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The interview questions listed in this blog are based on the roles and responsibilities of a data analyst. However, this list may vary based on the nature of work in an organization. if you are looking for a data analyst job profile then these interview questions will help you to land a dream job in top IT Companies as expected by you.
As we know, every industry releasing a huge amount of data need a data analyst to derive meaningful insights from multiple data sources. The average salary of an entry-level data analyst is calculated as $50,000-$75,000 and for experienced professionals, salary may reach up to $65,000-$110,000.
If you are an aspirant looking for a job in data analysis domain then you should have an idea of Hadoop framework, Spark, and other programming languages like R, Python, SAS, data mining, data visualization, statistics, and machine learning etc. An interviewer always judges the candidate based on his communication skills, problem-solving skills, and analytical skills etc. This post will prepare you for the analytical skills in case of data analyst job profile.
Q1). How will differentiate two terms data analysis and data mining? Data Mining
Q2). How will you define the data analysis process?
The data analysis process majorly involves data gathering, data cleaning, data analysis, and transforming data into a valuable model for better decision-making within an organization. the major steps for the data analysis process can be listed as – Data Exploration, Data preparation, Data Modeling, Data validation, Data implementation etc.
Q3). What is the role of a data model for any organization?
With the help of a data model, you can always keep your client informed in advance for a time period. However, when you enter a new market then you are facing new challenges almost every day. A data model helps you in understanding these challenges in the best way and deriving the accurate outputs from the same.
Q4). What are the major differences between data profiling and data mining?
Data profiling is the process of data analysis based on consistency, logic, and uniqueness. The process could not validate the inaccurate data values but it will check the data values for business anomalies. The main objective of this process is checking data for many other purposes. At the same time, the data mining process is used to find the relationship between data values that are not discovered earlier. It is based on bulk analysis as per attributes or data values etc.
Q5). What is the role of the QA process is defining the outputs as per customer requirements?
Here, you should divide the QA process into three parts – data sets, testing, and validation. Based on the data validation process, you can check either data model is defined as per customer requirements or needs more improvement.
Q6). How can you perform the data validation process successfully?
The data validation can be defined in two steps. First is data screening and other is data verification. In the first step i.e. data screening, algorithms are used to screen the data to find any inaccurate data. These values need to check or validate again. In the second step for Data verification, values are corrected on the case basis and invalidate values should be rejected.
Q7). What are the challenges during faced by the data analyst professionals?
Read: What Is a Data Warehouse: Definition, Types, And Its Benefits
It could be poor formatted files, inconsistent data, duplicate entries, or messy data representation etc.
Q8). How will you identify either a developed data model is good or not?
Q9). Is there any process to define customer trends in the case of unstructured data?
Here, you should use the iterative process to classify the data. Take some data samples and modify the model accordingly to evaluate the same for accuracy. Keep in mind that always use the basic process for data mapping. Also, focus on data mining, data visualization techniques, algorithm designing or more. With all these things, this is easy to convert unstructured data into well-document data files as per customer trends.
Q10). What do you understand by the term data cleansing?
Data cleansing is an important step in the case of a data analysis process where data is checked for repletion or inaccuracy. In case, it does not satisfy business rules then it should be removed from the list.
Q11). Define the best practices for data cleaning process.
The best practices for data cleansing process could be taken as –
Q12). What are the skills needed to become a successful data analyst professional?
He should have an idea of Hadoop framework, Spark, and other programming languages like R, Python, SAS, data mining, data visualization, statistics, and machine learning etc to become a successful data analyst professional.
Q13). What is the average salary of entry-level or experienced data analyst professionals?
The average salary of an entry-level data analyst is calculated as $50,000-$75,000 and for experienced professionals, salary may reach up to $65,000-$110,000.
Q14). When you are given a new data analytics project then how should you start? Explain based on your previous experiences.
The purpose of this question is to understand your approach how you work actually. Make sure that the process you are following is always organized. The process should be designed so well that it could help you in achieving business goals ultimately. Obviously, the answer to this question depends on your experience and person to person.
Q15). How will you define the interquartile range as a data analyst?
The measure of data dispersion within a box plot is defined as the interquartile range or it could be defined as the difference between upper and lower quartile.
Read: What Is Data Collection: Methods, Types, Tools, and Techniques
Q16). What were the major responsibilities you handled in your last Company?
These are just the ideas, you are free to change the responsibilities as per your experience.
Q17). How will you define the term logistic regression?
This is a statistical approach for examining datasets closely where one or more variables are dependent on each other and defining outputs clearly.
Q18). Name a few popular data analysis tools that you have used earlier.
Q19). Name the framework that can be used to process large datasets in a distributed computing environment.
Hadoop and MapReduce are two popular frameworks that are used by data analyst professionals to process large datasets in a distributed computing environment.
Q20). What are a few missing patterns that are frequently observed by the data analyst professionals?
A few missing patterns that are frequently observed by data analyst professionals include –
Popular Missing Patterns
Q21). What do you mean by the KNN imputation method?
In the KNN method, the missing values are computed through attributes with the help of a distance function where you may also check the similarities among two attributes.
Q22). Explain how should you work with multi-source problems.
To work with multi-source problems, here are the techniques –
Q23). What do you mean by the outlier?
Read: Data Analyst Job Description: Career Guide On How to Become Data Analyst
The term is usually preferred by analysts for values that are far away and diverges from an overall pattern. Two popular types of outliers could be given as –
Q24). What are the key skills needed for getting hired as a data analyst? Database Skills
Big Data Knowledge
Q25). What do you mean by the KPI, design of experiments, and 80/20 rule?
KPI means key performance indicator that could be defined as the metric consists of a combination of spreadsheets, charts, reports, or business processes etc. Design of experiment is the initial process that can be used to split data, data sampling, or data setup for statistical analysis. And the last term if 80/20 rule where 80 percent of total income comes from 20 percent of audiences.
Q26). What do you mean by the term MapReduce?
MapReduce is the process to split datasets, analyzing them, processing subset and combining outputs driven from each of the subsets.
Q27). How will you define the term clustering?
Clustering could be defined as the classification process that can be applied to the data. With the help of algorithms, you can always divide the data into natural clusters.
Q28). What are the few properties of clustering?
A few properties of clustering algorithms could be given as – Q29). What are the few statistical techniques that can be used by data analysts for effective outputs?
Statistical methods those are useful for data scientist
Q30). What do you mean by the time series analysis?
In the case of time series analysis, you could calculate the output of a series effectively with the help of various data analysis tools.
Read: 23 Smart Data Analytics Tools For Perfect Data Management
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