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# AB Testing |
# AB Testing |
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- [Practitioner’s Guide to Statistical Tests](https://vkteam.medium.com/practitioners-guide-to-statistical-tests-ed2d580ef04f#1e3b) |
- [@article@Practitioner’s Guide to Statistical Tests](https://vkteam.medium.com/practitioners-guide-to-statistical-tests-ed2d580ef04f#1e3b) |
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- [Step by Step Process for Planning an A/B Test](https://towardsdatascience.com/step-by-step-for-planning-an-a-b-test-ef3c93143c0b) |
- [@article@Step by Step Process for Planning an A/B Test](https://towardsdatascience.com/step-by-step-for-planning-an-a-b-test-ef3c93143c0b) |
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# Classic/Advanced ML |
# Classic/Advanced ML |
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- [Open Machine Learning Course](https://mlcourse.ai/book/topic01/topic01_intro.html) |
- [@article@Open Machine Learning Course](https://mlcourse.ai/book/topic01/topic01_intro.html) |
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- [Coursera: Machine Learning Specialization](https://imp.i384100.net/oqGkrg) |
- [@article@Coursera: Machine Learning Specialization](https://imp.i384100.net/oqGkrg) |
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- [Pattern Recognition and Machine Learning by Christopher Bishop](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf) |
- [@article@Pattern Recognition and Machine Learning by Christopher Bishop](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf) |
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- [Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop](https://github.com/gerdm/prml) |
- [@opensource@Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop](https://github.com/gerdm/prml) |
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# Data Understanding, Analysis and Visualization |
# Data Understanding, Analysis and Visualization |
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- [Exploratory Data Analysis With Python and Pandas](https://imp.i384100.net/AWAv4R) |
- [@article@Exploratory Data Analysis With Python and Pandas](https://imp.i384100.net/AWAv4R) |
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- [Exploratory Data Analysis for Machine Learning](https://imp.i384100.net/GmQMLE) |
- [@article@Exploratory Data Analysis for Machine Learning](https://imp.i384100.net/GmQMLE) |
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- [Exploratory Data Analysis with Seaborn](https://imp.i384100.net/ZQmMgR) |
- [@article@Exploratory Data Analysis with Seaborn](https://imp.i384100.net/ZQmMgR) |
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# MLOps |
# MLOps |
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- [Machine Learning Engineering for Production (MLOps) Specialization](https://imp.i384100.net/nLA5mx) |
- [@article@Machine Learning Engineering for Production (MLOps) Specialization](https://imp.i384100.net/nLA5mx) |
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- [Full Stack Deep Learning](https://fullstackdeeplearning.com/course/2022/) |
- [@article@Full Stack Deep Learning](https://fullstackdeeplearning.com/course/2022/) |
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# Differential Calculus |
# Differential Calculus |
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- [Algebra and Differential Calculus for Data Science](https://imp.i384100.net/LX5M7M) |
- [@article@Algebra and Differential Calculus for Data Science](https://imp.i384100.net/LX5M7M) |
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# Econometrics Pre-requisites |
# Econometrics Pre-requisites |
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- [10 Fundamental Theorems for Econometrics](https://bookdown.org/ts_robinson1994/10EconometricTheorems/) |
- [@article@10 Fundamental Theorems for Econometrics](https://bookdown.org/ts_robinson1994/10EconometricTheorems/) |
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# Fully Connected NN, CNN, RNN, LSTM, Transformers, Transfer Learning |
# Fully Connected NN, CNN, RNN, LSTM, Transformers, Transfer Learning |
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- [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) |
- [@article@The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) |
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- [Attention is All you Need](https://arxiv.org/pdf/1706.03762.pdf) |
- [@article@Attention is All you Need](https://arxiv.org/pdf/1706.03762.pdf) |
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- [Deep Learning Book](https://www.deeplearningbook.org/) |
- [@article@Deep Learning Book](https://www.deeplearningbook.org/) |
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- [Deep Learning Specialization](https://imp.i384100.net/Wq9MV3) |
- [@article@Deep Learning Specialization](https://imp.i384100.net/Wq9MV3) |
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# Hypothesis Testing |
# Hypothesis Testing |
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- [Introduction to Statistical Analysis: Hypothesis Testing](https://imp.i384100.net/vN0JAA) |
- [@article@Introduction to Statistical Analysis: Hypothesis Testing](https://imp.i384100.net/vN0JAA) |
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# Increasing Test Sensitivity |
# Increasing Test Sensitivity |
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- [Minimum Detectable Effect (MDE)](https://splitmetrics.com/resources/minimum-detectable-effect-mde/) |
- [@article@Minimum Detectable Effect (MDE)](https://splitmetrics.com/resources/minimum-detectable-effect-mde/) |
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- [Improving the Sensitivity of Online Controlled Experiments: Case Studies at Netflix](https://kdd.org/kdd2016/papers/files/adp0945-xieA.pdf) |
- [@article@Improving the Sensitivity of Online Controlled Experiments: Case Studies at Netflix](https://kdd.org/kdd2016/papers/files/adp0945-xieA.pdf) |
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- [Improving the Sensitivity of Online Controlled Experiments by Utilizing Pre-Experiment Data](https://exp-platform.com/Documents/2013-02-CUPED-ImprovingSensitivityOfControlledExperiments.pdf) |
- [@article@Improving the Sensitivity of Online Controlled Experiments by Utilizing Pre-Experiment Data](https://exp-platform.com/Documents/2013-02-CUPED-ImprovingSensitivityOfControlledExperiments.pdf) |
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- [How Booking.com increases the power of online experiments with CUPED](https://booking.ai/how-booking-com-increases-the-power-of-online-experiments-with-cuped-995d186fff1d) |
- [@article@How Booking.com increases the power of online experiments with CUPED](https://booking.ai/how-booking-com-increases-the-power-of-online-experiments-with-cuped-995d186fff1d) |
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- [Improving Experimental Power through Control Using Predictions as Covariate — CUPAC](https://doordash.engineering/2020/06/08/improving-experimental-power-through-control-using-predictions-as-covariate-cupac/) |
- [@article@Improving Experimental Power through Control Using Predictions as Covariate — CUPAC](https://doordash.engineering/2020/06/08/improving-experimental-power-through-control-using-predictions-as-covariate-cupac/) |
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- [Improving the Sensitivity of Online Controlled Experiments: Case Studies at Netflix](https://www.researchgate.net/publication/305997925_Improving_the_Sensitivity_of_Online_Controlled_Experiments_Case_Studies_at_Netflix) |
- [@article@Improving the Sensitivity of Online Controlled Experiments: Case Studies at Netflix](https://www.researchgate.net/publication/305997925_Improving_the_Sensitivity_of_Online_Controlled_Experiments_Case_Studies_at_Netflix) |
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# Data Structures and Algorithms |
# Data Structures and Algorithms |
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- [Learn Algorithms](https://leetcode.com/explore/learn/) |
- [@article@Learn Algorithms](https://leetcode.com/explore/learn/) |
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- [Leetcode - Study Plans](https://leetcode.com/studyplan/) |
- [@article@Leetcode - Study Plans](https://leetcode.com/studyplan/) |
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- [Algorithms Specialization](https://imp.i384100.net/5gqv4n) |
- [@article@Algorithms Specialization](https://imp.i384100.net/5gqv4n) |
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# Python |
# Python |
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- [Kaggle — Python](https://www.kaggle.com/learn/python) |
- [@article@Kaggle — Python](https://www.kaggle.com/learn/python) |
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- [Google's Python Class](https://developers.google.com/edu/python) |
- [@article@Google's Python Class](https://developers.google.com/edu/python) |
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# SQL |
# SQL |
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- [SQL Tutorial](https://www.sqltutorial.org/) |
- [@article@SQL Tutorial](https://www.sqltutorial.org/) |
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# Learn Algebra, Calculus, Mathematical Analysis |
# Learn Algebra, Calculus, Mathematical Analysis |
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- [Mathematics for Machine Learning Specialization](https://imp.i384100.net/baqMYv) |
- [@article@Mathematics for Machine Learning Specialization](https://imp.i384100.net/baqMYv) |
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- [Linear Algebra Youtube Course](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) |
- [@video@Linear Algebra Youtube Course](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) |
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# Probability and Sampling |
# Probability and Sampling |
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- [Probability and Statistics: To p or not to p?](https://imp.i384100.net/daDM6Q) |
- [@article@Probability and Statistics: To p or not to p?](https://imp.i384100.net/daDM6Q) |
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# Ratio Metrics |
# Ratio Metrics |
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- [Applying the Delta Method in Metric Analytics: A Practical Guide with Novel Ideas](https://arxiv.org/pdf/1803.06336.pdf) |
- [@article@Applying the Delta Method in Metric Analytics: A Practical Guide with Novel Ideas](https://arxiv.org/pdf/1803.06336.pdf) |
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- [Approximations for Mean and Variance of a Ratio](https://www.stat.cmu.edu/~hseltman/files/ratio.pdf) |
- [@article@Approximations for Mean and Variance of a Ratio](https://www.stat.cmu.edu/~hseltman/files/ratio.pdf) |
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# Regressions, Time series, Fitting Distributions |
# Regressions, Time series, Fitting Distributions |
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- [10 Fundamental Theorems for Econometrics](https://bookdown.org/ts_robinson1994/10EconometricTheorems/) |
- [@article@10 Fundamental Theorems for Econometrics](https://bookdown.org/ts_robinson1994/10EconometricTheorems/) |
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- [Dougherty Intro to Econometrics 4th edition](https://www.academia.edu/33062577/Dougherty_Intro_to_Econometrics_4th_ed_small) |
- [@article@Dougherty Intro to Econometrics 4th edition](https://www.academia.edu/33062577/Dougherty_Intro_to_Econometrics_4th_ed_small) |
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- [Econometrics: Methods and Applications](https://imp.i384100.net/k0krYL) |
- [@article@Econometrics: Methods and Applications](https://imp.i384100.net/k0krYL) |
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- [Kaggle - Learn Time Series](https://www.kaggle.com/learn/time-series) |
- [@article@Kaggle - Learn Time Series](https://www.kaggle.com/learn/time-series) |
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- [Time series Basics : Exploring traditional TS](https://www.kaggle.com/code/jagangupta/time-series-basics-exploring-traditional-ts#Hierarchical-time-series) |
- [@article@Time series Basics : Exploring traditional TS](https://www.kaggle.com/code/jagangupta/time-series-basics-exploring-traditional-ts#Hierarchical-time-series) |
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- [How to Create an ARIMA Model for Time Series Forecasting in Python](https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python) |
- [@article@How to Create an ARIMA Model for Time Series Forecasting in Python](https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python) |
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- [11 Classical Time Series Forecasting Methods in Python](https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/) |
- [@article@11 Classical Time Series Forecasting Methods in Python](https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/) |
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- [Blockchain.com Data Scientist TakeHome Test](https://github.com/stalkermustang/bcdc_ds_takehome) |
- [@opensource@Blockchain.com Data Scientist TakeHome Test](https://github.com/stalkermustang/bcdc_ds_takehome) |
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- [Linear Regression for Business Statistics](https://imp.i384100.net/9g97Ke) |
- [@article@Linear Regression for Business Statistics](https://imp.i384100.net/9g97Ke) |
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# Statistics, CLT |
# Statistics, CLT |
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- [Introduction to Statistics](https://imp.i384100.net/3eRv4v) |
- [@article@Introduction to Statistics](https://imp.i384100.net/3eRv4v) |
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# Zones |
# Zones |
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- [Angular - NgZone](https://angular.io/guide/zone) |
- [@official@Angular - NgZone](https://angular.io/guide/zone) |
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