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A personal blog about data visualization and data analysis.
Welcome to my blog!
Here I will share some ideas about dataviz and data science.
- Point patterns;/gis/point_patterns;1
- Geostatistical data;/gis/geospatial;2
- Spatial data and models;/gis/spatial_models;3
- Sampling points in space;/gis/spatial_sampling_points;4
- Spatial sampling;/gis/spatial_sampling;5
- Radar interferometry with Pygmtsar;/gis/pygmtsar;6
- OpenEO for SAR images;/gis/openeo_sar;7
- OpenEO 2: time series;/gis/openeo_ts;8
- OpenEO;/gis/openeo;9
- Digital Elevation Models;/gis/digitalelevationmodels;10
- Open Street Map services;/gis/openstreetmap;11
- Open Web Consortium standards;/gis/owc_standards;12
- Map design;/gis/map_design;13
- Operations on raster data;/gis/raster_ops;14
- Operations on vector data;/gis/vector_ops;15
- 101 ways to reproject your data;/gis/pyproj;16
- Raster data;/gis/raster_data;17
- Vector data;/gis/vector_data;18
- Choosing the right projection;/gis/projections;19
- Introduction to geographic data analysis;/gis/gis_intro;20
- Dataviz and probability;/statistics/visualizations_example;21
- Periodic splines;/statistics/periodic_splines;22
- Directional statistics;/statistics/directional;23
- Prior sensitivity analysis;/statistics/sensitivity;24
- Horseshoe priors;/statistics/horseshoe;25
- MRP;/statistics/mrp;26
- Application of the Lotka-Volterra model;/statistics/lotka_volterra;27
- Differential equations;/statistics/ode;28
- Dirichlet Process Mixture Models;/statistics/dp;29
- Bayesian Additive Regression Trees;/statistics/bart;30
- Splines;/statistics/spline;31
- Gaussian processes regression;/statistics/gp_example;32
- Gaussian processes;/statistics/gp;33
- Nonparametric models;/statistics/nonparametric_intro;34
- Stochastic volatility models;/statistics/stochastic_volatility;35
- Structural time series;/statistics/structural_time_series;36
- Time series;/statistics/time_series;37
- Quantile regression;/statistics/extreme_quantile;38
- Introduction to Extreme Values theory;/statistics/extreme_intro;39
- Frailty models and cure rate models;/statistics/survival_example_frailty;40
- Accelerated Failure Time models;/statistics/survival_example_aft;41
- Application of survival analysis with discrete times;/statistics/survival_example_2;42
- Application of survival analysis 1;/statistics/survival_example;43
- Introduction to survival analysis;/statistics/survival_analysis;44
- Regression discontinuity design;/statistics/rdd;45
- Difference in difference;/statistics/difference_in_differences;46
- Instrumental variable regression;/statistics/instrumental_variable;47
- Randomized controlled trials;/statistics/randomized;48
- Validity;/statistics/validity;49
- Causal inference and Bayesian networks;/statistics/causal_intro_2;50
- Causal inference;/statistics/causal_intro;51
- Nested factor;/statistics/nested_factors;52
- Repeated measures;/statistics/repeated_measures;53
- Split plot design;/statistics/split_plot;54
- Crossover design;/statistics/crossover;55
- Latin square design;/statistics/latin_square;56
- Full factorial design;/statistics/full_factorial;57
- Completely randomized design;/statistics/crd;58
- Design of experiments;/statistics/doe;59
- Stratification;/statistics/stratification;60
- Random sampling;/statistics/random_sampling;61
- Data collection;/statistics/data_collection;62
- Things that could go wrong;/statistics/problem_solving_issues;63
- The problem solving workflow;/statistics/problem_solving;64
- Mixed effects models with more than two levels;/statistics/three_levels;65
- Leveraging mixed-effect models;/statistics/bambi_multilevel;66
- Random models and mixed models;/statistics/random_models;67
- Hierarchical models and meta-analysis;/statistics/hierarchical_metaanalysis;68
- Hierarchical models;/statistics/hierarchical_models;69
- Poisson regression;/statistics/poisson_regression;70
- Logistic regression;/statistics/logistic_regression;71
- Robust linear regression;/statistics/robust_regression;72
- Multi-linear regression;/statistics/multivariate_regression;73
- Linear regression with binary input;/statistics/regression_binary_input;74
- Introduction to the linear regression;/statistics/regression;75
- Model comparison, cont.;/statistics/model_averaging_cont;76
- Model comparison;/statistics/model_averaging;77
- Re-parametrizing your model;/statistics/reparametrization;78
- Predictive checks;/statistics/predictive_checks;79
- Trace inspection;/statistics/trace_inspection;80
- Introduction to the Bayesian workflow;/statistics/bayesian_workflow;81
- Mixture models;/statistics/mixture;82
- Multidimensional distributions;/statistics/categories;83
- The Gaussian model;/statistics/reals;84
- Bonus: counting animals in a park;/statistics/hypergeom;85
- The Negative Binomial model;/statistics/negbin;86
- The Poisson model;/statistics/poisson;87
- The Beta-Binomial model;/statistics/betabin;88
- Section introduction;/statistics/simple_models_intro;89
- Some notation about probability;/statistics/probability_reminder;90
- How does MCMC works;/statistics/mcmc_intro;91
- Introduction to Bayesian inference;/statistics/bayes_intro;92
- An overview to statistics;/statistics/preface;93
- The Gestalt principles;/dataviz/gestalt;94
- Design tricks;/dataviz/design-introduction;95
- How to choose a color map;/dataviz/palettes-introduction;96
- Introduction to color perception;/dataviz/color-introduction;97
- Drawing is redrawing;/dataviz/gender-economist;98
- Visual queries;/dataviz/visual-queries;99
- Channel effectiveness;/dataviz/effectiveness;100
- Evolutions of the line chart;/dataviz/linechart-evolution;101
- Beyond the 1D scatterplot;/dataviz/scatterplot-evolution;102
- Perception;/dataviz/perception;103
- Fundamental charts;/dataviz/fundamental-charts;104
- Marks and channels;/dataviz/marks-channels;105
- Data abstraction;/dataviz/data-types;106
- Data visualization;/dataviz/dataviz;107
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