Pinned Repositories
Dezyre
2015lab7
Decison Trees, Random Forrests, Ensemble Methods
awesome-public-datasets
A awesome list of (large-scale) public datasets on the Internet. (On-going collection)
building-spark-applications-live-lessons
Supporting content (slides and exercises) for the Addison-Wesley (Pearson) video series covering best practices for developing scalable Spark applications for predictive analytics in the context of a data scientist's standard workflow.
CompStats
Code for a workshop on statistical interference using computational methods in Python.
courses
Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1
data-science-primer
A set of self paced resources for anyone looking to get into data science. The materials assume an absolute beginner and are intended to prepare students for the Galvanize Data Science interview process: http://www.galvanize.com/courses/data-science/
data-science-toolbox
Start doing data science in minutes
data-scientists-guide-apache-spark
Best practices of using Spark for practicing data scientists in the context of a data scientist’s standard workflow.
dataweek-workshop
Machine learning workshop using Python, pandas, and scikit-learn. The first half of the day covered supervised classification using Logistic Regression and how to use cross validation to evaluate your models . The second half of the day covered unsupervised clustering with Kmeans as well as an overview of the data science process.
xzanadu's Repositories
xzanadu/search_with_machine_learning_course
Public repository for the Search with Machine Learning course taught by Daniel Tunkelang and Grant Ingersoll. Available at https://corise.com/?utm_source=daniel.
xzanadu/fastbook
The fastai book, published as Jupyter Notebooks
xzanadu/ThinkStats2
Text and supporting code for Think Stats, 2nd Edition
xzanadu/Springboard
xzanadu/Dezyre
xzanadu/courses
Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1
xzanadu/awesome-public-datasets
A awesome list of (large-scale) public datasets on the Internet. (On-going collection)
xzanadu/javascript
GitBook teaching programming basics with Javascript
xzanadu/python_koans
Python Koans - Learn Python through TDD
xzanadu/git-flight-rules
Flight rules for git - a work in progress!
xzanadu/building-spark-applications-live-lessons
Supporting content (slides and exercises) for the Addison-Wesley (Pearson) video series covering best practices for developing scalable Spark applications for predictive analytics in the context of a data scientist's standard workflow.
xzanadu/data-science-primer
A set of self paced resources for anyone looking to get into data science. The materials assume an absolute beginner and are intended to prepare students for the Galvanize Data Science interview process: http://www.galvanize.com/courses/data-science/
xzanadu/2015lab7
Decison Trees, Random Forrests, Ensemble Methods
xzanadu/data-scientists-guide-apache-spark
Best practices of using Spark for practicing data scientists in the context of a data scientist’s standard workflow.
xzanadu/statlearning-notebooks
Python notebooks for exercises covered in Stanford statlearning class (where exercises were in R).
xzanadu/CompStats
Code for a workshop on statistical interference using computational methods in Python.
xzanadu/data-science-toolbox
Start doing data science in minutes
xzanadu/Python-WebImageScraper
A Python powered app that scrapes images from requested URLs and dumps them in scraper's directory.
xzanadu/storm
Distributed and fault-tolerant realtime computation: stream processing, continuous computation, distributed RPC, and more
xzanadu/dataweek-workshop
Machine learning workshop using Python, pandas, and scikit-learn. The first half of the day covered supervised classification using Logistic Regression and how to use cross validation to evaluate your models . The second half of the day covered unsupervised clustering with Kmeans as well as an overview of the data science process.
xzanadu/sounder
A grouping of Apache Pig examples.