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.

Interval kepercayaan

Interval kepercayaan secara manual

Menerapkan interval kepercayaan

Pengujian hipotesis

Uji z satu ekor

Uji t dua ekor

Daya dan ukuran sampel

Dampak pada galat Tipe II

Menghitung ukuran sampel

Memvisualisasikan hubungan

Pengujian berganda

Menghitung laju kesalahan

Koreksi Bonferroni

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. 

Regression models

Linear regression

Logistic regression

Evaluating models

Regression evaluation

Classification evaluation

Missing data and outliers

Handling null values

Identifying outliers

Bias-variance tradeoff

Test and training error

Visualizing the tradeoff

Wrapping up

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.

Pengujian hipotesis

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