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Practicing Statistics Interview Questions in R

In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.

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4 Stunden16 Videos50 Übungen3.838 LernendeTrophyLeistungsnachweis

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Kursbeschreibung

Are you job interview ready? You may know everything there is to know about your target company, but have you practiced the classic R statistical interview questions? If not, we have you covered. In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more. You’ll sharpen your skills using datasets including Parkinson’s disease data and gas prices. This course is purposely more challenging than a typical DataCamp course to make sure that when it comes to interviewing time you’re ready to confidently tackle any statistics interview question in R.
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  1. 1

    Probability Distributions

    Kostenlos

    Want to increase your odds of acing your job interview? If so, brush up on your knowledge of probability theory. In this chapter, we'll roll dice and shoot baskets to explain probabilities using real-life examples.

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    Discrete distributions
    50 xp
    Probability functions
    100 xp
    Bernoulli trials
    100 xp
    Binomial distribution
    100 xp
    Continuous distributions
    50 xp
    Uniform distribution
    100 xp
    Shape of normal distribution
    100 xp
    Sample from normal distribution
    100 xp
    Central limit theorem
    50 xp
    Law of large numbers
    100 xp
    Simulating central limit theorem
    100 xp
  2. 2

    Exploratory Data Analysis

    If the job description appeals to you review descriptive statistics before the interview. In this chapter, you will practice exploratory data analysis (EDA) using natural gas prices and data from a survey analysis.

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    Descriptive statistics
    50 xp
    Centrality measures
    100 xp
    Variability measures
    100 xp
    Categorical data
    50 xp
    Survey analysis
    100 xp
    Data encoding
    100 xp
    Time series
    50 xp
    Time series object
    100 xp
    Wrangling time series
    100 xp
    Principal Component Analysis
    50 xp
    PCA - rotation
    100 xp
    PCA - dimension reduction
    100 xp
  3. 3

    Statistical Tests

    March confidently into your job interview after reviewing confidence intervals. We'll review the t-test, ANOVA, and normality tests to prepare you for statistics-based coding questions.

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    Normality tests
    50 xp
    Shapiro-Wilk test
    100 xp
    Q-Q plot
    100 xp
    Inference for a mean
    50 xp
    Confidence interval
    100 xp
    One-sample t-test
    100 xp
    Comparing two means
    50 xp
    Two-sample t-test
    100 xp
    Paired test
    100 xp
    ANOVA
    50 xp
    Comparing groups
    100 xp
    ANOVA for plant growth
    100 xp
  4. 4

    Regression Models

    Is your potential employer planning to test your R skills? Make sure you’re prepared and practice model evaluation beforehand. In this chapter, we will fit and evaluate linear and logistic regression models using various biomedical datasets. By the end of this chapter, you’ll be fully prepared to answer any question the interviewer throws your way!

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    Covariance and correlation
    50 xp
    Covariance by hand
    100 xp
    Linear relationship
    100 xp
    Nonlinear relationship
    100 xp
    Linear regression model
    50 xp
    Fitting linear models
    100 xp
    Predicting with linear models
    100 xp
    Logistic regression model
    50 xp
    Fitting logistic models
    100 xp
    Predicting with logistic models
    100 xp
    Model evaluation
    50 xp
    Validation set approach
    100 xp
    Regression evaluation
    100 xp
    Classification evaluation
    100 xp
    Wrapping up
    50 xp
Für Unternehmen

Trainierst du 2 oder mehr?

Verschaffen Sie Ihrem Team Zugriff auf die vollständige DataCamp-Plattform, einschließlich aller Funktionen.

Datensätze

Natural GasGold monthlyParkinson's DataLetter Recognition

Mitwirkende

Collaborator's avatar
Anneleen Beckers
Collaborator's avatar
Maggie Matsui

Voraussetzungen

Intermediate RFoundations of Inference in RFoundations of Probability in R
Zuzanna Chmielewska HeadshotZuzanna Chmielewska

Actuary

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