Simple explanations of data science and statistics concepts

One of my favourite data science resources is the mini-episode series of the Data Skeptic podcast. These short episodes would feature the host explaining a data science concept to a non-expert in plain English.

I wanted to share a few of these with some colleagues from work and thought I’d catalogue them here.

• Machine learning
  • Decision Tree Learning
  • Random Forest
  • Gini Coefficient
  • k-means clustering
  • k-Nearest Neighbors
  • The Elbow Method
  • AdaBoost
  • Calculating Feature Importance
  • Cross Validation
  • F1 Score
  • Receiver Operating Characteristic (ROC) Curve

• Correlation/Regression
  • Covariance and Correlation
  • Ordinary Least Squares Regression
  • R-squared
  • Multiple Regression
  • Logistic Regression on Audio Data
  • Heteroskedasticity

• Primer on Deep Learning
  • The Perceptron
  • Activation Functions
  • Backpropagation
  • Feed Forward Neural Networks
  • Convolutional Neural Networks
  • Long Short Term Memory
  • Recurrent Neural Networks
  • Generative Adversarial Networks
  • Reinforcement Learning
  • The Vanishing Gradient
  • Max Pooling
  • Dropout

• Markov Chains
  • Markov Chain Monte Carlo
  • Markov Decision Processes

• Time Series
  • Stationarity and Differencing
  • Auto-correlative functions and correlograms

• Bayes
  • Spam Filtering with Naive Bayes
  • Bayesian Updating
  • Bayesian Belief Networks

• Statistics
  • The T-Test
  • p-values
  • z-scores
  • The Chi-Squared Test
  • ANOVA
  • The Bonferroni Correction
  • Sample Sizes
  • Confidence Intervals
  • The Bootstrap

• Text
  • Natural Language Processing
  • Term Frequency – Inverse Document Frequency

• Concepts
  • The Central Limit Theorem
  • The Curse of Dimensionality
  • Type i / Type ii Errors
  • Partially Observable State Spaces
  • The Accuracy Paradox
  • Bias Variance Tradeoff
  • Survival Analysis
  • Multi-armed Bandit Problems
  • Distance Measures
  • Structured and Unstructured Data
  • Anscombe’s Quartet
  • Selection Bias
  • Noise
  • Ant Colony Optimization
  • Conditional Independence
  • Entropy
  • Leakage
  • Goodhart’s Law
  • GPU CPU
  • Parallel Algorithms
  • k-d trees
  • Automated Feature Engineering

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• Bio
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Jack Simpson

Currently fascinated by energy markets and electrical engineering. In another life I was a beekeeper that did a PhD in computational biology writing image analysis software and using machine learning to quantify honeybee behaviour in the hive.

Latest posts by Jack Simpson (see all)

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• Optimisation and Energy Modelling Talks From JuliaCon 2021 - July 29, 2021
• How does AEMO predict demand in the National Electricity Market? - July 26, 2021

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