AzureDSVM - AzureDSVM is an R package that offers convenient harness of Azure DSVM, remote execution of scalable and elastic data science work, and monitoring of on-demand resource consumption

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The AzureDSVM (Azure Data Science Virtual Machine) is an R Package for Data Scientists working with the Azure compute platform as a complement to the underlying AzureSMR for controlling Azure Data Science Virtual Machines.Azure Data Science Virtual Machine (DSVM) is a powerful data science development environment with pre-installed tools and packages that empower data scientists for convenient data wrangling, model building, and service deployment.



Related Projects


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This repository contains walkthroughs, templates and documentation related to Machine Learning & Data Science services and platforms on Azure. Services and platforms include Data Science Virtual Machine, Azure ML, HDInsight, Microsoft R Server, SQL-Server, Azure Data Lake etc.There are also materials from tutorials we have delivered at KDD, Strata etc., using the above services and platforms.

MMLSpark - Microsoft Machine Learning for Apache Spark

  •    Scala

MMLSpark provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.MMLSpark requires Scala 2.11, Spark 2.1+, and either Python 2.7 or Python 3.5+. See the API documentation for Scala and for PySpark.

data-science-your-way - Ways of doing Data Science Engineering and Machine Learning in R and Python

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These series of tutorials on Data Science engineering will try to compare how different concepts in the discipline can be implemented in the two dominant ecosystems nowadays: R and Python. We will do this from a neutral point of view. Our opinion is that each environment has good and bad things, and any data scientist should know how to use both in order to be as prepared as posible for job market or to start personal project.

DataScienceR - a curated list of R tutorials for Data Science, NLP and Machine Learning

  •    R

This repo contains a curated list of R tutorials and packages for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks. Curated list of Python tutorials for Data Science, NLP and Machine Learning.

computerscience - Free technical resources for faculty, students, and Microsoft developer advocates for use in computer science learning forums

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The content and code on this repo is intended for computer science instruction as a collaboration with Microsoft developer advocates and Faculty / Students under the MIT license. Please check back regularly for updated versions. This repo provides technical resources to help students and faculty learn about Azure and teach others. The content covers cross-platform scenarios in AI and machine learning, data science, web development, mobile app dev, internet of things, and devops.

DataSciencePython - common data analysis and machine learning tasks using python

  •    Python

This repo contains a curated list of Python tutorials for Data Science, NLP and Machine Learning. Curated list of R tutorials for Data Science, NLP and Machine Learning.

awesome - Awesome resources on Bioinformatics, data science, machine learning, programming language (Python, Golang, R, Perl) and miscellaneous stuff


Collection of useful resources on Bioinformatics, data science, machine learning, programming language (Python, Golang, R, Perl, etc.) and miscellaneous stuff.

spark-py-notebooks - Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

  •    Jupyter

This is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, from basic to advanced, by using the Python language. If Python is not your language, and it is R, you may want to have a look at our R on Apache Spark (SparkR) notebooks instead. Additionally, if your are interested in being introduced to some basic Data Science Engineering, you might find these series of tutorials interesting. There we explain different concepts and applications using Python and R.

pachyderm - Reproducible Data Science at Scale!

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Pachyderm is a tool for production data pipelines. If you need to chain together data scraping, ingestion, cleaning, munging, wrangling, processing, modeling, and analysis in a sane way, then Pachyderm is for you. If you have an existing set of scripts which do this in an ad-hoc fashion and you're looking for a way to "productionize" them, Pachyderm can make this easy for you. Install Pachyderm locally or deploy on AWS/GCE/Azure in about 5 minutes.

dvc - ⚡️ML models version control, make them shareable and reproducible

  •    Python

It aims to replace tools like Excel and Docs that are being commonly used as a knowledge repo and a ledger for the team, ad-hoc scripts to track and move deploy different model versions, ad-hoc data file suffixes and prefixes. DVC is compatible with Git for storing code and the dependency graph (DAG), but not data files cache. To store and share data files cache DVC supports remotes - any cloud (S3, Azure, Google Cloud, etc) or any on-premise network storage (via SSH, for example).

awesome-datascience - :memo: An awesome Data Science repository to learn and apply for real world problems


An open source Data Science repository to learn and apply towards solving real world problems. First of all, Data Science is one of the hottest topics on the Computer and Internet farmland nowadays. People have gathered data from applications and systems until today and now is the time to analyze them. The next steps are producing suggestions from the data and creating predictions about the future. Here you can find the biggest question for Data Science and hundreds of answers from experts. Our favorite data scientist is Clare Corthell. She is an expert in data-related systems and a hacker, and has been working on a company as a data scientist. Clare's blog. This website helps you to understand the exact way to study as a professional data scientist.

datascience-box - Data Science Course in a Box

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This introductory data science course that is our (working) answer to these questions. The courses focuses on data acquisition and wrangling, exploratory data analysis, data visualization, and effective communication and approaching statistics from a model-based, instead of an inference-based, perspective. A heavy emphasis is placed on a consitent syntax (with tools from the tidyverse), reproducibility (with R Markdown) and version control and collaboration (with git/GitHub). We help ease the learning curve by avoiding local installation and supplementing out-of-class learning with interactive tools (like learnr tutorials). By the end of the semester teams of students work on fully reproducible data analysis projects on data they acquired, answering questions they care about. This repository serves as a "data science course in a box" containing all materials required to teach (or learn from) the course described above.

data-science-with-ruby - Practical Data Science with Ruby based tools.

  •    Ruby

Data Science is a new "sexy" buzzword without specific meaning but often used to substitute Statistics, Scientific Computing, Text and Data Mining and Visualization, Machine Learning, Data Processing and Warehousing as well as Retrieval Algorithms of any kind. This curated list comprises awesome tutorials, libraries, information sources about various Data Science applications using the Ruby programming language.

OpenML - Open Machine Learning

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We are a group of people who are excited about open science, open data and machine learning. We want to make machine learning and data analysis simple, accessible, collaborative and open with an optimal division of labour between computers and humans. OpenML is an online machine learning platform for sharing and organizing data, machine learning algorithms and experiments. It is designed to create a frictionless, networked ecosystem, that you can readily integrate into your existing processes/code/environments, allowing people all over the world to collaborate and build directly on each other’s latest ideas, data and results, irrespective of the tools and infrastructure they happen to use.

connectthedots - Connect tiny devices to Microsoft Azure services to build IoT solutions

  •    CSharp is an open source project created by Microsoft to help you get tiny devices connected to Microsoft Azure IoT and to implement great IoT solutions taking advantage of Microsoft Azure advanced analytic services such as Azure Stream Analytics and Azure Machine Learning.The project is built with the assumption that the sensors get the raw data and format it into a JSON string. That string is then sent to Azure IoT Hub, from which a Web app gathers the data and displays it as a chart. Optional other functions of the Azure cloud include detecting and displaying alerts and averages, however this is not required.

MyDriving - Building IoT or Mobile solutions are fun and exciting

  •    CSharp

This repository contains the MyDriving sample that demonstrates the design and implementation of a comprehensive Internet of Things (IoT) solution that gathers telemetry from devices, processes that data in the cloud, and applies machine learning to provide an adaptive response. The demonstration logs data about your car trips using both your mobile phone and an On-Board Diagnostics (OBD) adaptor that collects information from your vehicle's control system. The Azure backend uses this data to provide feedback on your driving style in comparison to other users. A collection of resources to enable you to deploy and configure the Azure backend for MyDriving to your own Azure subscription. This includes Azure Resource Manager (ARM) templates for deploying all the necessary Azure services, Bash scripts, and PowerShell scripts.

Agile_Data_Code_2 - Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition

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Like my work? I am Principal Consultant at Data Syndrome, a consultancy offering assistance and training with building full-stack analytics products, applications and systems. Find us on the web at There is now a video course using code from chapter 8, Realtime Predictive Analytics with Kafka, PySpark, Spark MLlib and Spark Streaming. Check it out now at

practical-machine-learning-with-python - Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system

  •    Jupyter

"Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial Intelligence and Deep Learning. Besides this, data scientists have been termed as having "The sexiest job in the 21st Century" which makes it all the more worthwhile to build up some valuable expertise in these areas. Getting started with machine learning in the real world can be overwhelming with the vast amount of resources out there on the web. "Practical Machine Learning with Python" follows a structured and comprehensive three-tiered approach packed with concepts, methodologies, hands-on examples, and code. This book is packed with over 500 pages of useful information which helps its readers master the essential skills needed to recognize and solve complex problems with Machine Learning and Deep Learning by following a data-driven mindset. By using real-world case studies that leverage the popular Python Machine Learning ecosystem, this book is your perfect companion for learning the art and science of Machine Learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute Machine Learning systems and projects successfully.

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

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This project aims at a minimal benchmark for scalability, speed and accuracy of commonly used implementations of a few machine learning algorithms. The target of this study is binary classification with numeric and categorical inputs (of limited cardinality i.e. not very sparse) and no missing data, perhaps the most common problem in business applications (e.g. credit scoring, fraud detection or churn prediction). If the input matrix is of n x p, n is varied as 10K, 100K, 1M, 10M, while p is ~1K (after expanding the categoricals into dummy variables/one-hot encoding). This particular type of data structure/size (the largest) stems from this author's interest in some particular business applications. Note: While a large part of this benchmark was done in Spring 2015 reflecting the state of ML implementations at that time, this repo is being updated if I see significant changes in implementations or new implementations have become widely available (e.g. lightgbm). Also, please find a summary of the progress and learnings from this benchmark at the end of this repo.