Awọn ẹya

1. Imọye Oríkĕ ni Awọn ọrọ-aje ati Awọn iṣiro: Awọn ipa, Awọn aala, Ijeri ati Aṣiri 2. Data Cleaning ati Igbaradi: Sonu Data, Outliers, ati Ifaminsi 3. Itupalẹ Data Exploratory ati Wiwo: Iyaworan ati Itumọ pẹlu Imọye Oríkĕ 4. Ipadasẹyin Laini: Ikọle Awoṣe, Itumọ Olusọdipúpọ ati Awọn Idaniloju 5. Awọn Ayẹwo Ipadasẹyin ati Awọn irufin: Collinearity, Heteroscedasticity, Autocorrelation 6. Idanwo Hypothesis ati p-iye: Pataki, Agbara, ati Awọn afiwera Ọpọ 7. p-jisaka, HARKing ati Iduroṣinṣin Iṣiro: Awọn ipalara ni Ọjọ-ori ti Imọye Oríkĕ 8. Onínọmbà jara akoko: iduro, ARIMA ati asọtẹlẹ 9. Idi ati Iṣayẹwo Ilana: DiD, IV, RDD ati Imọye Oríkĕ 10. Koodu iran ati reproducibility: r / Python, versioning ati reproducibility 11. Ijabọ Kikọ, Ibaraẹnisọrọ, ati Iwa: Awọn abajade Ififihan ati Awọn Aala Ibaraẹnisọrọ

Ẹyọ 10 / 11

Koodu iran ati reproducibility: r / Python, versioning ati reproducibility

Awọn anfani:

Agbara lati rii daju nipasẹ kika ati idanwo koodu itupalẹ R/Python ti a ṣe nipasẹ oye atọwọda ati fi idi iṣẹ akanṣe kan mulẹ pẹlu irugbin, titiipa ẹya ati igbekalẹ folda.
Agbara lati lo iṣakoso ẹya (Git), siseto imọwe (R Markdown / Quarto / Jupyter) ati iṣakoso ayika pẹlu atilẹyin itetisi atọwọda, ṣiṣe itupalẹ atunwi lati ibẹrẹ si ipari
Agbara lati ṣe idanwo ẹyọkan ati iṣayẹwo mimọ lati yẹ awọn aṣiṣe ipalọlọ ni koodu AI (misjoin, jo, àlẹmọ ti o padanu)

Ti tẹlẹ Itele