-datasciencecoursera

Repository to store files of data science project

2016

a-b-testing-and-beyond

Information and content associated with "A/B Testing and Beyond", a continuing eduction certificate offered by the University of San Francisco's Data Institute.

ab_test

Advanced A/B Testing Workshop

ad_response_tutorial

This tutorial shows users how to evaluate advertising response using last click attribution, experiments, marketing mix models and attribution models. By applying these methods to the same (synthetic) data set, users will learn how the methods compare. We also illustrate the data manipulation that is required to prepare typical raw advertising data for analysis. Examples are worked in R and slides are provided in LaTeX.

anaveenan

anaveenan.github.io

apache_spark_answers

arjunannaveenan

bayesian-modelling-in-python

A python tutorial on bayesian modeling techniques (PyMC3)

big-data-analytics-with-spark

blogpost

repository to hold all blog post

bostondatafest2016

causal-inference

Deliberate Practice for Learning Causal Inference

causal-inference-workshop

causalinference_crashcourse

Causal Inference Crash Course for Scientists - contains slides and Jupyter notebooks

classification_ml

Has ipython notebooks related to ML UW course

clumper

Clumper will make it easier to analyze list of dictionaries

counter-app

courses

Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

data-science-with-spark

Machine Learning and Data Analysis Case Studies using Spark.

data-screencasts

Code from live exploratory analyses of data in R

data-visualisation

This repository contains data visualisations

data_science_notes

Keep learning something new

datasharing

The Leek group guide to data sharing

dbt-taxi-data

dse230_data_analysis_using_hadoop_and_spark_ucsd

Map-reduce, streaming analysis, and external memory algorithms and their implementation using the Hadoop and its eco-system: HBase, Hive, Pig and Spark. The class will include assignment of analyzing large existing databases.

dto

Very Basic Optimization of the Email Delivery Time

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.

exdata_plotting1

Plotting Assignment 1 for Exploratory Data Analysis