├── .DS_Store ├── DSPN_syllabus_2025.pdf ├── ExampleFinalProject ├── Banuelos_DSPN19_FinalProject.ipynb ├── DWI.png ├── IGTDecks.png ├── IGTDisplay.png ├── NeuroimagingDiagram.png ├── Topologies.png └── cleanedMergedWINData.csv ├── ExerciseSubmissions ├── README.md └── ex2_models_as_testable_hypotheses.ipynb ├── Final_Project_Description.pdf ├── Final_Project_Rubric.pdf ├── Homework datasets ├── .DS_Store ├── hcp_data │ ├── HCP_S1200_DataDictionary_April_20_2018.csv │ ├── HCP_S1200_DataDictionary_April_20_2018.gsheet │ ├── README.md │ ├── data_loading.R │ └── unrestricted_trimmed_1_7_2020_10_50_44.csv └── lexDat │ ├── Items.csv │ ├── LexicalData.csv │ ├── LexicalData_toclean.csv │ ├── LexicalData_withIncorrect.csv │ └── README.md ├── LICENSE ├── Primers ├── ComparingMeans.pdf ├── Correlation.pdf ├── HypothesisTesting.pdf ├── Samples.pdf ├── SummaryStatistics.pdf └── TestsOfAssociation.pdf ├── README.md ├── Readings ├── .DS_Store ├── Bates_2010_chapter1.pdf ├── Beaujean_2014.pdf ├── Cairo_2012_chapter1.pdf ├── Cairo_2012_chapter3.pdf ├── DeRegt_Dieks_2005.pdf ├── Dretske_1983.pdf ├── Franconeri_etal_2021.pdf ├── Fulop_Chater_2013.pdf ├── Goodman_etal_2016.pdf ├── Gorgolewski_etal_2016.pdf ├── Guest_Martin_2020.pdf ├── Guest_Martin_2022.pdf ├── Mayo_1997.pdf ├── Mensh_Kording_2017.pdf ├── Muller_Freyberg_2003.pdf ├── Platt_1964.pdf ├── Popper_1959.pdf ├── Preacher_Hayes_2008.pdf ├── Sandve_etal_2013.pdf ├── Vlastelica_2019.pdf ├── Wagenmakers_2007.pdf ├── Wasserstein_etal_2019.pdf ├── Wickham_2013.pdf ├── Yanai_Lercher_2020.pdf ├── Yarkoni_2019.pdf └── vanRooij_Baggio_2021.pdf └── Slides ├── .DS_Store ├── 01 - Quantitative epistemology.pdf ├── 02 - The value of openness.pdf ├── 03 - What is a theory.pdf ├── 04 - Models as testable hypotheses.pdf ├── 05 - Data as objects and architectures.pdf ├── 06 - Techniques for data cleansing.pdf ├── 07 - Visualization as analysis.pdf ├── 08 - Visualization through human eyes.pdf ├── 09 - Bias-variance tradeoff.pdf ├── 10 - Linear models.pdf ├── 11 - Least squares and model evaluation.pdf ├── 12 - Limits of linear regression.pdf ├── 13 - Mixed effects models.pdf ├── 14 - Classifiers.pdf ├── 15 - The beauty of kNN.pdf ├── 16 - Cross validation.pdf ├── 17 - Resampling methods.pdf ├── 18 - Mediation and moderation.pdf ├── 19 - Power analysis via simulations.pdf ├── 20 - Selecting the _best_ model.pdf ├── 21 - Regularized regression.pdf ├── 22 - Principal component methods.pdf ├── 23 - Reconsidering the p-value.pdf ├── 24 - Bayes factor.pdf ├── 25 - Errors and inferences.pdf ├── 26 - Telling your data story.pdf └── 27 - Theories as social constructs.pdf /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CoAxLab/DataSciencePsychNeuro/HEAD/.DS_Store -------------------------------------------------------------------------------- /DSPN_syllabus_2025.pdf: -------------------------------------------------------------------------------- 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