20 Data Analytics Interview Questions

Introduction

 

In the field of technology, data is one such thing that is widely available from the world, especially nowadays when a collection of data is way easier than it was before. The point of the question is what is done with the data after collecting it? The information available needs to be processed and managed in order to bring forward something important from it. This is where Data Analytics comes into play.

 

Data Analytics is a field of technology that deals with the scientific procedure of analyzing raw structured or unstructured data in order to reach some sort of conclusion about it. 

 

Data analytics techniques and procedures have been turned into manufacturing devices and algorithms that operate on raw data for human utilization. The job of a data analyst is to gather insights from the given data for the benefit of 

 

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There is no lack of data in the world and hence, this field is gaining more and more popularity every day. As a result, many technology enthusiasts or students from Computer science backgrounds are opting for Data Analytics as their career option. To be a data analyst in any reputed company, one needs to sit for interviews facing professionals. 

 

In this article, we are going to discuss 20 popular and important questions that might be asked to an interviewee while facing an interview for the position of Data Analyst.

 

Top 20 Data Analytics questions:

 

1. What is the major difference between Data Mining and Data Profiling?

 

Data mining is a process in which unidentified relevant information is discovered and processed, so it is basically a process of converting raw data into valuable information. 

 

On the other hand, data profiling is a process of evaluation of any database on the basis of its uniqueness, relevance, logic, and consistency. Data profiling cannot be used to identify inaccurate data values.

 

(Also read: Data mining tools)

 

 

2. What is the difference between Data mining and data analysis?

 

Data Mining	Data Analysis
In the process of data mining, the stored data is analyzed for pattern recognition.	Data analysis is a process of organizing and managing raw data in a significant and relevant order.
Mining is done on well-documented data, yet, the extracted results are not easy to interpret	Data analysis is not performed on a clean set of data, hence the process of analysis involves Data Cleaning. And, the results extracted are also easier to interpret

 

3. How can you determine if a data model is well developed or not?

 

To be a well-developed model, the data model is required to fulfill some criteria that will determine its potential. They are:

 

• The performance of a model created for the dataset should be predictable. This is necessary in order to forecast the future.

• If a model can easily adapt to changes in business requirements, it is said to be a good model.

• If the data changes, the model should be able to adjust to the new information.

• Clients should be able to readily consume the model produced for actionable and lucrative outcomes.

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4. What are the problems faced by data analysts on an everyday basis?

 

The common problems encountered by data analysts in any analytics project are:

 

• Handling duplicate data

• Common misspellings

• Collection of meaningful and correct data at the right time

• Identification of overlapping data

• Handling data purging and storage problems

• Data security and compliance issues. 

 

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5. What is data cleansing and discuss the best practices for data cleansing?

 

Data cleansing or data cleaning is a process of identifying and deducting any errors or inconsistencies from the available data in order to enhance its quality.

The best practices that a data analyst can perform for data cleansing are:

 

• The data should be sorted according to different attributes.

• Large databases should be cleansed in a stepwise process and the quality improvement should be done gradually with each step.

• A data analyst should always work with lesser amounts of data as it increases the iteration speed, and hence the efficiency.

• A set of utility tools should be created for handling common cleansing tasks. It may be remapping values depending on a CSV file or SQL database or using regex search-and-replace to blank out any values that don't fit a regex pattern.

• To solve any issues, one should arrange the data by estimated frequency and prioritize the work by tackling the most common problems first. 

• A data analyst should analyze the summary statistics for each column including mean, standard deviation, and the number of missing values. 

• To alter or remove any operations later, one should always keep track of every data cleaning operation.

 

 

6. When is the restraining of a model required?

 

The format of business data does not change on a daily basis, but the data itself does. It is advised to retrain the model if a firm enters a new market, sees a rapid surge in opposition, or sees its own position grow or decline. As a result, it is advised that the model be retrained to reflect changing consumer behaviors as the company dynamics change.

 

 

7. Name some of the best data analysis tools.

 

Some of the best  data analysis tools include Tableau, RapidMiner, Solver, Google Search Operators, io, KNIME, NodeXL, Wolfram Alpha, Google Fusion Tables, and many others.

 

8. What do you mean by the KNN imputation method?

 

The KNN imputation technique attempts to impute missing attribute values by utilizing attribute values that are the closest to the missing attribute values. The distance function is used to determine the similarity of two attribute values.

 

 

9. What are the different validation methods that can be used by data analysts?

 

The most common methods used by data analysts to validate any datasets are:

 

• Field Level Validation- Data validation is performed in each field as the user inputs data in this approach. It aids in the correction of mistakes as you go.

• Form Level Validation- After the user fills out and submits the form, the data is verified using this technique. It scans the whole data entry form at once, validating all of the fields and highlighting any mistakes (if any) for the user to fix.

• Data Saving Validation- This data validation approach is employed when a file or database record is being saved. When numerous data entry forms must be verified, this is usually done.

• Search Criteria Validation- This validation approach is utilized to provide the user with correct and related matches for the keywords or phrases they searched for. This validation method's major goal is to guarantee that a user's search queries provide the most relevant results. (From)

 

 

10.  What is logistic regression?

 

A statistical approach for evaluating a database with one or more independent elements that determine a result is logistic regression.

 

 

11.  What is K-mean Algorithm?

 

K-mean clustering algorithm is a splitting technique that divides items into K groups. The clusters in this technique are spherical, with data points lined around each cluster, and the variation of these clusters is comparable to one another.

 

 

12.  Define Clustering.

 

A method in which the available data is identified and classified into different clusters and groups is called Clustering. Some of the properties of a clustering algorithm are:

 

• Hierarchical or flat

• Hard and soft

• Iterative

• Disjunctive

 

 

13.  How to tackle multi-source problems in data analysis?

 

In order to tackle multi-source problems one first needs to identify similar data and combine all of them into one data record that will have all the necessary attributes except the redundancy. Then they can facilitate schema integration through schema restructuring.

 

 

14. Discuss in brief, what do you mean by univariate, bivariate, and multivariate analysis.

 

Univariate analysis is a descriptive statistical approach that is used to analyze information with only one variable. The range of values, as well as the arithmetic mean of the values, are considered in the univariate analysis.

 

The bivariate analysis examines two variables at the same time to see whether there is an empirical link between them and how strong that link is, or if there are any differences between the variables and how significant those differences are.

 

Multivariate analysis is a bivariate analysis extension. Multivariate analysis uses the concepts of multivariate statistics to observe and analyze several variables (two or more independent variables) at the same time in order to predict the value of a dependent variable for individual subjects.

 

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15. How to tackle suspicious or missing data?

 

To tackle suspected data, a data analyst should first prepare a validation report that contains all the required information of that suspected data like failed validation criteria, date and time of occurrence, etc.  Then the suspicious data should be examined by experienced individuals to assess their acceptability and the invalid data should be issued a validation code and replaced. 

 

The data scientists can use the optimal analytic technique for missing data, such as the deletion method, single imputation methods, model-based methods, and so on.

 

 

16. What is collaborative filtering?

 

Collaborative filtering is a straightforward technique for generating a recommendation system based on user activity. Users-items-interest are the three most critical components of collaborative filtering. 

 

One of the best examples of collaborative filtering is the “recommended for you” section that pops up on any online buying store. This comes up based on the user’s browsing history.

 

 

17. Name some statistical methods that are beneficial for any data analyst.

 

Bayesian method, spatial and cluster process, Markov process, Rank statistics, percentile, outliers detection, Imputation techniques, Simplex algorithm, and mathematical optimization are a few of the statistical methods that are useful in data analytics.  

 

 

18. What are the advantages of version control?

 

Some of the main advantages of version control are as follows:

 

• It enables users to compare files, discover discrepancies, and merge changes in a smooth manner. 

• It aids in the organization of application builds by indicating which version belongs to which category - development, testing, QA, and production.

• It keeps a complete history of project files, which is useful in the event of a central server failure.

• It's ideal for securely storing and managing various versions and variations of code files.

• It allows us to assess the modifications that have been made to the content of various files.

 

 

19.  What is Time Series Analysis?

 

Time series analysis is a method for predicting the output of a process by examining historical data using techniques such as exponential smoothing, log-linear regression, and so on. 

 

 

20.  What is N-gram?

 

In a text or voice, an n-gram is a linked series of n elements. An N-gram is a probabilistic linguistic model that is used to anticipate the next item in a sequence, such as in (n-1).

 

 

Conclusion

 

It is very important for the interviewees to have a good grasp on all the basic and important concepts of Data analytics before sitting for the interview, in order to crack it. In this article, we have covered many important questions that might be asked by the interviewer. But it is essential for the candidates to prepare beyond these and learn about other concepts as well.