Data science and statistics
Clean data, explore it and draw conclusions you can defend.
30tutors
7teachers
3free to start
$3 to $12per paid lesson
Data science and statistics tutors
30 tutors
Monte Carlo Simulation for Intuition
Monte Carlo Simulation for Intuition
Answer tricky probability and planning questions by simulating them thousands of timesKenta Arai$4Bayesian Thinking for AnalystsBayesian Thinking for Analysts
Update beliefs with data using priors, likelihoods and posteriors you can explainLina Khoury$7Causal Inference BasicsCausal Inference Basics
Estimate causal effects from observational data and state your assumptions out loudKojo Amankwah$12Topic discovery and text clusteringTopic discovery and text clustering
Find themes in large text collections and check that the topics you found are real and usefulNadia Haddad$7Probability Puzzles and ParadoxesProbability Puzzles and Paradoxes
Sharpen your reasoning with famous puzzles, then settle every argument by counting or simulatingKenta Arai$5Sequential Testing and the Peeking ProblemSequential Testing and the Peeking Problem
Monitor experiments early and often without inflating your false positive rateKojo Amankwah$11Teachers who teach Data science and statistics
They wrote the lesson plans these tutors follow.
Nadia Haddad
Practical NLP: from tokens and embeddings to classification, translation and speechtokenisation, embeddings, text classification9 tutorsSampleLukas Brenner
Model evaluation you can trust: splits, metrics, leakage and tuningtrain validation test splits, cross validation, overfitting9 tutorsSampleKojo Amankwah
Experiments, causal questions and responsible models, explained for decision makersA/B testing, experiment design, statistical power9 tutorsSampleLin Zhao
Data cleaning, SQL, exploratory analysis and honest chartsdata cleaning, exploratory data analysis, SQL9 tutorsSampleKenta Arai
Probability for machine learning, plus forecasting and anomaly detectionprobability, expectation and variance, maximum likelihood9 tutorsSampleLina Khoury
Statistics in plain language, from averages to Bayesian reasoningdescriptive statistics, probability, distributions9 tutorsSampleMore in Machine learning
- Machine learning How models learn from data: training, validation, overfitting and the classic methods. 35 tutors
- Math for AI The linear algebra, calculus and probability behind modern models, one idea at a time. 23 tutors
- Deep learning Neural networks from single neurons to full training loops, with worked examples. 28 tutors
- NLP Work with text: tokens, embeddings, classification, translation and speech. 14 tutors
- Computer vision Models that see: classification, detection, segmentation and their limits. 14 tutors
- Fine tuning and training Adapt a model to your task with good data and careful training runs. 12 tutors
- MLOps and deployment Ship models to production and keep them healthy: serving, monitoring, updates. 18 tutors