ksatola/data-science-notes

Various resources on advanced analytics and beyond

Data-Science

Various resources on advanced analytics and beyond

This material is work-in-progress, only parts annotated with (done) can be consider complete (but may be extended in the future).

TODO: classify use cases (regression, classification, clustering, etc.)

To review:

https://www.datasciencecentral.com/profiles/blogs/k-nearest-neighbor-algorithm-using-python https://www.datasciencecentral.com/profiles/blogs/eight-levels-of-analytics-for-competitive-advantage https://www.datasciencecentral.com/profiles/blogs/difference-between-correlation-and-regression-in-statistics https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/ https://www.datasciencecentral.com/profiles/blogs/choosing-features-for-random-forests-algorithm https://www.datasciencecentral.com/profiles/blogs/linear-regression-geometry https://www.datasciencecentral.com/profiles/blogs/big-data-sets-available-for-free https://towardsdatascience.com/markov-chain-analysis-and-simulation-using-python-4507cee0b06e https://machinelearningmastery.com/a-gentle-introduction-to-normality-tests-in-python/ https://www.analyticsvidhya.com/blog/2019/08/5-applications-singular-value-decomposition-svd-data-science/ https://www.analyticsvidhya.com/blog/2019/08/detailed-guide-7-loss-functions-machine-learning-python-code https://towardsdatascience.com/beyond-accuracy-precision-and-recall-3da06bea9f6c https://towardsdatascience.com/histograms-and-density-plots-in-python-f6bda88f5ac0 https://towardsdatascience.com/how-to-out-compete-on-a-data-science-competition-insights-techniques-and-tactics-95a0545041d5 https://docs.featuretools.com/en/stable/# https://towardsdatascience.com/data-science-interview-guide-4ee9f5dc778 https://nbviewer.jupyter.org/ https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks https://github.com/chrisalbon/code_py https://github.com/abhat222 https://github.com/abhat222/Data-Science-Tutorials https://github.com/practicalAI/practicalAI https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python https://www.datasciencecentral.com/profiles/blogs/model-evaluation-techniques-in-one-picture https://python-graph-gallery.com/bubble-plot/ https://machinelearningmastery.com/category/algorithms-from-scratch/ https://machinelearningmastery.com/calculate-principal-component-analysis-scratch-python/ https://github.com/propublica/compas-analysis https://machinelearningmastery.com/a-gentle-introduction-to-the-bootstrap-method/ https://machinelearningmastery.com/what-is-information-entropy/

Topics

block quote

• Machine Learning

  • Supervised Learning

    • Decision Trees
      • CART
      • Ensemble Learning
        • Voting Classifier
        • Bagging
        • Random Forests
      • The Bias-Variance Tradeoff
      • Boosting
        • Ada Boost
        • Gradient Boosting
        • Stochastic Gradient Boosting
      • Model Tuning (Hyper Parameter Tuning)

  • Deep Learning

    • Regression problems
    • Forward propagation
    • Gradient Descent
    • Backpropagation
    • Classification problems

  • Unsupervised Learning

---

Topics Alphabetically

• The Curse of Dimensionality

---

Data and Big Data

• (done) Data Lake Maturity Model - the first thing needed for analytics is data. It should be complete, trustful, well governed and easily used by anyone needed to make data-driven decisions.

Python

• PyFormat - Using % and .format() for great good

Data Analysis and Cleaning

• Imbalanced dataset

  • Resampling EDA

• Accuracy paradox

Machile Learning

• (done) K-Nearest Neighbors

Tutorials, Trainings, Communities

• chrisalbon.com - Technical notes on using data science & artificial intelligence to fight for something that matters

• www.analyticsvidhya.com - Learn everything about analytics

• Python for Data Analysis Index - Technical notes on Python for data analysis

• 40+ Python Statistics For Data Science Resources - A list of Python resources for the eight statistics topics that you need to know to excel in data science

• Think Stats - Probability and Statistics for Programmers (free ebook)

• DASCA - Data Scientist Certifications