Data Analyst Interview Questions and Answers- Part 2

LISTEN TO DATA ANALYST FAQs LIKE AN AUDIOBOOK

Data Analyst Interview Questions and Answers- Part 2Starting a career as a data analyst can be exciting. You get to work with real data, find trends, and help businesses make better choices. But to land that first job, you need to do well in the interview.

Most interviews are more than just talking about your resume. Interviewers want to see if you understand data tools, can solve problems, and explain things clearly. They might ask you how you would handle missing data or how to write a SQL query. You could also be asked to show what a chart means or how you would fix a data error.

This page has real interview questions that many data analyst candidates face. Use it to study and practice your answers. The better prepared you are, the more confident you’ll feel.

Answer:

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.

Answer:

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).

Answer:

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.

Answer:

Terms KPI, design of experiments, and 80/20 rule are described 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.

Answer:

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.

Answer:

Null hypotheses are one example of a statistical hypothesis. They indicate that there is no statistical significance between the two variable types. It suggests that any difference is due to chance.

Answer:

R-Squared and Adjusted R-Squared are both data analysis techniques and differs in following way:

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.

Answer:

A correlogram analysis is the common form of spatial analysis in geography. It consists of a series of estimated autocorrelation coefficients calculated for a different spatial relationship.  It can be used to construct a correlogram for distance-based data, when the raw data is expressed as distance rather than values at individual points.

Answer:

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.

Answer:

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.

Answer:

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.

Answer:

Here are a few ways in which you can handle workbooks.

  • Try using manual calculation mode.
  • Maintain all the referenced data in a single sheet.
  • Often use excel tables and named ranges.
  • Use Helper columns instead of array formulas.
  • Try to avoid using entire rows or columns in references.
  • Convert all the unused formulas to values.

Answer:

The different types of hypothesis testing are as follows:

  • T-test: T-test is used when the standard deviation is unknown and the sample size is comparatively small.
  • Chi-Square Test for Independence: These tests are used to find out the significance of the association between categorical variables in the population sample.
  • Analysis of Variance (ANOVA): This kind of hypothesis testing is used to analyze differences between the means in various groups. This test is often used similarly to a T-test but, is used for more than two groups.
  • Welch’s T-test: This test is used to find out the test for equality of means between two population samples.

Answer:

The default TCP port assigned by the official Internet Number Authority(IANA) for SQL server is 1433.

Answer:

The various types of joins used to retrieve data between tables are as follows:

  • Inner join: Inner Join in MySQL is the most common type of join. It is used to return all the rows from multiple tables where the join condition is satisfied.
  • Left Join: Left Join in MySQL is used to return all the rows from the left table, but only the matching rows from the right table where the join condition is fulfilled.
  • Right Join: Right Join in MySQL is used to return all the rows from the right table, but only the matching rows from the left table where the join condition is fulfilled.
  • Full Join: Full join returns all the records when there is a match in any of the tables. Therefore, it returns all the rows from the left-hand side table and all the rows from the right-hand side table.

Answer:

The significance of EDA are as follows:

  • Exploratory data analysis (EDA) helps to understand the data better.
  • It helps you obtain confidence in your data to a point where you’re ready to engage a machine learning algorithm.
  • It allows you to refine your selection of feature variables that will be used later for model building.
  • You can discover hidden trends and insights from the data.

Answer:

In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.

A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.

Answer:

The storage of data is a big deal. Companies that use big data have been in the news a lot lately, as they try to maximize its potential. Data storage is usually handled by traditional databases for the layperson. For storing, managing, and analyzing big data, companies use data warehouses and data lakes.

Data Warehouse: This is considered an ideal place to store all the data you gather from many sources. A data warehouse is a centralized repository of data where data from operational systems and other sources are stored. It is a standard tool for integrating data across the team- or department-silos in mid-and large-sized companies. It collects and manages data from varied sources to provide meaningful business insights. Data warehouses can be of the following types:

  • Enterprise data warehouse (EDW): Provides decision support for the entire organization.
  • Operational Data Store (ODS): Has functionality such as reporting sales data or employee data.

Data Lake: Data lakes are basically large storage device that stores raw data in their original format until they are needed. with its large amount of data, analytical performance and native integration are improved. It exploits data warehouses’ biggest weakness: their incapacity to be flexible. In this, neither planning nor knowledge of data analysis is required; the analysis is assumed to happen later, on-demand.

Answer:

Data visualization has grown rapidly in popularity due to its ease of viewing and understanding complex data in the form of charts and graphs. In addition to providing data in a format that is easier to understand, it highlights trends and outliers. The best visualizations illuminate meaningful information while removing noise from data.

Answer:

Most essential tools in Big Data are:

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