Frequently asked Questions and Answers for Interview Preparation in Data Analytics

We are all in the run to find the most lucrative career option of all time to be able to stand tall and do what it takes to make a strong future. One of such names is data analytics. Being a highly knowledgeable field and one that is growing rapidly, you can be sure to get some of the finest career opportunities.

Millions of companies across the globe take the aid of data analytics to ensure great success and in the process, require expert and professional data analysts to carry out the tasks. In the wake of this, more jobs are being created and that is why, the demand for data analytics course is also on the rise.

However, companies are very particular about who they hire because the job in the question is one that requires the individual to have sharp knowledge, polished skills and soaring abilities. Hence, if you are ready with your training and have completed your course, then its time to get it all straight to be able to crack the interview you wish to ace.

Here is a list of the most frequently asked questions and answers for interview preparation in data analytics that will help you find the right job that suits you the best and will play a big role in advancing your career opportunities. To promote growth in your future, the base of a good company is essential and thus, you need this blog to help win the heart of any interviewer.

 

Data Analytics Interview Questions and Answers

 

Question 1. What are the things I require to become a Data Analyst?

Answer. To be able to make it big as a data analyst, you need the following things –

• You need to be well- versed in a few programming languages such as JavaScript or ETL frameworks, have extensive knowledge on subject of reporting packages and databases such as SQL, SQLite, etc.
• You must possess a certain level of technical knowledge in segmentation techniques, data mining, database design, etc.
• You should have enough knowledge to be able to collect, analyse, organize, and disseminate big data in an efficient manner.
• Possess good knowledge of statistical packages to be able to analyse humongous datasets such as SPSS, Excel, SAS, etc.

 

Question 2. What are the best tools employed for data analytics?

Answer. The best tools employed for data analytics include but are not limited to –

• Google Search Operators
• Google Fusion Tables
• io
• Solver
• Tableau
• KNIME
• OpenRefine
• RapidMiner
• NodeXL

 

Question 3. What will some of your job responsibilities as a data analyst?

Answer. There are certain tasks that a data analyst needs to perform and those include –

• Collection and interpretation of data from various sources. Analysing results afterwards.
• Filtering and cleaning the data that is garnered.
• Offering support to all aspects of data analysis.
• Analysing complicated datasets & identifying hidden patterns.
• Keeping databases secure.

 

Question 4. Explain data validation.

Answer. The process of determining the quality of the data source along with the accuracy of the data is known as data validation. It involves a lot of processes but there are two main ones –

• Data Screening – ensuring the accuracy of the data as well as making sure any redundancies are not there.
• Data Verification – in case a redundancy is found, the evaluation of it happens on multiple steps. Afterwards, a call is taken to make sure the data item is present.

 

Question 5. What is data profiling?

Answer. The method of analysing all the present entities of data in great depths is known as data profiling. The motive is to offer highly precise information on the basis of the data and its attributes that include the frequency of the occurrence, the datatype, etc.

 

Question 6. Give brief explanation of data cleaning.

Answer. Also known as data wrangling, the process of data cleaning is basically a structured and planned way of discovering erroneous content in data and carefully removing them to make sure the quality and standard of the data is utmost.

 

Question 7. What are some of the ways of data cleaning?

Answer. Some of the ways of data cleaning are –

• Replacing data with its median or mean values
• Filling black data in but without causing redundancies
• Using placeholders for empty spaces
• Entire removal of a data block

 

Question 8. Explain data analysis in brief.

Answer. Data analysis is a structured process involving working with and around data by performing many activities including assessing, transforming, ingestion and cleaning it all with the purpose of providing insights, which is then used to drive insights.

 

Question 9. What are the steps of data analysis?

Answer. Data analysis is a long process and involves multiple steps. It all begins with collection of data from various sources. This raw data is then cleaned and processed to remove any obstacle in the process.

Once this is done, analysing of the data starts with the aid of various models. The final step is about reporting as well as making sure the output is finally used to convert the format into a non-technical one, so that audience of all sorts can read and make sense of it.

 

Question 10. What is data mining?

Answer. Data mining is the process of finding hidden patterns in the data.

 

Question 11. Is data analysis better than data mining? If yes, why?

Answer. The results produced by data analysis are preferred more than those by data mining because of the general comprehensibility of the former one. People from more varied fields get to understand the results derived from data analysis than they can from data mining.

 

Question 12. Give any 3 problems encountered by a working data analyst?

Answer. There are quite a few problems encountered by a working data analyst, some of which are listed below –

• The analysis will seriously suffer if the data that has been obtained is either inaccurate or incomplete.
• When the source from which data is derived is not verified, the level of cleaning and pre-processing required on that set of data is super high.
• When the data is being extracted from multiple sources, the merging of them all together takes extra time and energy.

 

Question 13. Explain an outlier.

Answer. The value in a dataset which is considered to be distant from the mean of the central characteristic features of the dataset is known as an outlier.

 

Question 14. How many types of outliers are there? Name them.

Answer. There are two types of outliers namely –

• Univariate outlier
• Multivariate outlier

 

Question 15. Name some of the most widely used Big Data tools.

Answer. Some of the most widely used big data tools are –

• Flume
• Spark
• Hive
• Hadoop
• Mahout
• Scala

 

Question 16. Give me a brief explanation of the KNN imputation method.

Answer. In the simplest of terms, the KNN method is where selection of a distance metric and multiple nearest neighbours is required at the same time. thus, facilitating the prediction of both continuous as well as discrete attributes of the dataset.

 

Question 17. Certain problems are arising upon the data flow from multiple sources. How will you deal with them?

Answer. Problems that arise from multiple-source data flow are quite common but at the same time, there are also ways in which they can be dealt with including –

• The schema can be re-structured to make sure there is now good schema integration
• The presence of same/ similar records needs to be identified and then merged into a single record

 

Question 18. Where does a Pivot table come into use?

Answer. When we talk about the key features of Excel, Pivot tables make it to the top. They facilitate the viewing and summarizing of the complete large datasets in a super simple manner.

A majority of the operations with Pivot tables include drag- and- drop operations which help in the rapid and swift creation of reports.

 

Question 19. Talking about distributed computing environment, which are the top Apache frameworks being used?

Answer. When we talk about distributed computing environment, the top Apache frameworks that are being used are Hadoop and MapReduce.

 

Question 20. Name the steps that are a part of the data analysis project.

Answer. The number of steps that are involved in the data analysis project are numerous. However, the most important ones are –

• Writing a problem statement
• Starting with pre-processing or data cleaning
• Going forward to data exploration
• Moving on to modeling
• Data validation
• Implementing results
• Final verification

 

Question 21. Can you make us understand what Time Series Analysis is?

Answer. Time Series Analysis, which is more commonly referred to as TSA is a universally employed statistical technique, particularly used when working with time-series data and trend analysis.

This technique requires the presence of data at set periods or at certain intervals of time.

 

Question 22. Where does the use of Time Series Analysis come forth?

Answer. The scope of usage from Time Series Analysis is wide and has been seen and felt in multiple domains. Some of the most important places where TSA comes into play include –

• Econometrics
• Astronomy
• Statistics
• Applied science
• Weather forecasting
• Signal processing
• Earthquake prediction

 

Question 23. A Data Analyst employs multiple statistical methodologies. Name some of them.

Answer. A Data Analyst employs multiple statistical methodologies when performing data analysis and some of the most important ones include –

• Rank statistics
• Markov process
• Imputation techniques
• Cluster analysis
• Bayesian methodologies

 

Question 24. Can you give us a brief on Hierarchical Clustering?

Answer. Also known as Hierarchical Cluster Analysis, Hierarchical Clustering is an algorithm that involves grouping of similar objects into a common group referred to as clusters. The ultimate aim is to come up with a set of clusters wherein they contain similar entities but every cluster is different from the other one.

 

Question 25. Is there any line of difference between the concepts of true positive rate and recall?

Answer. The concepts of true positive rate and recall are totally identical. The formula for it is –

Recall = [True Positive] / [True Positive + False Negative]