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analytic_gw_book

This repository contains the Python code of "Analytical Groundwater Modeling: Theory and Applications Using Python" by Mark Bakker and Vincent Post

analyzing-visualizing-data-powerbi

This repository contains the lab files and other resources for the free Microsoft course DAT207x: Analyzing and Visualizing Data with Power BI. To learn how to connect, explore, and visualize data with Power BI, sign up for this course on edX.

atlas

Datasets, code and virtual workspace for the Climate Change ATLAS

awesome-geospatial

Long list of geospatial tools and resources

bayesian-biostatistics-2016-idiv

Course materials for the Bayesian Biostatistics course (25-27.1.2016, iDiv, Leipzig, Germany)

bayesian_statistics_course

botorch

Bayesian optimization in PyTorch

bpca

Bayesian Principal Components Analysis (bPCA)

calibrator

An R package for statistical calibration of seasonal climate forecasts

causal-inference-visual-guides

A collection of visual guides to help applied scientists learn causal inference.

cc-2-rmarkdown

Using R Markdown to construct reproducible code

cc-3-datamanip

Using tidyr, dplyr and piping for easy and efficient data manipulation

cc-data-manip-creative-dplyr

cc-dataviz-beautification

This repository holds the materials for the Coding Club dataviz workshop in Boulder

cc-time-series

Intro to time series analysis in R

cc-trends-analysis-dataviz

cheatsheets

climate4r

An R Framework for Climate Data Access and Post-processing

climate4r.indices

Climate Indices in Climate4R

climate_indices

Climate indices for drought monitoring, community reference implementations in Python

cnn-rnn

Image classification using CNN

convolution-network

A simple tutorial on convolution network in Python

correlation-analysis

correlationfunnel

Speed Up Exploratory Data Analysis (EDA)

coursera--deep_learning_specialization--by_anderw_ng

Coursera (Deep_Learning_Specialization) By Andrew Ng and offered by deeplearning.ai.**Each of the below Courses Contains Notes, programming assignments, and quizzes.1- Neural Networks and Deep Learning;2- Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization; 3- Structuring Machine Learning Projects; 4- Convolutional Neural Networks;5- Sequence Models.

coursera-machine-learning

My Python solutions for the assignments in the machine learning class by andrew ng on coursera.

data-analysis-using-r

data-science-from-scratch

code for Data Science From Scratch book

data-science-ml-capstone-projects

data-science-using-r