- Igual, Laura, Seguí, Santi, Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications, Springer Verlag, 2017 DOI 10.1007/978-3-319-50017-1
- Chen, Li M., Su, Zhixun, Jiang, Bo, Mathematical Problems in Data Science: Theoretical and Practical Methods, Springer, 2015, DOI 10.1007/978-3-319-25127-1
- Steele, Brian, Chandler, John, Reddy, Swarna, Algorithms for Data Science, Springer, 2016, DOI 10.1007/978-3-319-45797-0
- Akerkar, Rajendra, Sajja, Priti Srinivas, Intelligent Techniques for Data Science Springer, 2016, DOI 10.1007/978-3-319-29206-9
- Runkler, Thomas A., Data Analytics, Models and Algorithms for Intelligent Data Analysis, Springer, 2016 DOI 10.1007/978-3-658-14075-5
- Albert-László Barabási, Márton Pósfai, Network Science, Cambridge University Press, 2016, http://networksciencebook.com
- Stratos Idreos et al., The Periodic Table of Data Structures, https://stratos.seas.harvard.edu/files/stratos/files/periodictabledatastructures.pdf
Developing Data Engineering Skills
- Data Engineering Cookbook, https://github.com/andkret/Cookbook
- Probability Distributions in Data Science, https://towardsdatascience.com/probability-distributions-in-data-science-cce6e64873a7
- Mathematics for Machine Learning, https://mml-book.github.io/book/mml-book.pdf
Interesting references
- Liu, L. and Özsu, M.T., 2019. Encyclopedia of database systems. New York, NY, USA:: Springer. https://drive.google.com/file/d/1ZvoL8PgJf1SB6hdFrcft0F6ab63mFh1Y/view
Useful cheat sheets
- SQL Cheat Sheet
- Python for Data Analytics Cheat Sheets
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