sfarHD14's repositories
aas
Code to accompany Advanced Analytics with Spark from O'Reilly Media
ab-stats
A presentation explaining the statistics behind AB-testing using live simulations
ART-Forum-2017-Stan-Tutorial
Materials from tutorial "Using Stan to Estimate Hierarchical Bayes Models," ART Forum 2017
Bambi_resources
Educational resources
baseball
Baseball data analysis in Python
bayes-workflow-book
Source files for the book "Bayesian Workflow Using Stan"
Bayesian-Analysis-with-Python-Second-Edition
Bayesian Analysis with Python - Second Edition, published by Packt
bayesian-machine-learning
Notebooks related to Bayesian methods for machine learning
causalml
Uplift modeling and causal inference with machine learning algorithms
cmdstanpy
CmdStanPy is a lightweight interface to Stan for Python users which provides the necessary objects and functions to compile a Stan program and fit the model to data using CmdStan.
ctci
Cracking the Coding Interview, 5th Edition
CtCI-6th-Edition-Python
Cracking the Coding Interview 6th Ed. Python Solutions
Databricks-Apache-Spark-2X-Certified-Developer
Databricks - Apache Spark™ - 2X Certified Developer
EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
empirical-bayes-book
Introduction to Empirical Bayes: Examples from Baseball Statistics
gp_regression
A Primer on Gaussian Processes for Regression Analysis (PyData NYC 2019)
hello-world
GitHub guides hello-world tutorial
jupyter-dash
Develop Dash apps in the Jupyter Notebook and JupyterLab
Learning-PySpark
Code repository for Learning PySpark by Packt
mcmc_pydata_london_2019
PyData London 2019 Tutorial on Markov chain Monte Carlo with PyMC3
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
pydata-seattle-2017
Materials for DataScience.com LTV and Neural Nets Talks at PyData Seattle
Python-programming-exercises
100+ Python challenging programming exercises
PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
QB_Bayes_Course
3-day course on Bayesian methods in Python for QuantumBlack
rstanarm
rstanarm R package for Bayesian applied regression modeling
Spark-The-Definitive-Guide
Spark: The Definitive Guide's Code Repository
Statistical-Modeling-Examples
Basic statistical modelling examples.
statsintro_python
Python modules and IPython Notebooks, for the book "Introduction to Statistics With Python"