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Course

Practicing Statistics Interview Questions in R

AdvancedSkill Level

4.7+

Updated 10/2022

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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RProbability & Statistics4 hr16 videos50 Exercises4,200 XP4,613Statement of Accomplishment

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Course Description

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.

Prerequisites

Intermediate R Foundations of Inference in R Foundations of Probability in R

1

Probability Distributions

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.

Discrete distributions

Probability functions

Bernoulli trials

Binomial distribution

Continuous distributions

Uniform distribution

Shape of normal distribution

Sample from normal distribution

Central limit theorem

Law of large numbers

Simulating central limit theorem

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.

Descriptive statistics

Centrality measures

Variability measures

Categorical data

Survey analysis

Data encoding

Time series

Time series object

Wrangling time series

Principal Component Analysis

PCA - rotation

PCA - dimension reduction

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.

Normality tests

Shapiro-Wilk test

Inference for a mean

Confidence interval

One-sample t-test

Comparing two means

Two-sample t-test

Paired test

Comparing groups

ANOVA for plant growth

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!

Covariance and correlation

Covariance by hand

Linear relationship

Nonlinear relationship

Linear regression model

Fitting linear models

Predicting with linear models

Logistic regression model

Fitting logistic models

Predicting with logistic models

Model evaluation

Validation set approach

Regression evaluation

Classification evaluation

Wrapping up

Practicing Statistics Interview Questions in R

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Feels like should go more into regression and assessing model fit.

Delruba Mahmud

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FAQs

What topics are covered in these statistics interview practice questions?

You practice probability distributions, exploratory data analysis, statistical tests including t-tests and ANOVA, and both linear and logistic regression model evaluation.

Is this course harder than a typical DataCamp course?

Yes. It is intentionally more challenging than standard DataCamp courses to ensure you can confidently handle statistics questions during real job interviews.

What datasets are used for practice in this course?

You work with Parkinsons disease data and gas prices among other datasets, applying statistical methods to realistic scenarios that mirror interview question formats.

How many prerequisites does this course have?

It has eleven prerequisites spanning probability, inference, hypothesis testing, sampling, regression, and data visualization. This is an advanced course requiring broad statistical foundations.

Will this course help me prepare for data science job interviews?

Yes. It is specifically designed to help you practice the most frequently asked statistical interview questions, covering distributions through regression with hands-on R coding.

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