Cosmides, L. (2018). The true trigger of shame: Social devaluation is sufficient, wrongdoing is unnecessary. Evolution and Human Behavior, 39(5), 566-573.
Breugelmans, S. M., & Poortinga, Y. H. (2006). Emotion without a word: Shame and guilt among Rarámuri Indians and rural Javanese. Journal of Personality and Social Psychology, 91(6), 1111.
Goetz, J. L., & Keltner, D. (2007). Shifting meanings of self-conscious emotions across cultures. The self-conscious emotions: Theory and research. New York, NY: Guilford.
Fessler, D. (2004). Shame in two cultures: Implications for evolutionary approaches. Journal of Cognition and Culture, 4(2), 207-262.
Sznycer, D., Cosmides, L., & Tooby, J. (2017). Adaptationism carves emotions at their functional joints. Psychological Inquiry, 28(1), 56-62.
10 Temmuz 2019 Çarşamba
R and RStudio Learning Resources
https://bookdown.org/ndphillips/YaRrr/who-is-this-book-for.html
Daniel Nettle (July, 2019) Modelling and visualizing data using R: A practical introduction
Daniel Nettle (July, 2019) Modelling and visualizing data using R: A practical introduction
5 Temmuz 2019 Cuma
Erle Monfils' Recommended Coding Websites
Hi Pelin!
So, as promised a few site recommendations. First, I would recommend code academy, which is very could to learn the principles of code: https://www.codecademy.
What codeacademy is not so good at, is actually teaching you how to code on your own computer. For this, and to get a better taste for web development, I would recommend following the official tutorial for Angular, a popular framework for frontend: https://angular.io/
To learn more about Python, I would.recommend www.learnpythonthehardway.org. I didn't follow it entirely, but I've read it's very good. (I didn't check if it's still for.free though)
Lastly, if you want to.know about data science and machine learning, you could.look at the YouTube channel 'The coding train'.
Hope this helps!
Best, Erle
https://codaisseur.com/ (Erle's program that landed her in a job) https://eaglescience.
https://codaisseur.com/ (Erle's program that landed her in a job) https://eaglescience.
Martin Merener's Recommended Online Data Science/Machine Learning Courses
Pelin, these are 3 courses that I highly recommend to get into Machine Learning for data science. Note that not all DataScience jobs require ML. Some are more about code development to deal with data, but these are nothing fancier than ML, just some simple stats. I find ML to be more interesting, but it depends on your taste. No matter what, you would surely need Python and SQL. There are tons of sources for that online, and I don't have any favorite to recommend. For ML these are the ones:
1) Probably the most ML course online ever. From 2012, but still excellent. https://www.coursera.org/learn/machine-learning
2) Another classic, less popular, but also excellent. https://work.caltech.edu/telecourse.html
Those two focus on the foundations.
On the other side, there are courses that are a lot more hands-on. The best ones I've known are from these guys: https://www.fast.ai/
They teach you how to immediately start training neural networks in cloud services.
I recommend that everything you do by yourself, along with these courses, you put it nicely in a github repository, so you can show your portfolio eventually.
1) Probably the most ML course online ever. From 2012, but still excellent. https://www.coursera.org/learn/machine-learning
2) Another classic, less popular, but also excellent. https://work.caltech.edu/telecourse.html
Those two focus on the foundations.
On the other side, there are courses that are a lot more hands-on. The best ones I've known are from these guys: https://www.fast.ai/
They teach you how to immediately start training neural networks in cloud services.
I recommend that everything you do by yourself, along with these courses, you put it nicely in a github repository, so you can show your portfolio eventually.
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