ianozsvald / data_science_deliveredLinks
Observations from Ian on successfully delivering data science products
☆543Updated 4 years ago
Alternatives and similar repositories for data_science_delivered
Users that are interested in data_science_delivered are comparing it to the libraries listed below
Sorting:
- ☆316Updated 4 years ago
- ☆263Updated 6 years ago
- DePy 2015 Talk☆117Updated 7 years ago
- A guide on how to set up Jupyter with Pyspark painlessly on AWS EC2 clusters, with S3 I/O support☆261Updated 7 years ago
- ☆411Updated 7 years ago
- A library for defensive data analysis.☆502Updated 5 years ago
- PyData Seattle 2015: Python Data Bikeshed☆127Updated 10 years ago
- Generate bootstrapped confidence intervals for A/B testing in Python.☆637Updated 5 years ago
- Tools for exploratory data analysis in Python☆645Updated 3 weeks ago
- Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models☆490Updated 8 years ago
- Content for architecting a data science platform for products using Luigi, Spark & Flask.☆163Updated 5 years ago
- PyData, The Complete Works of☆298Updated 8 years ago
- Framework for setting up predictive analytics services☆488Updated 2 years ago
- Directory of Jupyter notebooks exploring various topics☆316Updated 8 years ago
- a web application framework for python☆832Updated 3 years ago
- PyData NYC 2015 conference☆95Updated 9 years ago
- ☆160Updated 8 years ago
- Open-source Python library for statistical analysis of randomised control trials (A/B tests)☆341Updated 2 years ago
- My notes and superstitions about common machine learning algorithms☆367Updated 8 years ago
- ☆212Updated 9 years ago
- The ultimate reference guide to data wrangling with Python and R☆241Updated 3 years ago
- Pandas tutorial for SciPy2015 and SciPy2016 conference☆142Updated 8 years ago
- Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.☆468Updated 6 months ago
- code for my "stupid itertools tricks" talk from pydata seattle 2015☆153Updated 9 years ago
- Standard evaluations for binary classifiers so you don't have to☆315Updated 6 years ago
- Compiled, automatically parallel Python for data science☆490Updated 8 years ago
- Curated list of all dataset websites that I find☆84Updated 6 years ago
- Magic functions for using Jupyter Notebook with Apache Spark and a variety of SQL databases.☆171Updated 6 years ago
- ☆84Updated 7 years ago
- Machine Learning Problem Bible | Problem Set Here >>☆714Updated 5 years ago