The-Almost-Astrophysicist
The video discusses the different types of data science interview questions that candidates can expect to encounter. These include coding assessments, behavioral questions, technical questions, case studies, take-home assessments, and final round presentations. The video provides examples of questions for each type and offers advice on how to prepare for them. The final round presentation may require candidates to tailor their report to different stakeholders such as product managers, data scientists, and software engineers. The video concludes by summarizing the different interview categories and inviting questions and video ideas from viewers.
In this section, Priya discusses the six types of data science interview questions that candidates can expect to encounter and provides examples of each. These include coding assessments, which can involve SQL questions, lead code questions, or working with data sets provided by the company. Behavioral questions are also a common feature and candidates should be prepared with relevant stories or experiences to answer these effectively. Priya stresses the importance of companies asking plenty of behavioral questions and shares some links to common questions. Additionally, she advises that candidates devote some time to preparing stories in advance of their interviews.
In this section, the video explains that candidates for data science positions can expect different types of questions throughout the interview process. One type is behavior-based questions where candidates need to showcase their work ethics, project results, and team collaboration skills using the STAR method (Situation, Task, Action, Result). The next type is technical questions to assess the candidate's quick thinking and statistical knowledge. For instance, questions could be rapid-fire queries on statistical concepts like gradient boosting or ensemble methods. Candidates should be knowledgeable about these concepts and could utilize resources like StatQuest on YouTube to prepare. Finally, the coding round assesses a candidate's ability to think through complex problems, and it could be a LeetCode medium with three parts, with each step relying on the successful completion of the previous task.
In this section, the speaker talks about different types of data science interview questions. The first type is the case study, which involves whiteboarding or pseudo coding a solution for a hypothetical problem that a company is facing. The candidate must ask the right questions about the given data and form a general framework to approach the problem. The second type is the take-home assessment, which usually involves real company data and a machine learning-based question. The candidate must take a methodological approach, including data cleaning, exploration, and evaluation metrics, and distinguish themselves from other candidates by adding as much detail as possible, such as commenting on design choices and using linear optimization methods to increase profitability.
In this section, the speaker explains what to expect during a final round of a data science interview, which typically involves writing a case report and presenting it to different stakeholders such as product managers, data scientists, and software engineers. The report should be articulated differently depending on the audience, as each stakeholder has a different focus. For example, the data science manager may want to know about design choices and metrics, while the software engineer may be more interested in implementation. The section concludes by summarizing the different interview buckets one should prepare for and encouraging viewers to ask questions and suggest video ideas.
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