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. 

Regressiemodellen

Lineaire regressie

Logistic regression

Modellen evalueren

Beoordeling van regressie

Evaluatie van classificatie

Ontbrekende gegevens en uitschieters

Omgaan met null-waarden

Uitschieters identificeren

Bias-variance-afruil

Test- en trainingsfout

De trade-off visualiseren

Afronding

Regression and Classification

Wil je die volgende baan binnenhalen of je statistiek-skills voor sollicitaties aanscherpen? Maak je klaar om klassieke interviewthema’s te beheersen, van conditionele kansen tot A/B-testen en de bias-variance trade-off, en nog veel meer! Je werkt met een gevarieerde set gegevenssets, waaronder resultaten van webexperimenten en Australische weerdata. Na deze cursus stap je vol vertrouwen je volgende gesprek in en pak je elke statistiekvraag aan met behulp van Python!

Hypothesis Testing in Python

Supervised Learning with scikit-learn

Bereid je voor op je statistiekgesprek: herzie kanscondities, A/B-testen, bias-variantie en meer.

Statistiek-vragen voor sollicitaties oefenen in Python

Uitschieters identificeren

Statistiek-vragen voor sollicitaties oefenen in Python

Praktische interactieve oefening