This chapter kicks the course off by reviewing conditional probabilities, Bayes' theorem, and central limit theorem. Along the way, you will learn how to handle questions that work with commonly referenced probability distributions.

Conditional probabilities

Setting up problems

Bayes' theorem applied

Central limit theorem

Samples from a rolled die

Simulating central limit theorem

Probability distributions

Bernoulli distribution

Binomial distribution

Normal distribution

Probability and Sampling Distributions

In this chapter, you will prepare for statistical concepts related to exploratory data analysis. The topics include descriptive statistics, dealing with categorical variables, and relationships between variables.  The exercises will prepare you for an analytical assessment or stats-based coding question.

Descriptive statistics

Mean or median

Standard deviation by hand

Categorical data

Encoding techniques

Exploring laptop prices

Two or more variables

Types of relationships

Pearson correlation

Sensitivity to outliers

Exploratory Data Analysis

Prepare to dive deeper into crucial concepts regarding experiments and testing by reviewing confidence intervals, hypothesis testing, multiple tests, and the role that power and sample size play. We'll also discuss types of errors, and what they mean in practice.

Confidence intervals

Confidence interval by hand

Applying confidence intervals

Hypothesis testing

One tailed z-test

Two tailed t-test

Power and sample size

Effect on type II error

Calculating sample size

Visualizing the relationship

Multiple testing

Calculating error rates

Bonferroni correction

Statistical Experiments and Significance Testing

Wrapping up, we'll address concepts related closely to regression and classification models. The chapter begins by reviewing fundamental machine learning algorithms and quickly ramps up to model evaluation, dealing with special cases, and the bias-variance tradeoff. 

Modelli di regressione

Regressione lineare

Regressione logistica

Valutare i modelli

Valutazione della regressione

Valutazione della classificazione

Dati mancanti e valori anomali

Gestire i valori null

Individuare gli outlier

Compromesso bias-varianza

Errore di test e di training

Visualizzare il trade-off

Tiriamo le fila

Regression and Classification

Vuoi ottenere il prossimo lavoro o affinare le tue abilità per i colloqui di statistica per restare al top? Preparati a padroneggiare i classici concetti da colloquio, dalle probabilità condizionate all’A/B testing fino al compromesso bias-varianza, e molto altro! Lavorerai con una raccolta eterogenea di insiemi di dati, tra cui risultati di esperimenti sul web e dati meteo australiani. Al termine del corso, potrai affrontare con sicurezza il tuo prossimo colloquio e rispondere a qualsiasi domanda di statistica con l’aiuto di Python!

Hypothesis Testing in Python

Supervised Learning with scikit-learn

Prepara il tuo colloquio di statistica ripassando probabilità condizionali, test A/B, bias-varianza.

Esercitarsi con le domande di statistica per i colloqui in Python

Preparati per il tuo prossimo colloquio di statistica ripassando concetti come probabilità condizionate, test A/B, compromesso bias-varianza e altro ancora. 

Errore di test e di training

Esercitarsi con le domande di statistica per i colloqui in Python

Esercizio pratico interattivo