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

Name		Name	Last commit message	Last commit date
Latest commit History 1,403 Commits
.github		.github
dash		dash
data		data
dc_projects/001 - Exploring 67 years of LEGO		dc_projects/001 - Exploring 67 years of LEGO
final_python		final_python
images		images
ml_python_harrison		ml_python_harrison
r		r
scratch		scratch
spark		spark
word_cloud		word_cloud
.gitattributes		.gitattributes
.gitignore		.gitignore
010_Data_Science.ipynb		010_Data_Science.ipynb
020_Data_Science_Roles.ipynb		020_Data_Science_Roles.ipynb
030_Data_Driven_Decisions.ipynb		030_Data_Driven_Decisions.ipynb
040_Data_Science_Process.ipynb		040_Data_Science_Process.ipynb
050_Data_Analysis.ipynb		050_Data_Analysis.ipynb
051_Outliers.ipynb		051_Outliers.ipynb
060_Stats_Introduction_to_Statistics.ipynb		060_Stats_Introduction_to_Statistics.ipynb
062_Stats_Data_and_Graphs_Types_and_Summary_Statistics.ipynb		062_Stats_Data_and_Graphs_Types_and_Summary_Statistics.ipynb
063_Stats_Probability_Distributions.ipynb		063_Stats_Probability_Distributions.ipynb
064_Stats_Inferential_Statistics.ipynb		064_Stats_Inferential_Statistics.ipynb
065_Stats_The_Scientific_Method.ipynb		065_Stats_The_Scientific_Method.ipynb
066_Stats_Computational_Statistics.ipynb		066_Stats_Computational_Statistics.ipynb
070_Machine_Learning.ipynb		070_Machine_Learning.ipynb
075_Gradient_Descent.ipynb		075_Gradient_Descent.ipynb
090_Features.ipynb		090_Features.ipynb
100_Imbalanced_Data.ipynb		100_Imbalanced_Data.ipynb
105_Learning_Curve.ipynb		105_Learning_Curve.ipynb
110_Overfitting_Underfitting.ipynb		110_Overfitting_Underfitting.ipynb
111_Cross_Validation_Hyper_Parameter_Search.ipynb		111_Cross_Validation_Hyper_Parameter_Search.ipynb
112_Bias_Variance_Trade_Off.ipynb		112_Bias_Variance_Trade_Off.ipynb
120_Data_Preprocessing_and_Normalization.ipynb		120_Data_Preprocessing_and_Normalization.ipynb
125_Dimensionality_Reduction.ipynb		125_Dimensionality_Reduction.ipynb
130_The_Curse_of_Dimensionality.ipynb		130_The_Curse_of_Dimensionality.ipynb
135_The_Bootstrap.ipynb		135_The_Bootstrap.ipynb
300_Python_Data_Visualization.ipynb		300_Python_Data_Visualization.ipynb
302_Python_Data_Visualization_Bokeh.ipynb		302_Python_Data_Visualization_Bokeh.ipynb
304-1_Python_Data_Visualization_Plotly_Dash.ipynb		304-1_Python_Data_Visualization_Plotly_Dash.ipynb
304-2_Python_Data_Visualization_Plotly_Dash.ipynb		304-2_Python_Data_Visualization_Plotly_Dash.ipynb
310_Python_Basics.ipynb		310_Python_Basics.ipynb
312_Python_Functions and Error Handling.ipynb		312_Python_Functions and Error Handling.ipynb
314_Python_Iterators_Comprehensions_Generators.ipynb		314_Python_Iterators_Comprehensions_Generators.ipynb
315_Python_Importing_Data.ipynb		315_Python_Importing_Data.ipynb
316_Python_Cleaning_Data.ipynb		316_Python_Cleaning_Data.ipynb
316x_Case_Study.ipynb		316x_Case_Study.ipynb
320_Python_Pandas_DataFrame.ipynb		320_Python_Pandas_DataFrame.ipynb
321_Python_Pandas_Time_Series.ipynb		321_Python_Pandas_Time_Series.ipynb
321x_Case_Study.ipynb		321x_Case_Study.ipynb
322_Python_Pandas_DataFrame_Manipulation.ipynb		322_Python_Pandas_DataFrame_Manipulation.ipynb
322x_Case_Study.ipynb		322x_Case_Study.ipynb
323_Python_Pandas_DataFrame_Merging.ipynb		323_Python_Pandas_DataFrame_Merging.ipynb
323x_Case_Study.ipynb		323x_Case_Study.ipynb
328_Python_Statistics.ipynb		328_Python_Statistics.ipynb
328x_Case_Study.ipynb		328x_Case_Study.ipynb
330-01_Impact_of_gender_on_police_behavior.ipynb		330-01_Impact_of_gender_on_police_behavior.ipynb
330-02_School_Budgets_Multi-label_Classification.ipynb		330-02_School_Budgets_Multi-label_Classification.ipynb
350_Model_Performance_Metrics.ipynb		350_Model_Performance_Metrics.ipynb
360-1-0_R_Introduction.ipynb		360-1-0_R_Introduction.ipynb
360-1-1_R_Case_Study.R		360-1-1_R_Case_Study.R
360-2-0_R_Data_Wrangling_1.ipynb		360-2-0_R_Data_Wrangling_1.ipynb
360-2-1_R_Case_Study.R		360-2-1_R_Case_Study.R
360-3-0_R_Data_Wrangling_2.ipynb		360-3-0_R_Data_Wrangling_2.ipynb
360-3-1_R_Case_Study-Copy1.R		360-3-1_R_Case_Study-Copy1.R
360-4-0_R_Data_Visualization.ipynb		360-4-0_R_Data_Visualization.ipynb
360-4-1_R_Case_Study.R		360-4-1_R_Case_Study.R
360-4-2_R_Case_Study2.R		360-4-2_R_Case_Study2.R
360-5-0_R_RMarkdown_Dynamic_Reports.ipynb		360-5-0_R_RMarkdown_Dynamic_Reports.ipynb
360-5-1_R_Markdown_Example.Rmd		360-5-1_R_Markdown_Example.Rmd
360-5-1_R_Markdown_Example.html		360-5-1_R_Markdown_Example.html
360-5-2_R_Case_Study.R		360-5-2_R_Case_Study.R
360-5-2_R_Case_Study.html		360-5-2_R_Case_Study.html
360-5-3_R_Case_Study.Rmd		360-5-3_R_Case_Study.Rmd
360-6-0_R_Shiny_Interactive_Web_Applications.ipynb		360-6-0_R_Shiny_Interactive_Web_Applications.ipynb
400_K_Nearest_Neighbors.ipynb		400_K_Nearest_Neighbors.ipynb
405_Simple_Linear_Regression.ipynb		405_Simple_Linear_Regression.ipynb
410_Multiple_Regression.ipynb		410_Multiple_Regression.ipynb
415_Logistic_Regression.ipynb		415_Logistic_Regression.ipynb
420_Support_Vector_Machines.ipynb		420_Support_Vector_Machines.ipynb
430_Neural_Networks.ipynb		430_Neural_Networks.ipynb
432_Deep_Learning.ipynb		432_Deep_Learning.ipynb
435_kMeans_Clustering.ipynb		435_kMeans_Clustering.ipynb
437_Bottom_Up_Hierarchical_Clustering.ipynb		437_Bottom_Up_Hierarchical_Clustering.ipynb
440_Natural Language Processing.ipynb		440_Natural Language Processing.ipynb
442_NLP_WordCloud.ipynb		442_NLP_WordCloud.ipynb
445_Network_Analysis.ipynb		445_Network_Analysis.ipynb
446_Python_Network_Analysis_1.ipynb		446_Python_Network_Analysis_1.ipynb
446x_Case_Study.ipynb		446x_Case_Study.ipynb
447_Python_Network_Analysis_2.ipynb		447_Python_Network_Analysis_2.ipynb
447x_Case_Study.ipynb		447x_Case_Study.ipynb
448_1_0_Python_Timeseries_Analysis.ipynb		448_1_0_Python_Timeseries_Analysis.ipynb
448_2_0_Python_Manipulating_Timeseries.ipynb		448_2_0_Python_Manipulating_Timeseries.ipynb
448_2_1_Case_Study.ipynb		448_2_1_Case_Study.ipynb
448_3_0_Python_Timeseries_Visualization.ipynb		448_3_0_Python_Timeseries_Visualization.ipynb
448_3_1_Case_Study.ipynb		448_3_1_Case_Study.ipynb
448_4_0_Python_ARIMA_Models.ipynb		448_4_0_Python_ARIMA_Models.ipynb
448_5_0_Machine_Learning_for_Time_Series_in_Python.ipynb		448_5_0_Machine_Learning_for_Time_Series_in_Python.ipynb
450_Recommender_Systems.ipynb		450_Recommender_Systems.ipynb
452_Forecasting.ipynb		452_Forecasting.ipynb

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

Data-Science

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

To review:

Topics

Topics Alphabetically

Data and Big Data

Python

Data Analysis and Cleaning

Machile Learning

Tutorials, Trainings, Communities

About

Releases

Packages

Contributors 2

Languages

ksatola/Data-Science-Notes

Folders and files

Latest commit

History

Repository files navigation

Data-Science

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

To review:

Topics

Topics Alphabetically

Data and Big Data

Python

Data Analysis and Cleaning

Machile Learning

Tutorials, Trainings, Communities

About

Resources

Stars

Watchers

Forks

Releases

Packages 0

Contributors 2

Languages

Packages