Data Science: Difference between revisions

From Ioannis Kourouklides
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*[https://sebastianraschka.com/Articles/2014_multiprocessing.html An introduction to parallel programming using Python's multiprocessing module] - blog post
 
*[https://sebastianraschka.com/Articles/2014_multiprocessing.html An introduction to parallel programming using Python's multiprocessing module] - blog post
 
*[https://blog.cambridgespark.com/putting-machine-learning-models-into-production-d768560907bd Putting Machine Learning Models into Production] - blog post
 
*[https://blog.cambridgespark.com/putting-machine-learning-models-into-production-d768560907bd Putting Machine Learning Models into Production] - blog post
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*[https://www.kdnuggets.com/2015/12/spark-deep-learning-training-with-sparknet.html Spark + Deep Learning: Distributed Deep Neural Network Training with SparkNet] - blog post
 
*[https://www.datasciencecentral.com/profiles/blogs/data-science-in-python-pandas-cheat-sheet Data Science in Python: Pandas Cheat Sheet]
 
*[https://www.datasciencecentral.com/profiles/blogs/data-science-in-python-pandas-cheat-sheet Data Science in Python: Pandas Cheat Sheet]
 
*[https://www.kaggle.com/randylaosat/simple-exploratory-data-analysis-passnyc Simple Exploratory Data Analysis - PASSNYC] - Kaggle
 
*[https://www.kaggle.com/randylaosat/simple-exploratory-data-analysis-passnyc Simple Exploratory Data Analysis - PASSNYC] - Kaggle

Revision as of 16:49, 7 August 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.

Software

See also

Other Resources