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Difference between revisions of "Data Science"

From Ioannis Kourouklides
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*[ Exploratory Data Analysis with Pandas] - blog post
*[ Exploratory Data Analysis with Pandas] - blog post
*[ Plotly Python Library Maps]
*[ Plotly Python Library Maps]
*[ 5 Quick and Easy Data Visualizations in Python with Code] - blog post

Revision as of 00:03, 23 July 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
  • Parallel/Distributed/Concurrent Computing for Machine Learning
  • Data Engineering and Databases
  • Data Visualization
  • Big Data

Online courses

Video Lectures

Lecture Notes


  • 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.


See also

Other Resources