Abhinav is a Data Analyst at UpGrad. Hes an experienced Data Analyst with a demonstrated history of working in the higher education industry. Strong information technology professional skilled in Python,…
Attending a data analyst interview and wondering what are all the questions and discussions you will go through? Before attending a data analysis interview, it’s better to have an idea of the type of data analyst interview questions so that you can mentally prepare answers for them.
When one is appearing for an interview, they are being compared with other candidates as well. To think that I can crack it without any prep is good but one should never underestimate the competition as well. It is wise to keep one prepared for an interview. Now this “preparation” sounds vague. The preparation should be strategic, it should begin with an understanding of the company, job role, and culture of the company. And should be escalated to gaining additional knowledge of the domain the interview is for.
In this article, we will be looking at some most important data analyst interview questions and answers. Data Science and Data Analytics are both flourishing fields in the industry right now. Naturally, careers in these domains are skyrocketing. The best part about building a career in the data science domain is that it offers a diverse range of career options to choose from!
Organizations around the world are leveraging Big Data to enhance their overall productivity and efficiency, which inevitably means that the demand for expert data professionals such as data analysts, data engineers, and data scientists is also exponentially increasing. However, to bag these jobs, only having the basic qualifications isn’t enough. Having data science certifications by your side will increase the weight of your profile.
You need to clear the trickiest part – the interview. Worry not, we’ve created this Data analyst interview questions and answers guide to understand the depth and real-intend behind the questions.
Data Mapping Tutorial | SQL | UML | Use Case Diagram | Business Analyst Object Orientation Video
Yes, it is possible to automate data mapping processes. This can be done through the use of software that can read and understand both the source data and the target data, and then map the two together accordingly. This can save a lot of time and effort in manually creating data maps, and can help to ensure that the mapping is done accurately and correctly.
Data Mapping is the process of creating a correspondence between two sets of data. This is often done in order to transfer data from one format to another, or to convert data from one system to another. In order to do this, Data Mappers must have a strong understanding of both the source and target data, as well as the relationships between them.
One of the biggest issues that can arise when mapping complex data structures is data loss. This can happen if the mapping is not done correctly, and some of the data ends up getting lost in translation. Another issue that can come up is data corruption, which can occur if the mapping process is not done correctly and some of the data gets corrupted in the process.
– Ease of use: The solution should be easy to use and understand, even for complex data mapping tasks. – Flexibility: The solution should be flexible enough to handle a variety of data mapping scenarios. – Accuracy: The solution should be accurate in mapping data from one format to another. – Performance: The solution should be able to perform data mapping tasks quickly and efficiently.
Data mapping is often used when two different systems need to share data with each other. For example, if you have a customer database in one system and an order database in another, you might need to map the data between the two in order to keep track of which customers placed which orders. Data mapping can also be used to convert data from one format to another, or to transform data in some other way.
General Data Analyst Interview Questions
In an interview, these questions are more likely to appear early in the process and cover data analysis at a high level.Â
Experience-based Big Data Interview Questions
If you have some considerable experience of working in Big Data world, you will be asked a number of questions in your big data interview based on your previous experience. These questions may be simply related to your experience or scenario based. So, get prepared with these best Big data interview questions and answers –
How to Approach: There is no specific answer to the question as it is a subjective question and the answer depends on your previous experience. Asking this question during a big data interview, the interviewer wants to understand your previous experience and is also trying to evaluate if you are fit for the project requirement.
So, how will you approach the question? If you have previous experience, start with your duties in your past position and slowly add details to the conversation. Tell them about your contributions that made the project successful. This question is generally, the 2nd or 3rd question asked in an interview. The later questions are based on this question, so answer it carefully. You should also take care not to go overboard with a single aspect of your previous job. Keep it simple and to the point.
How to Approach: This is a tricky question but generally asked in the big data interview. It asks you to choose between good data or good models. As a candidate, you should try to answer it from your experience. Many companies want to follow a strict process of evaluating data, means they have already selected data models. In this case, having good data can be game-changing. The other way around also works as a model is chosen based on good data.
As we already mentioned, answer it from your experience. However, don’t say that having both good data and good models is important as it is hard to have both in real life projects.
How to Approach: The answer to this question should always be “Yes.” Real world performance matters and it doesn’t depend on the data or model you are using in your project.
The interviewer might also be interested to know if you have had any previous experience in code or algorithm optimization. For a beginner, it obviously depends on which projects he worked on in the past. Experienced candidates can share their experience accordingly as well. However, be honest about your work, and it is fine if you haven’t optimized code in the past. Just let the interviewer know your real experience and you will be able to crack the big data interview.
How to Approach: Data preparation is one of the crucial steps in big data projects. A big data interview may involve at least one question based on data preparation. When the interviewer asks you this question, he wants to know what steps or precautions you take during data preparation.
As you already know, data preparation is required to get necessary data which can then further be used for modeling purposes. You should convey this message to the interviewer. You should also emphasize the type of model you are going to use and reasons behind choosing that particular model. Last, but not the least, you should also discuss important data preparation terms such as transforming variables, outlier values, unstructured data, identifying gaps, and others.
How to Approach: Unstructured data is very common in big data. The unstructured data should be transformed into structured data to ensure proper data analysis. You can start answering the question by briefly differentiating between the two. Once done, you can now discuss the methods you use to transform one form to another. You might also share the real-world situation where you did it. If you have recently been graduated, then you can share information related to your academic projects.
By answering this question correctly, you are signaling that you understand the types of data, both structured and unstructured, and also have the practical experience to work with these. If you give an answer to this question specifically, you will definitely be able to crack the big data interview.
Dual processors or core machines with a configuration of 4 / 8 GB RAM and ECC memory is ideal for running Hadoop operations. However, the hardware configuration varies based on the project-specific workflow and process flow and need customization accordingly.
HDFS NameNode supports exclusive write only. Hence, only the first user will receive the grant for file access and the second user will be rejected.
The following steps need to execute to make the Hadoop cluster up and running:
In case of large Hadoop clusters, the NameNode recovery process consumes a lot of time which turns out to be a more significant challenge in case of routine maintenance.
It is an algorithm applied to the NameNode to decide how blocks and its replicas are placed. Depending on rack definitions network traffic is minimized between DataNodes within the same rack. For example, if we consider replication factor as 3, two copies will be placed on one rack whereas the third copy in a separate rack.
The HDFS divides the input data physically into blocks for processing which is known as HDFS Block.
Input Split is a logical division of data by mapper for mapping operation.
Top Data Analyst Interview Questions & Answers
These are standard data science interview questions frequently asked by interviewers to check your perception of the skills required. This data analyst interview question tests your knowledge about the required skill set to become a data scientist.
To become a data analyst, you need to:
Along with that, in order to these data analyst interview questions, make sure to represent the use case of all that you have mentioned. Bring a layer to your answers by sharing how these skills will be utilised and why they are useful.
Our learners also read: Excel online course free!
FAQ
What is data mapping process?
What is the importance of data mapping?
- Tell me about yourself. What they’re really asking: What makes you the right fit for this job? …
- What do data analysts do? …
- What was your most successful/most challenging data analysis project? …
- What’s the largest data set you’ve worked with?
How do you validate data interview?
