Most Asked Data Analyst Interview Questions

Following is a list of frequently asked Data Analyst interview questions and their best possible answers.

1) What is Data Analysis?

Data analysis is a process of inspecting, cleansing, transforming, and modeling data to get useful information to find a conclusion or make a decision. There are several approaches used in Data Analysis to discover helpful information. Data analysis is widely used in every industry for various purposes, and that's why there is a huge demand for Data Analysts worldwide.

2) What are the main requirements to become a Data Analyst?

If you want to become a Data Analyst, you have to fulfill the following requirements:

• You should know some programming languages such as XML, JavaScript, or ETL frameworks, databases such as SQL, SQLite, DB2, etc., and have extensive knowledge of reporting packages (Business Objects).
• You must have the skills and ability to analyze, organize, collect and disseminate big data efficiently.
• You must have technical knowledge in database design, data models, data mining, and segmentation techniques.
• You should have good knowledge of statistical packages for analyzing large datasets such as SAS, Excel, SPSS, etc.

3) What are the essential responsibilities of a Data Analyst?

Following is the list of the most critical responsibilities of a Data Analyst. He has to do the following things:

• Provide support to all data analysis and coordinate with customers and staff.
• Collect and interpret data from multiple sources and analyze results.
• Filter and clean data gathered from multiple sources.
• Offer support to every aspect of data analysis and keep the databases secured.
• Analyze complex datasets and identify the hidden patterns in them.
• Resolve associated business issues for clients and perform an audit on data.
• Analyze results and interpret data using statistical techniques and provide ongoing reports.
• Identify a new process to find out improvement opportunities.
• Acquire data from primary or secondary data sources and maintain databases/data systems.
• Specify a performance indicator to locate and correct code problems.

4) What are the different steps used in the Data Analytics project?

Following are the several steps used in the Data Analytics project:

• Problem definition and detection
• Data exploration
• Preparing the Data
• Modeling
• Validation of data
• Implementation and tracking of the data

5) What is the meaning of data cleansing? What are the best ways to do this?

Data cleansing is a process of detecting and removing errors and inconsistencies from the data to improve the data's quality. It is also called Data Cleaning.

Following are the best ways to do the Data Cleansing:

• Segregate the data into groups according to their respective attributes.
• Break a large amount of data into small datasets and then detect and remove errors and inconsistencies from the data.
• Analyze the statistics of each data column.
• Create a set of utility functions or scripts to apply cleaning tasks.
• Keep track of all the data cleansing operations and add or remove data from the datasets, if required.

6) What are the best tools used for Data Analysis?

Following is a list of some best and most useful tools for Data Analysis:

• Tableau
• Google Fusion Tables
• Google Search Operators
• KNIME
• RapidMiner
• Solver
• OpenRefine
• NodeXL
• IO
• Wolfram Alpha's etc.

7) What are the key differences between data profiling and data mining?

The main difference between Data profiling and Data mining is specified as follows:

Data Profiling is used to focus on analyzing individual attributes of data mainly. It provides valuable information on data attributes such as data type, frequency, length, along with their discrete values and value ranges.

On the other hand, data mining is mainly used to identify unusual records, analyze data clusters, sequence discovery, relation holding between several attributes, etc.

8) What should a data analyst do if he/she finds missing or suspected data?

To deal with missing or suspected data, a Data Analyst should do the following things:

• Apply some data analysis strategies like deletion method, single imputation methods, and model-based methods to detect missing data.
• Make a validation report which contains all information about the suspected or missing data.
• Use some tools to check the suspicious data to assess their validity.
• Replace all the invalid data (if any) with a proper validation code.

9) What are some common problems faced by Data Analysts?

A list of some common problems faced by Data analysts are:

• Duplicate entries
• Missing values
• Illegal values
• Common misspelling
• Varying value representations
• Identify overlapping data etc.

10) What do you understand by logistic regression?

Logistic regression is a statistical method used to examine a dataset in which one or more independent variables define an outcome.

11) What do you understand by the KNN imputation method?

The full form of the KNN imputation method is the "k-nearest neighbors" imputation method. The KNN method is used to identify the "k" samples in the dataset that are similar or close in the space. After that, we use these "k" samples to estimate the missing data points' value.

A dataset may have some missing values. It is common to identify missing values in a dataset and replace them with a numeric value. This is called data imputing or missing data imputation. Each sample's missing values are assigned using the mean value of the "k"-neighbors found in the dataset. This method is called the KNN imputation method.

12) How can you categorize data validation methods?

We can categorize data validation methods into two types:

• Data screening
• Data verification

13) What are the different data validation methods used by Data Analysts?

There are many ways used for datasets validation. Following are some most common data validation methods used by Data Analysts:

Field Level Validation: In this method, data validation is done in each field when a user enters the data. It helps to correct the errors you get in the data.

Form Level Validation: In the form level validation method, the data is validated when the user completes the form and submits it. This method checks the entire data entry form at once, validates all the fields in it, and highlights the errors (if any) so that the user can correct them.

Data Saving Validation: The data saving validation method is used to keep an actual file or database record. Generally, this method is used when we have to validate multiple data entry forms.

Search Criteria Validation: The search criteria validation method is used to provide the user accurate and related matches for their searched keywords, queries, or phrases. This validation method is used primarily to ensure that the users get the most relevant results in return after their search queries.

14) Which are the most famous Apache frameworks for processing extensive data set?

Apache has developed two critical frameworks for processing extensive data set for an application in a distributed computing environment. These names are Hadoop and MapReduce.

15) What are the generally observed missing patterns which data analysis?

Following is the list of some missing patterns that are generally observed during data analysis:

• Missing at random.
• Missing completely at random.
• Missing that depends on the missing value itself.
• Missing that depends on unobserved input variable.

16) What do you understand by the term Outlier in Data Analysis?

In data analysis, the term outlier is commonly used when data analysts refer to a value that appears to be far removed and divergent from a set pattern in a sample.

There are two kinds of outliers used in data analysis:

• Univariate outlier
• Multivariate outlier

There are two methods used for detecting outliers:

Box plot method: The box plot method specifies that if the value is greater or lower than 1.5*IQR (interquartile range), such that it lies above the upper quartile (Q3) or below the lower quartile (Q1), the value is known as an outlier.

Standard deviation method: The standard deviation method specifies that if a value is greater or lower than mean ± (3*standard deviation), it will be an outlier.

17) What is the Hierarchical Clustering Algorithm, and what is its use?

Hierarchical clustering algorithm is used to combine and divide existing groups to create a hierarchical structure that showcases the order in which groups are split or merged.

18) What do you understand by the term Collaborative Filtering?

Collaborative filtering is an algorithm used to create a recommendation system based on behavioral data. For example, online shopping sites set a list of items under "recommended for you" based on your browsing history and previous purchases. This algorithm uses user's behavior, objects, and their interest.

19) What are the most useful statistical methods used by Data Analysts?

The most useful statistical methods used by Data Analysts are:

• Simplex algorithm
• Bayesian method
• Markov process
• Imputation
• Spatial and cluster processes
• Rank statistics, percentile, outliers detection
• Mathematical optimization

20) What do you understand by an N-gram in Data Analysis?

In Data Analysis, N-gram is a connected sequence of n items in a given text or speech. In other words, we can say that an N-gram is a probabilistic language model used to predict the next thing in a particular sequence, as in (n-1).

21) How can you deal with the multi-source problems in Data Analysis?

There are following two ways to deal with the multi-source problems in Data Analysis:

• Restructuring of schemas to accomplish schema integration.
• We have to identify similar records and then merge them into a single record containing all relevant attributes without redundancy.

22) What is a hash table collision? How can you prevent it?

When two separate keys hash to a typical value, a collision occurs called a hash table collision. The hash table collision specifies that we cannot store two different data in the same slot. There are two ways to avoid hash table collisions:

• Separate chaining: In this particular chaining method, we use a data structure to store multiple items hashing to a common slot.
• Open addressing: The open addressing method is used to find out the empty slots and store the items in the first empty slot available.

23) What is the use of the K-mean Algorithm in Data Analysis?

The K-mean algorithm is a famous partitioning method or algorithm. In this algorithm, objects are classified as belonging to one of the K groups.

Following are two main characteristics of the K-mean algorithm:

• The clusters are spherical: The data points in a cluster are centered on that cluster.
• The variance/spread of the clusters is similar: Each data point belongs to the closest cluster.

24) Explain the "Time Series Analysis."

Generally, there are two types of series analysis. It means we can apply series analysis on two domains, the time domain and the frequency domain.

In the time series analysis method, the output forecast of a process is done by analyzing the past data using techniques such as exponential smoothening, log-linear regression method, etc.

25) How can you tackle the multi-source problems?

To tackle the multi-source problems, we need to do the following things:

• First, we have to identify similar data records and combine them into one record that will contain all the useful attributes, minus the redundancy.
• After that, facilitate schema integration through schema restructuring.

26) Which are the different tools required in Big Data?

Most essential tools in Big Data are:

• Hadoop
• Hive
• Pig
• Flume
• Mahout
• Sqoop etc.

27) What are the main characteristics of an exemplary data model?

A data model is considered a good and developed data model if it shows the following characteristics:

• A good data model should have a predictable performance to estimate the outcomes accurately or near accuracy.
• It should be adaptive and responsive to changes to easily accommodate the growing business from time to time.
• It should be easily scalable in proportion to the changes in data.
• It should be consumable for clients and customers so that they can get profitable results.

28) What do you understand by the terms KPI, design of experiments, and 80/20 rule?

We can define the terms KPI, design of experiments, and 80/20 rule as following:

KPI: The full form of KPI is the Key Performance Indicator. It is a metric that contains any combination of spreadsheets, reports, or charts about the business process.

Design of experiments: It is the initial process used to split, sample, and set up the data for statistical analysis.

80/20 rules: This rule specifies that 80 percent of our income comes from 20 percent of our clients.

29) What is the difference between variance and covariance in Data Analysis?

Variance and covariance are both statistical terms. Variance indicates how distant two numbers or quantities are concerning the mean value. So, it only specifies the magnitude of the relationship between the two quantities. It means how much the data is spread around the mean.

On the other hand, covariance is used to specify how two random variables will change together. So, we can say that covariance provides both the direction and magnitude of how two quantities vary concerning each other.

30) What do you understand by waterfall chart, and when do we use it?

The waterfall chart is used to specify both positive and negative values, which lead to the final result value.

For example, if we analyze a company's net income, we must include all the cost values in this chart. Using this type of chart, we can see how the value from revenue to the net income is obtained when we deduct all the costs.

31) What is "Normal Distribution" in Data Analysis?

Normal Distribution is also known as the Bell Curve or Gaussian Curve. It is used to indicate a probability function that describes and measures how a variable's values are distributed and how they differ in their means and their standard deviations. The Normal Distribution curve is always symmetric.

32) What is a Pivot table, and what are the different sections of a Pivot Table?

A Pivot table is a simple feature of Microsoft Excel. It makes the user able to summarize massive datasets quickly. You can use it very easily by dragging and dropping rows/column headers to create reports.

There are four different sections of a Pivot table:

• Values Area: This area is dedicated to reporting values only.
• Rows Area: In this area, the headings are reported, which are present on the left of the values.
• Column Area: The column area specifies the headings at the top of the values area.
• Filter Area: This is an optional filter used to drill down in the data set.

33) Can we make a Pivot Table from multiple tables?

Yes. We can easily create a Pivot Table from multiple different tables if there is a connection between them.

34) What do you understand by univariate, bivariate, and multivariate analysis?

Following are the definition of the Univariate, Bivariate, and Multivariate analysis:

• Univariate Analysis: Univariate analysis is a descriptive statistical technique applied to datasets which contain a single variable. The univariate analysis considers the range of values and also the central tendency of the values.
• Bivariate Analysis: This analysis analyzes two variables simultaneously to explore the possibilities of an empirical relationship between them. It also specifies an association between the two variables, the strength of the association, or any differences between the variables and the importance of these differences between them.
• Multivariate analysis: This analysis is an extension of bivariate analysis. Based on multivariate statistics principles, this analysis observes and analyzes multiple variables (two or more independent variables) simultaneously to predict the value of a dependent variable for the individual subjects.

35) What is the difference between R-Squared and Adjusted R-Squared?

R-Squared and Adjusted R-Squared are both data analysis techniques.

• R-Squared technique: The R-Squared technique is a statistical measure of the variation in the dependent variables, as explained by the independent variables.
• Adjusted R-Squared technique: The Adjusted R-Squared technique is a modified version of the R-squared technique, adjusted for the number of predictors in a model. It provides the percentage of variation explained by the specific independent variables that directly impact the dependent variables.

36) What do you understand by imputation, and what are the different types of imputation techniques?

Imputation is a technique that is used to replace missing data with substituted values. There are mainly two types of imputation techniques: Single Imputation and Multiple Imputation.

Single Imputation: It is further categorized into the following techniques:

• Hot-deck imputation: In this technique, a missing value is imputed from a randomly selected similar record using a punch card.
• Cold deck imputation is very similar to hot deck imputation, but it is more advanced and selects donors from another dataset.
• Mean imputation: This technique replaces the missing value with the mean of that variable for all other cases.
• Regression imputation: This technique replaces the missing value with the predicted values of a variable based on other variables.
• Stochastic regression: It technique is very similar to regression imputation, but it adds the average regression variance to regression imputation.

Multiple Imputation: Unlike the single imputation technique, the multiple imputation techniques is used to estimate the values multiple times.

37) What is Map Reduce in the context of Data Analysis?

Map Reduce is a framework developed by Amazon. It is used to process large data sets by splitting them into subsets, processing each subset on a different server, and then making results.

38) What do you understand by correlogram analysis?

The correlogram analysis is a common form of spatial analysis. It contains a series of estimated autocorrelation coefficients calculated for a different spatial relationship. It is also used to construct a correlogram for distance-based data when the raw data is expressed as distance rather than values at individual points.

39) What are the advantages of version control in Data Analysis?

Following are the key advantages of version control in Data Analysis:

• Version control facilitates us to compare files, identify differences between them, and integrate the changes seamlessly without any problem.
• It also keeps track of applications built by identifying which version is made under which category, i.e., development, testing, QA, and production.
• It is used to maintain a complete history of project files that would be very useful in central server breakdown.
• It is beneficial for storing and maintaining multiple versions and variants of code files securely.
• By using version control, we can see the changes made in the content of different files.

40) How can you highlight the cells containing negative values in an Excel sheet?

A Data Analyst uses conditional formatting to highlight the cells having negative values in an Excel sheet. Following are the steps for conditional formatting:

• Select the cells that contain the negative values.
• Now, go to the Home tab and select the Conditional Formatting
• Now, go to the Highlight Cell Rules and choose the Less Than
• Finally, go to the Less Than option's dialog box and enter "0" as the value.

41) Which imputation method is most used and which is more favorable?

Single imputation is most widely used, but it does not reflect the uncertainty created by missing data at random. So, multiple imputation is more favorable than single imputation in the context of data missing at random.

42) What do you understand by the term Data Wrangling in Data Analytics?

Data wrangling is the process of polishing the raw data. In this process, the raw data is cleaned, structured, and enriched into a desired usable format for better decision making. This process involves discovering, structuring, cleaning, improving, validating, and analyzing the raw data. Data Analysts apply this process to turn and map out large amounts of data extracted from various sources into a more useful format. They use some techniques such as merging, grouping, concatenating, joining, and sorting to analyze the data. After that, it gets ready to be used with another dataset.

43) What is the difference between Data joining and Data blending in Data Analysis?

Difference between data joining and data blending:

44) What is the difference between Overfitting and Underfitting?

Difference between Overfitting and Underfitting:

45) What is the difference between COUNT, COUNTA, COUNTBLANK, and COUNTIF in Excel?

COUNT: This function is used to count numeric cells within a range.

COUNTA: This function counts only the non-blank cells within a range.

COUNTBLANK: This function is used to count blank cells in a range.

COUNTIF: This function is used to count the values if any condition is given.