roman79
Data professional with an interest in Python, R, Data Engineering, Distributed Computing, Machine Learning and Data Science.
Pinned Repositories
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
AWS_Lambda_in_Action
This source code distribution is a companion to the AWS Lambda in Action book available from Manning Publications.
cookbook-code
Recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python
courses
Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1
DSEC
Data Science and Engineering Club
DublinDataEngineering
The Open Source resources in Data Engineering, Machine Learning, Data Science areas, inspired by [The Open-Source Data Science Masters] (http://datasciencemasters.org/).
Introduction-to-Git-and-GitHub
Git is the most popular distributed version control system. Git is an open source and it is created by the same people who developed Linux (Linus Torvalds). Git allows you to track files and file changes in a repository “repo” (folder). Everything is stored in local repositories on your computer. Git makes changes and tracks modification to the files stored in GitHub. Git synchronize repository and code between different people.
python-machine-learning-book
The "Python Machine Learning" book code repository and info resource
python-patterns
A collection of design patterns/idioms in Python
Two-Sigma-Connect-Rental-Listing-Inquiries
Two Sigma and RentHop, a portfolio company of Two Sigma Ventures, invited Kagglers to unleash their creative engines to uncover business value in this unique recruiting competition. RentHop makes apartment search smarter by using data to sort rental listings by quality. But while looking for the perfect apartment is difficult enough, structuring and making sense of all available real estate data programmatically is even harder. Two Sigma invited kagglers to apply their talents in this recruiting competition featuring rental listing data from RentHop. Kagglers will predict the number of inquiries a new listing receives based on the listing’s creation date and other features. Doing so will help RentHop better handle fraud control, identify potential listing quality issues, and allow owners and agents to better understand renters’ needs and preferences. Two Sigma has been at the forefront of applying technology and data science to financial forecasts. While their pioneering advances in big data, AI, and machine learning in the financial world have been pushing the industry forward, as with all other scientific progress, they are driven to make continual progress. This challenge is an opportunity for competitors to gain a sneak peek into Two Sigma's data science work outside of finance.
roman79's Repositories
roman79/DSEC
Data Science and Engineering Club
roman79/DublinDataEngineering
The Open Source resources in Data Engineering, Machine Learning, Data Science areas, inspired by [The Open-Source Data Science Masters] (http://datasciencemasters.org/).
roman79/Introduction-to-Git-and-GitHub
Git is the most popular distributed version control system. Git is an open source and it is created by the same people who developed Linux (Linus Torvalds). Git allows you to track files and file changes in a repository “repo” (folder). Everything is stored in local repositories on your computer. Git makes changes and tracks modification to the files stored in GitHub. Git synchronize repository and code between different people.
roman79/python-machine-learning-book
The "Python Machine Learning" book code repository and info resource
roman79/Two-Sigma-Connect-Rental-Listing-Inquiries
Two Sigma and RentHop, a portfolio company of Two Sigma Ventures, invited Kagglers to unleash their creative engines to uncover business value in this unique recruiting competition. RentHop makes apartment search smarter by using data to sort rental listings by quality. But while looking for the perfect apartment is difficult enough, structuring and making sense of all available real estate data programmatically is even harder. Two Sigma invited kagglers to apply their talents in this recruiting competition featuring rental listing data from RentHop. Kagglers will predict the number of inquiries a new listing receives based on the listing’s creation date and other features. Doing so will help RentHop better handle fraud control, identify potential listing quality issues, and allow owners and agents to better understand renters’ needs and preferences. Two Sigma has been at the forefront of applying technology and data science to financial forecasts. While their pioneering advances in big data, AI, and machine learning in the financial world have been pushing the industry forward, as with all other scientific progress, they are driven to make continual progress. This challenge is an opportunity for competitors to gain a sneak peek into Two Sigma's data science work outside of finance.
roman79/awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
roman79/AWS_Lambda_in_Action
This source code distribution is a companion to the AWS Lambda in Action book available from Manning Publications.
roman79/customer-analytics-pydata-workshop-march-2018
Repo containing info and exercises about the customer analytics / pydata workshop in March 2018
roman79/Data-Wrangling-with-R
Meetup
roman79/DataScienceSpecialization.github.io
http://DataScienceSpecialization.github.io
roman79/Exploratory_Data_Analysis
roman79/Flask
Flask
roman79/Getting_and_Cleaning_Data
roman79/ggthemes
ggplot themes and scales
roman79/Hostelworld-Challenge
roman79/LearnJava
Learn Java Programming
roman79/Lending-Club
Analysis and modeling Lending Club datasets
roman79/movies-python-bolt
Neo4j Example application with flask backend using the neo4j-python-driver
roman79/pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
roman79/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 ;)
roman79/PythonDataScienceHandbook
Jupyter Notebooks for the Python Data Science Handbook
roman79/RepData_PeerAssessment1
Peer Assessment 1 for Reproducible Research
roman79/scikit-learn
scikit-learn: machine learning in Python
roman79/seaborn
Statistical data visualization using matplotlib
roman79/serverless-python-sample
A simple serverless python sample with REST API endpoints and dependencies
roman79/Shiny
Simple shiny app
roman79/statsintro
Introduction to Statistics
roman79/things
Logical puzzles, utils, snippets, things
roman79/ThinkStats2
Text and supporting code for Think Stats, 2nd Edition
roman79/xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow