Difference between revisions of "Data Science"

From Ioannis Kourouklides
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*[https://www.elastic.co/webinars/event-logs-in-elasticsearch-and-machine-learning Web Access Logs in Elasticsearch and Machine Learning] - webinar
 
*[https://www.elastic.co/webinars/event-logs-in-elasticsearch-and-machine-learning Web Access Logs in Elasticsearch and Machine Learning] - webinar
 
*[https://www.youtube.com/watch?v=f3I0izerPvc Deploying Python models to production] - video
 
*[https://www.youtube.com/watch?v=f3I0izerPvc Deploying Python models to production] - video
*[https://www.youtube.com/watch?v=knAFR4u73Es Deploying Machine Learning apps with Docker containers - MUPy 2017] - video
 
 
*[https://www.youtube.com/watch?v=-UYyyeYJAoQ How to deploy machine learning models into production] - video
 
*[https://www.youtube.com/watch?v=-UYyyeYJAoQ How to deploy machine learning models into production] - video
 
*[https://ai.googleblog.com/2017/04/federated-learning-collaborative.html Federated Learning: Collaborative Machine Learning without Centralized Training Data] - blog post
 
*[https://ai.googleblog.com/2017/04/federated-learning-collaborative.html Federated Learning: Collaborative Machine Learning without Centralized Training Data] - blog post
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*[https://towardsdatascience.com/deploying-keras-deep-learning-models-with-flask-5da4181436a2 Deploying Keras Deep Learning Models with Flask] - blog post
 
*[https://towardsdatascience.com/deploying-keras-deep-learning-models-with-flask-5da4181436a2 Deploying Keras Deep Learning Models with Flask] - blog post
 
*[https://www.twilio.com/engineering/2012/10/18/open-sourcing-flask-restful Introducing Flask-RESTful] - blog post
 
*[https://www.twilio.com/engineering/2012/10/18/open-sourcing-flask-restful Introducing Flask-RESTful] - blog post
  +
*[https://www.youtube.com/watch?v=knAFR4u73Es Deploying Machine Learning apps with Docker containers - MUPy 2017] - video
 
*[https://medium.com/@patrickmichelberger/getting-started-with-anaconda-docker-b50a2c482139 Getting started with Anaconda & Docker] - blog post
 
*[https://medium.com/@patrickmichelberger/getting-started-with-anaconda-docker-b50a2c482139 Getting started with Anaconda & Docker] - blog post
 
*[https://towardsdatascience.com/docker-for-data-science-9c0ce73e8263 Docker for Data Science] - blog post
 
*[https://towardsdatascience.com/docker-for-data-science-9c0ce73e8263 Docker for Data Science] - blog post
 
*[https://towardsdatascience.com/how-docker-can-help-you-become-a-more-effective-data-scientist-7fc048ef91d5 How Docker Can Help You Become A More Effective Data Scientist] - blog post
 
*[https://towardsdatascience.com/how-docker-can-help-you-become-a-more-effective-data-scientist-7fc048ef91d5 How Docker Can Help You Become A More Effective Data Scientist] - blog post
  +
*[https://becominghuman.ai/docker-for-data-science-part-1-dd41e5ef1d80 Simplified Docker-ing for Data Science — Part 1] - blog post
 
*[https://www.born2data.com/2017/deeplearning_install-part4.html Deep Learning Installation Tutorial - Part 4: How to install Docker for Deep Learning ] - blog post
 
*[https://www.born2data.com/2017/deeplearning_install-part4.html Deep Learning Installation Tutorial - Part 4: How to install Docker for Deep Learning ] - blog post
 
*[https://github.com/vsmolyakov/pyspark pyspark (GitHub)] - collection of resources
 
*[https://github.com/vsmolyakov/pyspark pyspark (GitHub)] - collection of resources

Revision as of 02:33, 28 October 2018

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

Online courses

Video Lectures

Lecture Notes

Books

  • 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.

Scholarly Articles

  • 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.

Software

See also

Other Resources