reading-material

Books, articles, papers etc. about Data Science.

Roadmap to Data Science:

• as a map, with resources: https://github.com/MrMimic/data-scientist-roadmap
• as an article: https://towardsdatascience.com/a-long-term-data-science-roadmap-which-wont-help-you-become-an-expert-in-only-several-months-4436733e63ff

Job Interviews:

Data Science Machine Learning Job Interview Guide

• https://towardsdatascience.com/data-science-interview-guide-4ee9f5dc778

Comprehensive Data Science Guide (questions asked in interviews)

• https://www.analyticsvidhya.com/blog/2018/06/comprehensive-data-science-machine-learning-interview-guide/

Stack Exchange / Overflow questions:

• Equality of t-test and Anova

  • https://stats.stackexchange.com/questions/1637/if-the-t-test-and-the-anova-for-two-groups-are-equivalent-why-arent-their-assu

• Anova is Linear Regression

  • https://stats.stackexchange.com/questions/175246/why-is-anova-equivalent-to-linear-regression

• How does an artificial neural network work?

  • https://stats.stackexchange.com/questions/63152/what-does-the-hidden-layer-in-a-neural-network-compute

Statistics:

• Statistical Tests

  • https://keydifferences.com/difference-between-parametric-and-nonparametric-test.html

  • https://en.wikipedia.org/wiki/Location_test

  • Understanding t-tests: http://blog.minitab.com/blog/adventures-in-statistics-2/understanding-t-tests-1-sample-2-sample-and-paired-t-tests

• Anova

• https://www.quora.com/What-is-%E2%80%9Cexplained-variation%E2%80%9D-%E2%80%9Cunexplained-variation%E2%80%9D-%E2%80%9Ctotal-variation%E2%80%9D-using-regression-line

Machine Learning in general:

• Hyperparameters: https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning)

Regularization

• Bayesian Interpretation of Regularization http://bjlkeng.github.io/posts/probabilistic-interpretation-of-regularization/

Random Forests:

• https://towardsdatascience.com/random-forest-in-python-24d0893d51c0

Visualization:

• Tukey test visualized https://www.r-graph-gallery.com/84-tukey-test/

Data Sets:

• https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research

Robust Machine Learning:

• https://towardsdatascience.com/the-three-pillars-of-robust-machine-learning-specification-testing-robust-training-and-formal-51c1c6192f8

Tools:

• Automated Feature Engineering in Python:

  • https://www.featuretools.com/

• Visualization tools

  • http://www.visualisingdata.com/resources/