1. Aprender
    2. /
  2. Cursos
    3. /
  3. Practicing Statistics Interview Questions in Python
  • 1

    Probability and Sampling Distributions

    Gratuito

    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.

  • 2

    Exploratory Data Analysis

    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.

  • 3

    Statistical Experiments and Significance Testing

    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.

  • 4

    Regression and Classification

    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.

Initializing

Exercicio

Logistic regression

Let's move on to logistic regression. You'll be working with the same weather dataset again, but the goal here is to predict if it's going to rain tomorrow. We've gone ahead and created your train and test sets for you. Your dependent variables are the Humidity9am and Humidity3pm features.

It's also worth noting that the dataset has already been normalized in order to ensure that we can interpret the coefficients later on. This is always good to bring up during your interview when talking about regression for inference.

Instruções 1/3

undefined XP
    1
    2
    3
  • Create and fit your logistic regression model using the X_train and y_train variables.