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. 

Model regresi

Regresi linear

Regresi logistik

Mengevaluasi model

Evaluasi regresi

Evaluasi klasifikasi

Data hilang dan nilai pencilan

Menangani nilai null

Mengidentifikasi pencilan (outlier)

Pertukaran bias-variasi

Galat uji dan latih

Memvisualisasikan trade-off

Penutup

Regression and Classification

Apakah Anda ingin mendapatkan pekerjaan berikutnya atau mengasah keterampilan wawancara statistik agar tetap tajam? Bersiaplah untuk menguasai konsep wawancara klasik mulai dari probabilitas bersyarat, A/B testing, hingga bias-variance tradeoff, dan banyak lagi! Anda akan bekerja dengan beragam himpunan data termasuk hasil eksperimen berbasis web dan data cuaca Australia. Setelah mengikuti kursus ini, Anda akan dapat menghadapi wawancara berikutnya dengan percaya diri dan menaklukkan pertanyaan statistik apa pun dengan bantuan Python!

Hypothesis Testing in Python

Supervised Learning with scikit-learn

Siapkan wawancara statistik dengan meninjau probabilitas bersyarat, uji A/B, dan bias-varian.

Data hilang dan nilai pencilan

Create Your Free Account