This article will cover some of the most commonly asked statistic and probability interview questions and answers to help you prepare for your next data science interview. This is of the most common questions we get asked at ProjectPro from data science aspirants preparing for a data scientist job interview. What probability and statistics questions are asked in a data science interview ? You should take some time to learn the theory behind these concepts before making the transition into data science. There are many statistics textbooks and online courses that cover the above topics. Statistical Modelsâ-âLinear Regression, Logistic Regression To become a data scientist, here are some statistical concepts you need to understand: You should understand the data presented along with the business requirement and decide the kind of model you should build to make predictions on the dataset. Data scientists should be able to create and test hypotheses, understand the intuition behind statistical algorithms they use, and have knowledge of different probability distributions.ĭownloadable solution code | Explanatory videos | Tech Support Start ProjectĪs a data scientist, if you are presented with a large dataset, you need to have the ability to understand the type of data pre-processing and analysis to do. However, it is still essential for data scientists to understand statistics and probability concepts to examine datasets. Running a regression algorithm on thousands of data points only takes a couple of seconds to do. Today, many libraries are available in languages like Python and R that help cut out a lot of the manual work. The term ‘ data science’ suddenly took off and became popular, and data scientists were individuals who possessed knowledge of both statistics and programming. Data pipelines had to be created to munge large amounts of data that couldn’t be processed by hand. Over time, however, as the amount of data increased, statisticians alone were not enough to do the job. “A data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”īefore data science became a well-known career path, companies would hire statisticians to process their data and develop insights based on trends observed. As a data science aspirant, you would have probably come across the following phrase more than once:
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