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Data Science

From Ioannis Kourouklides
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This page contains resources about Data Science, including Data Engineering and Data Management.

Subfields and Concepts

  • Machine Learning / Data Mining
  • Exploratory Data Analysis
  • Data Preparation and Data Preprocessing
  • Data Fusion and Data Integration
  • Data Wrangling / Data Munging
  • Data Scraping
  • Data Sampling
  • Data Cleaning
  • High Performance/Parallel/Distributed/Cloud Computing for Machine Learning
  • Concurrent/Multi-threading Computing for Machine Learning
  • Data Engineering, Data Management and Databases
  • Data Visualization
  • Big Data

Online courses

Video Lectures

Lecture Notes

Books

  • Lanaro, G. (2017). Python High Performance. Packt Publishing Ltd.
  • Wickham, H., & Grolemund, G. (2017). R for Data Science. O'Reilly Media.
  • VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly Media.
  • 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.
  • 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.
  • Schutt, R., & O'Neil, C. (2013). Doing data science: Straight talk from the frontline. O'Reilly Media.
  • Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.

Scholarly Articles

  • Kang, D., Emmons, J., Abuzaid, F., Bailis, P., & Zaharia, M. (2017). NoScope: optimizing neural network queries over video at scale. Proceedings of the VLDB Endowment, 10(11), 1586-1597.
  • Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why Should I Trust You? Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144).
  • 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.
  • Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... & Dennison, D. (2015). Hidden technical debt in machine learning systems. In Advances in Neural Information Processing Systems (pp. 2503-2511).
  • 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).
  • Moritz, P., Nishihara, R., Stoica, I., & Jordan, M. I. (2015). SparkNet: Training Deep Networks in Spark. arXiv preprint arXiv:1511.06051.
  • Upadhyaya, S. R. (2013). Parallel approaches to machine learning—A comprehensive survey. Journal of Parallel and Distributed Computing, 73(3), 284-292.
  • Sakr, S., Liu, A., Batista, D. M., & Alomari, M. (2011). A survey of large scale data management approaches in cloud environments. IEEE Communications Surveys & Tutorials, 13(3), 311-336.
  • Abadi, D. J. (2009). Data management in the cloud: Limitations and opportunities. IEEE Data Eng. Bull., 32(1), 3-12.

Software

See also

Other Resources

General

Deployment and Production