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Table of contents
1.
Introduction
2.
Easy Level Data Analytics Interview Questions
3.
Medium-Level Data Analytics Interview Questions 
4.
Hard Level Data Analytics Interview Questions 
5.
Conclusion
Last Updated: Mar 27, 2024

Data Analytics Interview Questions

Author Monika Yadav
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Introduction

Hello Ninjas, Looking for Data analytics interview questions? If Yes, then you are at the correct place. Without wasting your time, let’s get started with Data analytics. Do you guys know Nowadays, every company has massive data, but that data does not really mean anything as it is in raw form? So, in such scenarios, Data analytics came into the picture. 

Data Analytics Interview Questions

Data analytics is the process of drawing out meaningful insights from that raw data. By analyzing data, companies can identify areas of improvement, and this will improve productivity.

Easy Level Data Analytics Interview Questions

Question 1: What is data analysis?

Answer: Data analysis is the process of inspecting, transforming, and modeling data to extract valuable data. Data analysis involves different techniques, tools, and methods that can be applied to different data types. Data analysis is used in a wide range of fields, including business, finance, healthcare, social sciences, engineering, and many others.
 

Question 2: What are the different types of processes in data analysis?

Answer: There are different processes like assembling, cleaning, interpreting, transforming, and modeling data. These are used to draw out meaningful insights. The various steps involved in this process are:

  • Collecting data
     
  • Analysing data 
     
  • Creating reports
     

Question 3: What is clustering?

Answer: In Data analysis, Clustering is a technique used to group a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than those in other groups (clusters). 
 

Question 4: What are the properties of clustering algorithms?

Answer: The clustering algorithm, when implemented, it posses different properties, which are:

  • Flat or hierarchical 
     
  • Hard or Soft
     
  • Iterative 
     
  • Disjunctive
     

Question 5: Write down the different ways of data cleaning.

Answer: There are several ways of data cleaning. Some of them are:

  • Data cleaning can be done by removing duplicate data that can occur due to many reasons, such as data entry errors, system errors, or merging data from multiple sources. 
     
  • Inconsistent data can occur when data is entered manually or when data is collected from different sources. 
     

Question 6: What is Hierarchical clustering?

Answer: Hierarchical clustering is an algorithm that helps in making clusters of objects based on similarities. After hierarchical clustering is performed, we obtain cluster sets that differ from each other. This clustering technique groups together similar data points and form a tree-like structure, which is also known as a dendrogram.
 

Question 7: What are the criteria for a good data model?

Answer: The criteria for a good data model are given below:

  • It is inherent
     
  • Its Data can be easily consumed
     
  • It can support new business
     
  • It can evolve new business models
     

Question 8: What are the types of hierarchical clustering?

Answer: Hierarchical clustering is divided into two types:

  • Agglomerative clustering: In agglomerative clustering, every data point is considered as a separate cluster. These clusters are combined iteratively in order to form one cluster. They combined iteratively based on similarity criteria. This technique uses a bottom-up strategy for decomposing clusters. 
     
  • Divisive clustering: In divisive clustering, every data point is considered a single cluster. The cluster is divided recursively into smaller clusters in order to form its own cluster. They divided recursively based on dissimilarity criteria. For decomposing clusters, this technique uses a top-down approach.
     

Question 9: Mention some tools used for data analysis.

Answer: Following are the tools used for data analysis:

  • RapidMiner 
     
  • Solver 
     
  • NodeXL 
     
  • KNIME(Konstanz Information Miner) 
     
  • Wolfram Alpha 
     
  • io 
     
  • Tableau
     
  • Google Search Operators 
     
  • Google Fusion Tables 
     
  • OpenRefine 
     

Question 10: Explain Data cleansing

Answer: In Data analytics, Data cleaning is also known as Data scrubbing or Data wrangling. This is a process to identify and fix errors, irrelevant data, and duplicate data from the raw set.
 

Moving forward, let’s discuss medium-level Data Analytics interview questions.

Medium-Level Data Analytics Interview Questions
 

Question 1: What is the difference between Data Analytics and Data Science?

Answer: The Data Science goal is to discover meaningful insights from massive datasets and extract the finest solutions to resolve the problem, whereas the Data Analytics goal is to display the precise data of retrieved insights.
 

Question 2: In which domain is Time Series Analysis used?

Answer: Time series analysis is used in multiple fields.

  1. Econometrics
     
  2. Astronomy
     
  3. Applied science  
     
  4. Weather forecasting
     
  5. Statistics
     
  6. Signal processing
     
  7. Earthquake prediction
     

Question 3: What do you mean by the term hash table?

Answer: A hash table is a data structure. It stores data in an associative manner. It is a map of keys to values. The data is stored in array format. Here each value has its index value. The index value is unique. This table uses a hash technique to generate an index into an array of slots.

 

Question 4: How to detect outliers?

Answer: There are several ways to detect outliers. Some commonly used methods are:

  • Standard deviation method: According to this method, The value is considered an outlier if and only if the value is higher or lower than mean ± (3*standard deviation)
     
  • Box plot method: According to this method, the value is considered to be an outlier if it exceeds or falls below the 1.5*IQR(Interquartile Range). 
     

Question 5: Which python libraries are used in data analytics?

Answer: Python libraries that are used in data analytics are given below:

Question 6: Write the different sections of a pivot table.

Answer: Pivot tables have four different sections that are:

  • Filter area: This is a section which allows the user to filter the data based on certain criteria. 
     
  • Column area: This is a section which displays categorical variables that the user wants to group their data by. Every unique value will create a new column.
     
  • Rows area: This is a section which displays another categorical variables that the user wants to group their data by. Every unique value will create a new row.
     
  • Values area: This is a section which displays the numerical value that the user wants to summarize in the table.
     

Question 7: What problems do data analysts face while doing analysis?

Answer: The problems that Data Analyst face while doing the analysis are the following:

  • The issue of data overlapping. 
     
  • Spelling mistakes and duplicity in entries.
     
  • Incomplete data.
     
  • The data file needs to be formatted better.
     

Question 8: What do you mean by the term Collaborative Filtering?

Answer: In data analytics, collaborative filtering is a technique used to identify relationships and patterns in data by inspecting the behavior of multiple users or entities. The most common application of collaborative filtering is in the field of recommendation systems. It is used to predict the likelihood that a user will enjoy a particular item based on the preferences of other similar users.
 

Question 9: What are the techniques used to overcome overfitting?

Answer: We can overcome overfitting by using many techniques. Some of them are given below:

  1. By reducing the complexity of the model.
     
  2. Using Regularization in a model.
     
  3. Early Stopping.
     
  4. Creating more data samples from the existing data.
     
  5. Dropouts.
     

Question 10: What are the disadvantages of Data analytics?

Answer: Data analytics has various advantages in terms of extracting valuable insights from data, but at the same time, there are some disadvantages also that can impact its effectiveness. Some of the disadvantages of data analytics are given below:

  • Data privacy and security
     
  • Data quality
     
  • Bias
     
  • Complexity
     
  • Ethical concerns.
     

Moving forward, let’s discuss hard-level Data Analytics interview questions.

Hard Level Data Analytics Interview Questions
 

Question 1: What is the difference between data mining and data profiling 

Answer:  The following are the difference between data mining and data profiling: 

Data Mining Data Profiling 
Data mining refers to the process of identifying patterns in a pre-built database Data profiling analyses raw data from existing datasets.
It cannot identify incorrect or inaccurate data values. It can identify the wrong data at the initial stage of data.
It identifies the hidden patterns, searches for new, valuable, and non-trivial knowledge to generate useful information. It helps in evaluating data sets for consistency, uniqueness, and logic.


Question 2: Define Hadoop Ecosystem.

Answer: Hadoop Ecosystem is a platform that provides several services to resolve big data problems. Apache developed the Hadoop ecosystem.It can process enormous datasets for an application in a distributed computing environment. It consists of the following Hadoop components.

Question 3: What do you mean by cluster sampling and systematic sampling?

Answer: A cluster sample is obtained by dividing the total population under observation into sections or clusters, then randomly selecting one or more of the clusters and using all of its members as the members of the sample. This is usually used when the population is large or there is a large geographic area. In contrast, Systematic sampling is where a researcher assigns a counting number to every member of the population, selects a random number, and selects members for the sample at regular intervals from the starting random number that was selected. For example, let’s say you wanted to know how much time people living in a singles-only apartment complex spent watching Netflix on a weekly basis.
 

Question 4: What do you understand by outlier

Answer: In data analytics, an outlier refers to an observation or data point significantly different from other observations or data points in a dataset. Outliers can be generated by measurement errors, data entry errors, or legitimate variations in the data. Outliers can have a significant impact on statistical analyses, such as mean and standard deviation, and can lead to inaccurate results if not adequately addressed.
 

Question 5: Write the steps involved in a data analytics project.

Answer: The basic steps involved in a data analysis project are given below:

  • The very first step in a data analytics project is to clearly know the problem that needs to be solved.
     
  • After the data has been prepared, the next step is to explore it. To identify patterns, trends, and outliers in the data.
     
  • The next step is to build a statistical or machine learning model that can help answer the key questions and achieve the business objectives of the project.
     
  • When the model is built the next step is to validate it. To ensure that it is accurate and reliable.
     
  • The next step is to communicate the insights and recommendations to the relevant stakeholders.
     
  • Finally, the insights and recommendations from the analysis need to be implemented in the business or organization. 

Throughout the project, it is important to continually monitor and evaluate the results to ensure that the analysis is meeting the business objectives and generating value for the organization.
 

Question 6: Mention the types of Hypothesis testing.

Answer: Nowadays, there are many types of hypothesis testing used. Some of them are given below:

  • ANOVA: ANOVA is the Analysis of variance. The analysis is done between the mean values of numerous groups.
     
  • T-test: This analysis is used when the sample is relatively less, and the standard deviation is unknown.
     
  • Chi-square test: This analysis is used to know the level of association between the categorical variables.
     

Question 7: In Data Analytics, which data validation methods are used?

Answer: The methods which are used for data validation by Data Analytics are:   

  • Field Level Validation: This is a method that validates data at the same time when it is entered.
     
  • Form Level Validation: This method validates after the user submits the form.
     
  • Data Saving Validation: This method validates the data when a file is saved.  
     
  • Search Criteria Validation: This method validates the user search to provide the user with a valid result.
     

Question 8: What are the techniques used to overcome overfitting?

Answer: We can overcome overfitting by using many techniques. Some of them are given below:

  1. By reducing the complexity of the model.
  2. Using Regularization in a model.
  3. Early Stopping.
  4. Creating more data samples from the existing data.
  5. Dropouts.
     

Question 9: Differentiate between Data analysis and Data mining. 

Answer: The difference between data analysis and data mining are:

Data Analysis  Data Mining
Analyzing data provides insight or tests hypotheses.  A hidden pattern is identified and discovered in large datasets.
Data visualization is undoubtedly required.  Visualization is generally optional.
Data-driven decisions can be taken using this way. Data usability is the main objective. 

 

Question 10: Differentiate between linear regression and logistic regression.

Answer: The following are the difference between linear regression and logistic regression:

Linear Regression Logistic Regression
It requires independent variables to be continuous It can have dependent variables with more than two categories
Based on the least-square estimation Based on maximum likelihood estimation
Requires five cases per independent variable Requires at least ten events per independent variable

You can also check out Data Analyst vs Data Scientist here.

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Conclusion

Dealing with many questions, we came to the end of the article. These questions help you to get aware of basic knowledge of data analytics and get an idea of interview questions as well. So far, we have discussed easy, medium, and hard-level data science interview questions. 

Recommended Reading:
Servicenow Interview Questions

 

We hope this article helped you in learning Data analytics interview questions. You can read more such articles on our platform, Coding Ninjas Studio. You will find articles on almost every topic on our platform. You can also consider our Data Analytics Course to give your career an edge over others.

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