I pulled all to these questions and answers from our website, But we brought in nearly 100 different professional interviewers to create all the interview questions and answer examples. So in this article, we will dive into what I believe are five best data. Analysts interview questions with the answer examples. Let's get started. Question number one, think of a project where you were working with a relatively large data set. Describe to me the process you had to take to gather and prepare the data for analysis. Working with large data sets can present some challenges. So hiring managers want to know that you have the experience to handle them if they arise, share any challenges you might have faced and how you successfully overcame them. If you have been fortunate enough, not to facing challenges, stick to the details of your project and the steps you took while working with the data.

我从我们的网站上找到了所有这些问题和答案，但我们带来了近100个不同的专业面试官来创建所有的面试问题和答案示例。所以在本节中，我们将深入研究我认为最好的五个数据。分析师面试问题与答案示例。我们开始吧。第一个问题，考虑一个项目，你正在处理一个相对较大的数据集。请向我描述您收集和准备数据进行分析的过程。处理大型数据集可能会带来一些挑战。所以招聘经理想知道这些。你有处理它们的经验，如果它们出现，分享你可能面临的任何挑战，以及你如何成功克服它们。如果你足够幸运，不需要面对挑战，那就坚持你的项目的细节和你在处理数据时所采取的步骤。

Here's an answer example from our website. I have had experience working with large data sets delivered to us from outside vendors. Many times these data sets were survey responses for marketing research projects with a large sample size.

下面是我们网站上的一个答案示例。我有处理外部供应商交付给我们的大型数据集的经验。很多时候，这些数据集是对大样本量的市场研究项目的调查反馈。

Upon first receiving the data set, I check the validity of the data by running predetermined frequencies inquiries. Doing so would often reveal such issues as missing data type issues and errors and skip patterns within the survey. I would work with the vendor to correct these issues before beginning further analysis on the data. Once the data issues were resolved, I would load the data into a data analysis, told to begin my analysis. Sometimes I would work with a data engineer to load it into an appropriate tool that could handle the size of the data set.

在第一次收到数据集时，我通过运行预定的频率查询来检查数据的有效性。这样做通常会暴露一些问题，如缺少数据类型问题和错误，以及调查中的跳过模式。在开始对数据进行进一步分析之前，我会与供应商一起纠正这些问题。一旦数据问题得到解决，我会将数据加载到数据分析中，然后开始分析。有时我会与数据工程师合作，将其加载到可以处理数据集大小的适当工具中。

Question number two, describe to me an analysis project. You have worked on where the results were the most surprising to you and all those involved in the project. When launching an analysis, most analysts have a prediction on the outcome based on learning from past projects. However, there will likely be times when the results were unexpected. Your answer to this question will give the interviewer a glimpse of not only the type of analytical projects you have worked on, but also your enthusiasm for them. When describing your project, be sure to show some passion about the learning you drew from it. Also consider including what action was taken by you and or the stakeholders as a result of the unexpected results.

第二个问题，给我描述一个分析项目。你在哪里工作，结果对你和所有参与项目的人来说是最令人惊讶的。在启动分析时，大多数分析师都会根据从过去项目中学到的经验对结果进行预测。然而，有时可能会出现意想不到的结果。你对这个问题的回答不仅会让面试官了解你所从事的分析项目的类型，还会让面试官了解你对这些项目的热情。在描述你的项目时，一定要对你从中学到的东西表现出一些热情。还要考虑包括您和/或利益相关者因意外结果而采取的行动。

In my experience, working with customer profiling projects, analysis usually do not show notably so, surprising results, particularly for established brands. However, while conducting one routine analysis, I was able to identify a customer sub segment that had the potential to provide additional value to the company. If it was offered the right product and services with a relevant message, for me, it felt as if I struck old, the opportunity to add value to a subset of an existing customer base through new products and services was invaluable. It was surprising to everyone involved that we could identify sub segment from this customer base. From there, we begin ST. Ra TE GI Zi ng with product development and brand managers to develop a plan for this new sub segment.

根据我的经验，在客户分析项目中，分析通常不会显示出明显的令人惊讶的结果，特别是对于已建立的品牌。然而，在进行一次常规分析时，我能够确定有可能为公司提供额外价值的客户细分市场。如果提供了正确的产品和服务以及相关的信息，对我来说，感觉就像我老了一样，通过新产品和服务为现有客户群的子集增加价值的机会是无价的。我们可以从这个客户群中确定细分市场，这让所有相关人员都感到惊讶。从那里，我们开始圣。与产品开发和品牌经理合作，为这一新的细分市场制定计划。

The question number three, talk about your knowledge of statistics and how you have used this knowledge in your analytical projects. At the minimum data, analysts should have knowledge and experience using basic statistics, including mean median and mode, and be able to conduct significance testing. A more advanced level of statistics may be required, but this would be specified in the job description. Did analysts not only know how to calculate these basic statistics, but should be able to interpret them in relation to the business? I use statistics on a regular basis as a data analyst for the majority of my work. I calculate basic statistics such as the mean and standard variances. I also conduct significance testing frequently to determine if measurement differences between two populations. Is it typically significant and worth highlighting for further investigation? In addition for a few projects, i've worked with correlation coefficients to determine the relationship between two variables, and a, data set.

第三个问题，谈谈你的统计学知识，以及你如何在你的分析项目中使用这些知识。在最低限度的数据上，分析员应具备使用基本统计的知识和经验，包括均值、中位数和众数，并能够进行显著性检验。可能需要更高级的统计数据，但这将在职务说明中具体说明。分析师是否不仅知道如何计算这些基本统计数据，而且应该能够根据业务情况对其进行解释？作为一名数据分析师，我在大部分工作中都会定期使用统计数据。我计算基本的统计数据，如均值和标准方差。我还经常进行显著性检验，以确定两个群体之间是否存在测量差异。它是否具有典型意义，是否值得强调以供进一步调查？此外，在一些项目中，我使用相关系数来确定两个变量和数据集之间的关系。

Question number four, why do you think creativity is a good skill for a data analyst to have? And how have you used it in your career? When considering data analyst skills, creativity is not Top of mine for many. Instead, plenty of people would consider technical and math or statistical skills be at the Top of the list. However, data analysts, user creativity in a variety of ways, including developing analytical plans, finding solutions to data issues and presenting data. Visually, creativity is about thinking outside the box. Be prepared to share in more detail how you use your creativity for a specific project. As a data analyst. There's no question that creativity is an important skill to have. Activities was got me, pass the data roadblocks in past projects. It has also helped me find new and interesting ways present analytical results to clients. More specifically, I find that creativity is important when validating data before analyzing it. There been a few times when I began analyzing data, only to find there were some abnormal results. I set back and create new and non routine data checks in order to identify issues, causing the typical reason results.

第四个问题，为什么你认为创造力是数据分析师应该具备的一项好技能？在你的职业生涯中，你是如何使用它的？当考虑数据分析技能时，对许多人来说，创造力并不是我的首选。相反，很多人会认为技术和数学或统计技能是最重要的。然而，数据分析师以各种方式发挥用户的创造力，包括制定分析计划、寻找数据问题的解决方案和呈现数据。从视觉上看，创造力就是跳出框框思考。准备好更详细地分享您如何在特定项目中使用您的创造力。作为一个数据分析师。毫无疑问，创造力是一项重要的技能。活动是我得到的，通过过去项目中的数据路障。它还帮助我找到了向客户展示分析结果的新的、有趣的方法。更具体地说，我发现在分析数据之前验证数据时，创造力非常重要。有几次，当我开始分析数据时，只发现有一些不正常的结果。我设置并创建新的非常规数据检查，以确定导致典型原因结果的问题。

Question number five, describe a project will use both quantitative and qualitative data to conduct your analysis. Data analysts use all the data available to them to conduct the most impact full analysis. This could include both quantitative and qualitative data. The hiring Andrew wants to know how much experience you have marrying qualitative to quantitative data. Sometimes it is straightforward, as is the case when working with survey data that has both qualitative and quantitative questions. Other times, it may take some creativity to find applicable qualitative data to using conjunction with your quantitative data. If you have several projects to choose from, sure about the project will use the most creativity emerging the 2 types of data.

第五个问题，描述一个项目将使用定量和定性数据来进行分析。数据分析师使用所有可用数据进行最具影响力的全面分析。这可能包括定量和定性数据。招聘安德鲁想知道你有多少将定性数据与定量数据相结合的经验。有时它是直截了当的，就像处理既有定性问题又有定量问题的调查数据一样。其他时候，可能需要一些创造性来找到适用的定性数据与定量数据结合使用。如果你有几个项目可供选择，可以肯定的是，该项目将使用最具创造性的2种类型的数据。

If possible, I always try to incorporate qualitative data to support with the quantitative data is telling me. I've been fortunate enough to have conducted several analysis where qualitative survey data was readily available to me. However, when working with survey data, I don't think you should limit yourself to the qualitative data from one survey. One appropriate, I found that there can be valuable qualitative data from other surveys or external sources. For one particular market analysis, dealing with a new product evaluation, I reached out to the operations department to utilize qualitative data. They had collected from distributors using this qualitative data, strengthen the validity of my recommendations to the product development group. 

如果可能的话，我总是试图将定性数据与定量数据结合起来，以支持定量数据告诉我的情况。我很幸运地进行了几次分析，其中定性调查数据对我来说是现成的。然而，在处理调查数据时，我认为您不应将自己局限于一项调查中的定性数据。一个适当的，我发现可以从其他调查或外部来源获得有价值的定性数据。对于一个特定的市场分析，处理新产品评估，我联系了运营部门，以利用定性数据。他们利用从分销商那里收集到的定性数据，加强了我对产品开发小组建议的有效性。