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This page contains resources about Data Science, including Data Engineering.
Subfields and Concepts
- Machine Learning / Data Mining
- Exploratory Data Analysis
- Data Preparation and Preprocessing
- High Performance/Parallel/Distributed Computing for Machine Learning
- Concurrent/Multi-threading Computing for Machine Learning
- Data Engineering and Databases
- Data Visualization
- Big Data
- How to Win a Data Science Competition: Learn from Top Kagglers - Coursera
- Exploratory data analysis in Python by Chloe Mawer and Jonathan Whitmore - PyCon 2017
- What is Data Science by Ioannis Kourouklides
- When [to use] and When Not to Use Distributed Machine Learning by Chih-Jen Lin
- Open Machine Learning Course (Medium)
- Mining Massive Datasets by Jure Leskovec, Anand Rajaraman and Jeff Ullman
- Hardware Acceleration for Data Processing by Gustavo Alonso
- CS109: Data Science
- Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.
- Schutt, R., & O'Neil, C. (2013). Doing data science: Straight talk from the frontline. O'Reilly Media.
- Leskovec, J., Rajaraman, A., & Ullman, J. D. (2014). Mining of massive datasets. Cambridge University Press. (link)
- Zumel, N., Mount, J., & Porzak, J. (2014). Practical data science with R. Manning.
- Nolan, D., & Lang, D. T. (2015). Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving. CRC Press.
- Elston, S. F. (2015). Data Science in the Cloud with Microsoft Azure Machine Learning and R. O'Reilly Media, Inc.
- Grus, J. (2015). Data Science from Scratch: First Principles with Python. O'Reilly Media.
- Madhavan, S. (2015). Mastering Python for Data Science. Packt Publishing Ltd.
- Blum, A., Hopcroft, J., & Kannan, R. (2015). Foundations of Data Science.
- VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly Media.
- Wickham, H., & Grolemund, G. (2017). R for Data Science. O'Reilly Media.
- Xing, E. P., Ho, Q., Xie, P., & Wei, D. (2016). Strategies and principles of distributed machine learning on big data. Engineering, 2(2), 179-195.
- Salloum, S., Dautov, R., Chen, X., Peng, P. X., & Huang, J. Z. (2016). Big data analytics on Apache Spark. International Journal of Data Science and Analytics, 1(3-4), 145-164.
- Huang, Y., Zhu, F., Yuan, M., Deng, K., Li, Y., Ni, B., ... & Zeng, J. (2015). Telco Churn Prediction with Big Data. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (pp. 607-618). ACM.
- Moritz, P., Nishihara, R., Stoica, I., & Jordan, M. I. (2015). SparkNet: Training Deep Networks in Spark. arXiv preprint arXiv:1511.06051.
- Docker (Containers)
- Anaconda Distribution - Python
- Beautiful Soup 4 - Python
- ray - Python
- multiprocessing - Python
- threading - Python
- auto_ml - Python
- Elasticsearch, Logstash, Kibana (ELK)
- Apache Solr
- Apache Hadoop
- Apache HBase
- Apache Spark
- Apache Hive
- Apache Kafka, which includes Kafka Connect
- Apache Cassandra
- Apache ZooKeeper
- Apache Pig
- Apache Storm
- Apache CouchDB
- Apache ActiveMQ
- pyspark - Spark Python API
- tensorflow_scala - Scala API for TensorFlow
- TensorFlowSharp - TensorFlow API for .NET languages
- TensorFlowOnSpark - It brings TensorFlow programs onto Apache Spark clusters
- Numba - Python
- Data Science Guide
- Data Science Engineering, your way
- Large Scale Machine Learning - libraries and papers
- What are some courses on large scale learning? - Quora
- 7 Steps to Mastering Data Preparation with Python - blog post
- Web Scraping for Data Science with Python - blog post
- Intro to Distributed Deep Learning Systems - blog post
- Princeton Commodities Modeling Blog
- Exploratory data analysis using Python for used car database taken from Kaggle - Github
- Detailed exploratory data analysis with Python - Kaggle
- Python-camp - Github
- Big Data: Spark, Hadoop, Hive, ZooKeeper, Solr, Kafka, Nutch, MongoDB, ... - installation instructions
- Deep Learning with Apache Spark and TensorFlow - blog post
- Build a Simple Chatbot with Tensorflow, Python and MongoDB - blog post
- Visual Data Analysis with Python - blog post
- Exploratory Data Analysis with Pandas - blog post
- Plotly Python Library Maps
- 5 Quick and Easy Data Visualizations in Python with Code - blog post
- William Koehrsen - blog
- ClaoudML - Free Data Science & Machine Learning Resources
- Parallel and Distributed Deep Learning by Tal Ben-Nun
- An introduction to parallel programming using Python's multiprocessing module - blog post
- Putting Machine Learning Models into Production - blog post
- Spark + Deep Learning: Distributed Deep Neural Network Training with SparkNet - blog post
- Data Science in Python: Pandas Cheat Sheet
- Simple Exploratory Data Analysis - PASSNYC - Kaggle
- EDA and Clustering - Kaggle
- Time Series Anomaly Detection: Optimizing your Machine Learning Jobs in Elasticsearch - webinar
- Web Access Logs in Elasticsearch and Machine Learning - webinar
- Deploying Python models to production - video
- Deploying Machine Learning apps with Docker containers - MUPy 2017 - video
- How to deploy machine learning models into production - video
- Federated Learning: Collaborative Machine Learning without Centralized Training Data - blog post
- Learn to Build Machine Learning Services, Prototype Real Applications, and Deploy your Work to Users - blog post
- Deploying Keras Deep Learning Models with Flask - blog post
- Introducing Flask-RESTful - blog post
- Getting started with Anaconda & Docker - blog post
- Docker for Data Science - blog post
- How Docker Can Help You Become A More Effective Data Scientist - blog post
- Deep Learning Installation Tutorial - Part 4: How to install Docker for Deep Learning - blog post
- pyspark (GitHub) - collection of resources
- Distributed-TensorFlow-Guide (GitHub) - Distributed TensorFlow basics and examples of training algorithms (with code)
- kafka-streams-machine-learning-examples (GitHub) - Machine Learning + Kafka Streams Examples (with code)
- How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka - blog post
- Realtime Machine Learning predictions with Kafka and H2O.ai - blog post
- Deploying deep learning models: Part 1 an overview - blog post
- A guide to deploying Machine/Deep Learning model(s) in Production - blog post
- How redBus uses Scikit-Learn ML models to classify customer complaints? - blog post