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Inspired by David Donoho's "50 Years of Data Science" (2015) paper, I'm releasing here a course proposal draft I wrote in 2009 for a possible course of "data science".

benchm-ml

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

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GBM-perf

Performance of various open source GBM implementations

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215

teach-data-science-UCLA-master-appl-stats

Materials for STATS 418 - Tools in Data Science course taught in the Master of Applied Statistics at UCLA

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benchm-databases

A minimal benchmark of various tools (statistical software, databases etc.) for working with tabular data of moderately large sizes (interactive data analysis).

ml-prod

Some thoughts on how to use machine learning in production

benchm-dl

Playing with various deep learning tools and network architectures

Python

survey-ml-tools

Quick informal survey at the Los Angeles Machine learning meetup about tools used for machine learning.

teach-data-science-msc-analytics-ceu

Materials for a short introductory/intermediate Data Science course taught in the MSc in Business Analytics program at the Central European University

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xgboost-adv-workshop-LA

Advanced workshop on XGBoost with Tianqi Chen in Santa Monica, June 2, 2016

ML-scoring

Compare the scoring speed of several open source machine learning libraries.

teach-ML-CEU-master-bizanalytics

Machine Learning #1 and #2 courses at CEU Master of Science in Business Analytics

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GBM-tune

Tuning GBMs (hyperparameter tuning) and impact on out-of-sample predictions

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GBM-multicore

GBM multicore scaling: h2o, xgboost and lightgbm on multicore and multi-socket systems

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datascience-latency

Latency numbers every data scientist should know (aka the pyramid of analytical tasks) - the order of magnitude of computational time for the most common analytical tasks (SQL-like data munging, linear and non-linear supervised learning etc.) with the typically available tools on commodity hardware.

GBM-intro

GBM intro talk (with R and Python code)

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dataset-sizes-kdnuggets

Size of datasets used for analytics based on 10 years of surveys by KDnuggets.

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talks-main

Most recent/important talks given at conferences/meetups

GBM-adv-workshop-Bp19

Advanced GBM Workshop - Budapest, Nov 2019

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kaggle-scripts-R-pydata

Kaggle scripts: R vs pydata + most popular R and Python packages for Machine Learning

awesome-GBMs

A curated list of gradient boosting machines (GBM) resources

benchm-dplyr-dt

dscomp-winstab

Winner stability in data science competitions

ml-algos-perf

Performance of Machine Learning Algorithms - playground for experimentation in order to understand their performance characteristics as a function of the attributes of the datasets used for training

Python

GBM-workshop

Code (and other materials) for an introductory talk/workshop on GBMs (developed originally for an R-Ladies Meetup)

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DS_meetups

Contents from the Real Data Science USA (formerly LA Data Science) Meetup

h2o-scoring--OLD

Various options for deploying h2o.ai models to production (scoring new data)

Java

datascience-1slide

Data Science in 1 Slide

ml-x1

Machine learning tools on monster EC2 X1 instance (128 cores, 2 TB RAM)

HTML

aboutme

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GBM-meltdown

The Effect of the Linux Kernel Page-Table Isolation (KPTI) Patch (Meltdown Vulnerability) on GBMs

benchm-ml-talks

bio

Szilard Pafka's short bio (to go with conference talk abstracts)

benchm-R-mysql

shinyvalidinp

MLprod-1slide

Machine Learning in Production in 1 Slide

LA-data-meetups

BigDataDayLA2015-DataScience

List of talks from the Data Science Track of Big Data Day LA 2015 (annual free conference)

szilard/datascience-course-historical

szilard

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