50+ Best Data Analyst Interview Questions To Get Hired With

Data Analysis is quickly becoming a core aspect of operation for many modern internet startups. Because so many of the products entering the market in 2019 are only marginally different from one another, operators of businesses must think highly quantitatively in their approach to profit. This is where the need for business intelligence and data analysis skills become important. These personal assets often play a major role in understanding the customer and helping to better design products to appeal to the target market. Additionally, an understanding of business intellect and data analysis can help to address broader market changes and competition, providing a lens for the executive team that is otherwise inaccessible and incalculably valuable. Clearly, professionals in the data analysis field are both useful and highly sought-after. These data analyst interview questions and answers should be helpful in preparing you for the first step towards gainful employment in this coveted position. Below are the best possible data analyst interview questions and answers I could put together to help you land your new data analyst position.


Before we get into the interview questions, it might be helpful to see someone explain the role in greater detail. This short 3-minute video provides a succinct explanation of what a data analyst actually does. It should be very helpful to see how industry professionals describe the role as a whole; this will help you as you begin to explain to future employers your own potential value and passion for the space.


Data Analyst Interview Questions and Answers Table Of Contents

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.

Data Analyst Interview Questions & Answers

1. Could you please provide a detailed explanation of what is required for one to become a data analyst?

• The person has to have in-depth knowledge on programming languages, including Javascript, HMTL, reporting packages, and SQL.
• They should have technical knowledge concerning database design, data modes, segmentation, and mining approaches.
• The analyst should have an in-depth knowledge on statistics packages, which are integral to the analysis of big datasets on platforms like SPSS, excel, and SAS.
• They should also possess strong skills in the analysis, organization, collection and dissemination of big data.

2. What are the obligations of a data analyst?

• They should provide support for their particular analyses and correspond with both clientele and staff.
• They should make certain to sort out the business-related problems for the clients and frequently audit their data
• Analysts commonly analyze products and consider the information they find using statistical tools, providing ongoing reports to leaders in their company.
• Prioritizing business requirements and working alongside management to deal with data needs is a major duty of the data analyst.
• The data analyst should be adept at the identification of new processes and specific areas where the analysis and data storage process could be improved. • A data analyst will help to set the the standards and performance, locating and correcting the code issues preventing these standards from being met.
• Securing the database through the development of access systems to determine and regulate user levels of access is another huge duty of this position.

3. Explain the process of data analysis.

Data analysis involves the collection, inspection, cleansing, transformation, and modeling of data in order to provide the best insights and support decision-making protocols within the firm. At its core, this position provides the backbone of what constitutes the most difficult decisions a firm will have to make. The different steps within the process of analysis include:

• Exploration of data; when a business problem has been identified, the analyst might go through the data as provided by the customer so they can get to the root of the issue.
• Preparing the data; the preparation of data is crucial because it helps to identify where there might be are data anomalies like missing values and outliers-- inappropriately modeled data can lead to costly decision-making errors.
• Data modeling; the step for modeling starts as soon as the data has been prepared. In this process, the model is run repeatedly for the purpose of improving clarity and certainty of the data. Modeling helps to guarantee that the best possible result is eventually found for particular problems.
• Data validation; this step involves the model provided to the client and the model given to the analyst being verified against one other to ascertain if the Newly-developed model will meet expectations.
• Model implementation and tracking; this final step of the of the process of analysis allows the model to be implemented after it has been has been tested efficiency and correctness.

4. What is the frequency of retraining data models?

An effective data analyst knows all about changing dynamics in their business and how this evolving nature might affect the efficiency and certainty of their predictive models. the analyst should be a consultant who is able to utilize their skills in analysis as well as their acumen for getting to the cause of problems. The appropriate way to answer this query is to claim that it would be possible to work with the customer towards defining a particular period as well as possible. It would also be possible to retrain the model when the firm goes into a new market, begins to face competition, or is part of a merger.

5. Describe the way that a data analyst would go about QA when considering a predictive model for the forecasting of customer churn.

The analyst often requires significant input from proprietors, as well as a good environment where they are able to conduct operations from the analytics. For one, to create and deploy the model demands that this process needs to be as efficient as possible. Without feedback from the owner, the model loses applicability as the business model evolves and changes. The appropriate course of action is usually to divide the data into three separate sets which include training, testing and validation. The results of the validation would then be presented to the business owner after the elimination of the biases from the first two sets. The input of the client should give the analyst a good idea about whether or not the model is able to predict the customer churn with accuracy and consistently provide the correct results.

6. How does an analyst create a classification to identify the main customer trends when it comes to unstructured data?

The model will not be valuable if its results are not actionable. A data analyst with experience would have varying strategies depending on the data type which is being analyzed. The sensitive data types must be protected, so experienced analysts will frequently consult with the stakeholder to make certain they go along with the necessary regulations of the company and associated disclosure laws. It may be advisable to first talk with the stakeholder to ascertain the goal of classifying the data. Then iterative processes can be used to pull the samples and modify the process so that it can then be evaluated for accuracy and efficiency. The procedure is usually to map the data, initiate the relevant algorithms, and then mine the data.

7. Define the data cleansing process.

The process of data cleaning relies on finding and removing errors and inconsistencies from the data in order to make ensure high quality.

8. Discuss some of the most important practices when data cleansing.

• Sorting the data according to different characteristics
• For the larger datasets, they need to be cleaned in steps and then improved with each step until a good quality is achieved.
• Larger type data sets should be broken down into smaller data. Working with less data is will typically increase the speed of one’s iteration.
• In order to do this, the usual cleanse requires analysts to create a set of utility type functions. This may include the remapping of values according to the regex or CSV files and searching for and replacing the values not matching the regex.
• In the event of a cleanliness issue, then data sets need to be arranged according to the frequency as the analyst considers the issues. Data analysts should then analyze the statistics for each column according to the mean, standard deviations, and the missing values.
• Consider every data cleaning operation so that changes can be done or operations can be altered if need be.

9. Why is data mining useful as a process in big data analysis?

Hadoop is one of the cluster architectures needed to analyze a large data set in order to identify prevalent unique patterns. These patterns assist in helping the analyst understand the problem areas of business so that a solution can be found. Data mining is very useful as a process for this task, making it particularly widely used in big data analysis.

10. Can you briefly explain data profiles.

Data profiling is a process that validates the data in an existing data source and tries to understand if it can be readily utilized for other means.

11. What is logistic regression?

This is one of the statistical means used by data analysts to examine datasets where single and multiple independent variables determine the outcome.

12. What is the data screening process?

Data screening is a part of the validation process in which a complete set of data is processed through a number of validation algorithms to try to figure out if the data contributes to any business-related problems.

13. Explain the meaning of a K-mean algorithm.

The k-mean algorithm is used for data partitioning within a clustered architecture type. During this process, data sets can be classified through different clusters, meaning that objects are divided into different k-groups according to the algorithm:

• As the clusters form the shape of a sphere, the data points within them are centered.
• The spread or the variance of the cluster is almost always relatively similar.

14. Explain the difference between mining and profiling.

• Data mining refers to cluster analytics and the detection of inconsistencies, as well as the discovery of trends between particular features.
• Data profiling refers to the analysis of particular features data. The focus of this process is primarily on details of the set data, including range, frequency, value ranges, and the occurrence of null values.

15. What are some of the most common problems faced by data analysts?

• Duplicated entries
• Missing values
• Variances in the values represented (errors)
• Common misspelling
• Unidentified overlapping data

16. What is the name of the Apache framework made for processing large data sets for an application within a distributed computing environment?

MapReduce and Hadoop are both programming networks initiated by Apache for the processes of handling large sets of data for applications within a distributed computing type of environment.

• Discuss the missing patterns commonly observed. Which ones are missing at random?
• Missing dependent on an absent value
• Missing depended on an unobserved variable
• Missing completely at random

17. Discuss the meaning of collaborative filtering.

Collaborative filtering is a simple algorithm that initiates a recommendation system according to the behavioral data of the user. The user’s items of interest are a significant input in the algorithm. One example would be when a company claims that an ad or a video is recommended for the user’s viewership. It may be a platform online, which gives a line of suggestions depending on the user’s browser history. It considers what the user has been shopping for and then gives a list of other potential products that could fit that type according to price and style.

18. Discuss what a KPI is, and explain the 80/20 rule of experiments.

• KPI: KPI refers to Key performance indicators. This is a metric which consists of the trends illustrated by spreadsheets, as well as charts and reports of the financial and business related performance.
• 80/20 regulation: this rule implies that the majority (80 percent) of the business or income comes from the minority (20 percent) of its customers.
• Experimental design: This is the initial process for splitting data and samples before setting the data for the purposes of statistical review and analyses.

19. Please explain “map-reduce.”

Map-reduce is a specific framework which processes large sets of data and divides them into subsets. These sets are processed on different server types and then blended with the results on each server.

20. What are some of the main tools used in Big Data?

• Hive
• Hadoop
• Pig
• Mahout
• Flume
• Sqoop

21. What is an outlier?

The outlier is a term used by statistical analysts which refers to a value that seems distant from the other values and diverges significantly from the original pattern of the sample. There are two types of these outliers:

• Multivariate
• Univariate

22. Explain the hierarchal clustering algorithm.

This is a process for combining and dividing the existing data groups in order to create a hierarchal structure out of that to represent the order in which the groups are merged or divided.

23. What is time series analysis?

Time series analysis is the process of forecasting output through the analysis of previous data using typical statistical tools, such as the log linear-regression method, exponential smoothing, among others. This is implemented in two dimensions, which are time and frequency.

24. What is clustering in data analysis?

Clustering defines the process of grouping a particular set of objects according to specific predefined parameters. This is one of the most important data analysis approaches utilized in big data analysis.

25. Discuss the meaning of Correlogram Analysis.

This refers to spatial analysis. It consists of a series of autocorrelation coefficients, which are estimated and computed for different spatial links. These can be used to create correlograms for data, which explain the distance whereby the data is expressed rather than the values, which are at individual points.

26. Define the hash table.

The hash table is a map of keys to particular values. It is a structure which can be used to create an associative array. This is a function which computes the index into particular arrays. These are differentiated slots where the needed value would be gotten.

27. Describe hash table collisions how they can be avoided.

A hash table collision happens when there are different keys which have similar types of values. Some types of data cannot be stored in specific types of slots. The following approaches to data management help to avoid hash table collisions:

• Separate forms of chaining allows for chaining of different types of data to avoid hash collisions.
• Open addressing searches for other slots with the use of a second function and stores the items in the first empty slots that are found.

28. Describe imputation and list the different types of imputation approaches there are.

During imputation, missing data is replaced with substituted values.The types of imputation are single and multiple. For the single imputation approach:

• Cold deck imputation selects donors from another datasets at an advanced level.
• Hot deck imputations allow missing values to be imputed from similar records with the help of a punch card.
• Mean imputation refers to the replacement of missing values with specific variable means.
• Regression imputation concerns the replacement of missing variables with predicted values of the variables according to the others.
• Stochastic regression is similar to regression imputation but with the addition of the average regression variance.

29. Which imputation method is usually preferred among analysts?

Even in the event that single imputation can be used severally, it does not necessarily reflect the uncertainty that is initiated by missing data. In doing so, multiple imputation is better when compared to single imputation when dealing with random missing data.

30. How can a data analyst respond to missing or suspected data?

• Preparing a validation report will provide information concerning the missing or suspected data. This information includes points such as validation fails with date and time stamps.
• The suspected data can then be further examined in order to validate its credibility.
• The invalid data ought to be replaced and assigned with a validation code.
• Then embark on the key data analysis protocols, including deletion method, model-based methods or single imputation to work on the missing data.

31. How does one deal with multi-source problems?

• Explain how to perform a schema integration by restructuring the schemas
• Identify and merge similar records into a single record with all of the relevant attributes while avoiding redundancy.

32. What would be the optimal attributes of a good data model?

• It needs to be scalable for large data changes.
• It should be consumed easily.
• It should be performed in a predictable manner.
• It should be adaptable if the requirements of the model where to be changed.

33. Determine the most common statistical approaches for data analysis.

• Simplex algorithm
• Bayesian approach
• Markov chains
• Mathematical optimization
• Cluster and spatial processes
• Rank statistics

34. What are data validation methods utilized in data analytics?

• Form level validation; in this method, validation is done at the time the user completes the form before a save of the information is needed.
• Field level validation; validation is done in each field as the user enters the data to avoid any errors that have been caused by human interaction.
• Data saving validation; this validation approach is done during the saving process of the actual file or database record. This may be done when there are a number of data entry forms.
• Search criteria validation; this this validation type would be relevant when trying to match what the user is searching for to a particular degree. It is to make certain the results are returned actually.

35. What is the difference between the true positive rate and recall?

There is no particular difference as they are the same with the formula denoted as: (true positive)/(true positive + false negative)

36. What are the differences between logistic and linear regression?

• Linear regression needs independent variables to be continuous, but when it comes to logistic regression, they can have dependent variables with more than two categories.
• The linear regression is based according least square estimation while logistic regression is based according to maximum likelihood estimation.
• Linear regression is directed at finding the best fitting straight line where the distance between the points and the regression line would be errors. Logistic regression is used in the prediction of a binary outcome as the resultant graph is S – curved.
• Linear regression also requires 5 cases for each independent variable. Logistic regression requires at least 10 events for each independent variable.

37. What are the conditions where it would be best to use a t-test or a z-test?

The T-test is used when there is a sample size of less than 30 and the z-test is best used when there is a sample test that is greater than 30.

38. What are the main methods for the detection of outliers?

• Box plot method: if the value happens to be higher or lesser than 1.5 for the inter quartile range and above the upper third quartile or below the second quartile respectively then it would be considered as an outlier.
• Standard deviation: if the value is higher or lower than the mean or plus/negative three times the standard deviation, then it would be considered as an outlier.

39. What is the difference between the standardized and the non-standardized coefficients?

The standardized coefficient is interpreted according to the standard deviation though the unstandardized coefficient would be measured in actual values.

40. What is the difference between R-squared and adjusted R-squared?

• R-squared measures the proportion of the variation within the dependent variables as explained by the independent variables. The adjusted R-squared provides the percentage of variation as explained by the independent variables that in reality affect the dependent variable.

41. What are the main skills that are required for data analysts?

• Database knowledge: they have to have knowledge on this as concerns data management, blending, querying and manipulation.
• Predictive analytics: this considers basic descriptive statistics, advanced analytics and predictive modeling
• Presentation skill entails insight presentation, report design, and data visualization
• Big data knowledge entails machine learning, unstructured data analysis, and big data analytics

42. What is the KNN imputation method?

In KNN imputation, the missing attribute values are imputed through the use of the attributes value, which is most similar to the attribute whose value, is missing. Through using a distance function, the similarity of two attributes can then be determined.

43. Distinguish between principal component analysis and factor analysis.

• The objective of principal component analysis would be to explain the covariance between the variables, though the aim of factor analysis is to explain the variance between the variables.
• Concerning the principal component part, the elements may be calculated as linear combinations for the variables. When it comes to factor analyses, though, the raw input variable would be set as linear collaborations of the factors.
• PCA is utilized when you have to reduce the variables; however, the FA is enacted when you need to group the attributes according to the factors.
• The main idea when it comes to PCA would be explaining the total variance in the variables as much as, possible. Factor analysis on the other hand illustrates particular correlations between variables.

44. Why is ‘Naive Bayes’ naive?

It is naive as it assumes that all of the appropriate datasets are equally the same and independent, which is not the case when it comes to a real world scenario.

45. How does one statistically compare means between the groups?

• You can opt to use an independent T-test in the case the categorical and continuous variables have two independent categories.
• It may be advisable to use a paired T test when the categorical and continuous variables have two dependent categories.
• You can use one-way ANOVA when a continuous and categorical variable have two independent categories.
• Make use of GLM repeated measures for continuous variable and categorical variables.

46. Give a definition for homoscedasticity.

Within a linear regression model, there ought to be homogeneity of variance relating to the residuals. The variance of the residuals is almost the same for all of the predicted dependent variable type values.

47. Explain the difference between the mean, mode and the median.

The mean is calculated by obtaining the sum of every value in the list and then dividing that by the number of observations. The mode is the most-occurring value in the list, while the median refers to the middle value.

48. Which types of data are appropriate for the median, mode or the mean?

The mean is best used for the continuous type data without any outliers. It is affected by the extreme type values, which are outliers. The mode is suitable for the type of categorical data, which belongs to nominal or ordinal scales. The median is best suited for the continuous data that has outliers or ordinal data.

49. What is the difference between stratified and cluster type sampling?

The main difference between stratified and cluster sampling is that cluster sampling happens by selecting clusters at random and then sampling each of the clusters or doing a census within the cluster, though not all of the clusters ought to be selected. When it comes to stratified sampling, all of the strata should be sampled.

50. Determine the meaning of the p-value in statistics.

This is a common statistical measurement which is the lowest significance when you can reject the null hypothesis on a normal curve. In the event the p value is less than 0.05 than you would reject the null hypothesis at a 5 percent significance level.

51. What are eigenvalues and eigenvectors?

• The eigenvalue refers to a variance as explained by particular elements. The variance denotes the diagonal values within the covariance matrix set. When the eigenvalue is larger than 1, then it retains particular components. As such, the average eigenvalue would be 1.
• The eigenvector is the coefficient of the orthogonal transformation of the variables into principal components

52. What is the default value of last parameter when it comes to VLOOPKUP?

It is a reference to finding the closest match and assuming the table is sorted in ascending order. In this case, FALSE/0 would refer to the exact match.

53. Does VLOOKUP refer the case sensitive values?

It is not case sensitive. The text ‘can’ and ‘CAN’ are the same for VLOOKUP.

54. What is the main limitation of the VLOOKUP function?

The lookup value should be at the most left side column within the table array. VLOOKUP only seems right as it cannot look right to left.

55. What are the different types of samplings?

• Stratified sampling
• Cluster sampling
• Simple random sampling
• Systematic sampling

author: patrick algrim
About the author

Patrick Algrim is an experienced executive who has spent a number of years in Silicon Valley hiring and coaching some of the world’s most valuable technology teams.

Want higher salary?

Our comprehensive free guide on increasing your salary through new or existing employment has finally dropped. Get it here.

     

    Share

    Help us by spreading the word