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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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.
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.
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.
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.
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!
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.
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.
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.
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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Harvard Business School
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Decision Science Analytics @ USAA