Data Analytics Interview Questions

Data Analytics Interview Questions and Answers

Interview questions and answers are useful while giving any mock test or interview. Data analytics is a widely used technology in multiple sectors. The career of data analytics is highly demanded in today’s life. While giving the interview on this technology following questions and answers are very helpful. At the time of applying for the post of a data analyst, we need to prepare the question and answer of data analytics.

Top 15 Data Analytics Interview Questions and Answers

Below are the top question and answers of the data Analytics as follows. These questions are helpful while giving mock tests or interviews.

Q1. Which steps are involved in any data analytics project?

Answer:

In a data analytics project, various steps are involved. Below are the steps of the data analytics project.

1. Problem understanding – To understand the problem of business and define the goals of the organization.
2. Collecting data – This step involves to gather correct data from multiple sources based on business priorities.
3. Cleaning data – This step involves cleaning unwanted data for making it ready for analysis purposes.
4. Interpret the results – In this step we are interpreting the results and finding the hidden patterns.

Q2. What are the common problems that data analysts face at the time of data analysis?

Answer:

Below are the common problems involved in a data analytics project as follows.

1. Duplicate handling
2. Collect the right data
3. Handle the data purging
4. Make the data source

Q3. Which tools data analysts are using for presentation and analysis purposes?

Answer:

A Data analyst uses MySQL, MSSQL server, MS Excel, Tableau, Python, R, and MS PowerPoint for data analysis and presentation purposes.

Q4. What are the methods to clean the data?

Answer:

The below methods are used to clean the data. We need to define each method at the time of cleaning data in data analytics.

1. Create a data clean plan to understand where the common error will take place.
2. Before working with the data remove the duplicates from the data.
3. Focus on data accuracy.
4. Normalize our data.

Q5. What is descriptive data analytics?

Answer:

Descriptive data analytics provides insights into the past and what happened with the data log. The descriptive data analytics method is using data mining and data aggregation techniques. The data analytics techniques are different from other analytics techniques.

Q6. What are the types of data analytics techniques?

Answer:

There are three types of data analytics techniques used while working with data are as follows.

1. Descriptive – This technique uses data mining techniques.
2. Predictive – This technique uses forecasting techniques and statistical models.
3. Prescriptive – This technique will use simulation algorithms.

Q7. Which types are used by data analysts in sampling techniques?

Answer:

The statistical procedure used to choose the data subset called sampling. The data analyst employs the subsequent five categories of sampling strategies.

1. Cluster sampling
2. Systematic sampling
3. Simple random sampling
4. Stratified sampling
5. Judgmental sampling

Q8. What is the use of univariate data analysis?

Answer:

Univariate analysis is a very simple and easy form of data analysis where we analyze the data of a single variable. Analysis of univariate is described by using Bar charts, and central tendency.

Q9. How we are handling missing values from the dataset?

Answer:

We are using four methods for handling missing values from the dataset as follows. This question is important when giving an interview on data analytics.

1. Litwise deletion – In this method entire record will exclude from the analysis if a single value is missing.
2. Average imputation – This method takes the average value from the participants.
3. Regression substitution – We are using multiple regression analysis if a single value is missing.
4. Multiple imputations – These will create possible values based on missing data and correlations.

Q10. What is data validation?

Answer:

Data validation is involved to ensure that data quality is high and it is more accurate. We can achieve the same by using report checks. There are multiple processes involved in data validation but the important process of data validation is data verification and data screening.

1. Data screening – This makes use of models to ensure that data is accurate and redundancies are not present.
2. Data verification – If redundancy is present in data then we need to evaluate multiple steps on data and then need to call the data item presence.

Q11. Which data validation method is used in data analytics?

Answer:

Multiple types of analytics methods are used in data analytics. Below are the methods of data analytics as follows.

1. Field level validation – This validation is done in every field as the user enters the data for avoiding the error.
2. Form level validation – In this level validation is done at the time user is complete the form.
3. Data saving validation – This time of validation is done at the time of the saving process.
4. Search criteria validation – This type of validation is relevant to the user.

Q12. What is data cleansing in data analytics?

Answer:

Wrangling data is the term for cleaning data. Errors are found and removed using this procedure. These fundamental elements ensure that our data is consistent, correct, and error-free.

Q13. Which skills are required for data analytics?

Answer:

Data analysis requires ML, data cleaning, data visualization, SQL, NoSQL, python, and calculus skills. Data analytics requires important knowledge of SQL and NoSQL. We also require proficiency in python while working with data analytics.

Q14. What is the use of predictive data analytics?

Answer:

Predictive data analytics is understanding the future and answering the question of what could happen. Predictive data analytics uses forecasting techniques and statistical models. For instance, a company that sells ice cream can anticipate how much will be sold and evaluate sales from the previous day and today using predictive analysis.

Q15. What is the use of multivariate analysis?

Answer:

The multivariate analysis involves the analysis of three or multiple variables to understand the relationship between variables. This analysis is similar to the bivariate analysis.

Conclusion

In this article, we have explained the questions and answers of data analytics. These questions and answers are very useful at the time of giving the interview and attending any test related to data analytics.

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