20 Data Wrangling Interview Questions and Answers

Data Wrangling is the process of cleaning and organizing data so that it can be used for analysis. It is an important skill for any data analyst or data scientist, as it ensures that the data is ready for use and is of high quality. When interviewing for a position that requires data wrangling skills, you can expect to be asked questions about your experience and approach to data wrangling. In this article, we review some common data wrangling interview questions and how you should answer them.

Data Wrangling Interview Questions and Answers

Here are 20 commonly asked Data Wrangling interview questions and answers to prepare you for your interview:

1. What is the difference between Data Wrangling and ETL?

Data Wrangling is the process of cleaning and preparing data for analysis. This usually involves tasks such as identifying and dealing with missing values, outliers, and inconsistencies in the data. ETL, on the other hand, is the process of Extracting, Transforming, and Loading data. This usually refers to the process of taking data from one or more sources, manipulating it in some way, and then loading it into a destination database or file.

2. Can you explain what a dirty data record is in context of data wrangling?

A dirty data record is one that contains errors, inconsistencies, or missing values. Data wrangling is the process of cleaning up dirty data records so that they can be more easily analyzed. This usually involves identifying and correcting errors, filling in missing values, and standardizing data formats.

3. Can you explain what an outlier is? How do you deal with them?

An outlier is a data point that is significantly different from the rest of the data in a dataset. There are a few different ways to deal with outliers, depending on the situation. One option is to simply remove them from the dataset. Another option is to transform the data so that the outliers are closer to the rest of the data points. This can be done through methods like Winsorizing or Trimming.

4. How would you go about identifying duplicate records in a database?

There are a few different ways that you could go about identifying duplicate records in a database. One way would be to simply compare each record to every other record in the database and look for matches. Another way would be to create a hash of each record and then compare the hashes to look for duplicates.

5. What are some common problems that can occur when dealing with messy or unclean data?

Some common problems that can occur when dealing with messy or unclean data include:

-Incomplete data: This can happen when some data is missing or has not been collected.
-Inconsistent data: This can happen when data is recorded in different ways or formats.
-Invalid data: This can happen when data is incorrect or does not meet the required standards.
-Duplicate data: This can happen when data is duplicated or redundant.

6. Can you give me some examples of real-world uses cases where data wrangling has played an important role?

Data wrangling has been used in a variety of different fields in order to help make sense of large and complex data sets. For example, data wrangling has been used in the field of genomics in order to help researchers better understand the human genome. In the field of marketing, data wrangling has been used in order to better understand customer behavior. And in the field of finance, data wrangling has been used in order to better understand stock market data.

7. What are some best practices for cleaning up messy data?

There are a few different best practices for cleaning up messy data:

– First, you should make sure that all of your data is in a consistent format. This means that all of your column headers should be spelled the same way, and that all of your data should be in the same format (e.g. all dates should be in the same format, all numbers should be in the same format, etc.).
– Second, you should check for missing values and handle them appropriately. This might mean imputing missing values, or simply dropping rows or columns that have too many missing values.
– Third, you should look for outliers and decide how to deal with them. This might mean dropping them from your data, or transforming them in some way (e.g. by taking the log of all values).
– Finally, you should double-check your data to make sure that everything looks clean and ready to go.

8. What’s your understanding of missing values? What are some ways to handle them?

There are a few different ways to handle missing values, depending on the situation. One common way is to simply impute the missing values, either by using the mean or median of the rest of the data, or by using a more sophisticated technique like k-nearest neighbors. Another way is to simply drop the rows or columns with missing values, although this can introduce bias if the missing values are not randomly distributed.

9. Are there any situations where it makes sense to delete data instead of replacing it or trying to fix it?

In general, it is best to try to fix data or replace it with corrected data if possible. However, there are some situations where it may make more sense to simply delete data. For example, if data is so corrupted that it is not possible to fix it, or if the cost of fixing the data would be prohibitive, then it may be best to delete the data and start from scratch.

10. What are some common mistakes that people make while performing data wrangling?

Some common mistakes that people make while performing data wrangling include:

– Not having a clear goal or understanding of what they want to achieve
– Not taking the time to understand the data they are working with
– Not having a plan for how they will wrangle the data
– Not documenting their work
– Not testing their work

11. What are the different types of data quality issues you’ve had to deal with in previous roles?

I’ve had to deal with a lot of different types of data quality issues in previous roles. One of the most common issues is data that is incomplete or inaccurate. This can be caused by a number of things, including human error, incorrect data entry, or data that has been corrupted. Another common issue is data that is duplicated or redundant. This can be caused by things like multiple copies of data being stored in different places, or by data that has been entered multiple times.

12. What are the pros and cons of using Python vs R for data wrangling?

There are pros and cons to using either Python or R for data wrangling. Python is generally faster and more efficient for larger data sets, while R is more flexible and easier to use for smaller data sets. Python also has a wider range of libraries and tools available for data wrangling, while R is more focused on statistical analysis.

13. What are the differences between local, global, and universal variables in JavaScript?

Local variables are only accessible within the function in which they are declared. Global variables are accessible from anywhere in your code. Universal variables are global variables that are created by the browser and are available to all JavaScript code running on that page.

14. Have you ever used regular expressions to perform data cleansing? If yes, then please describe.

Yes, I have used regular expressions to perform data cleansing. I find them to be a very powerful tool for this purpose. Regular expressions can be used to find and replace patterns in data, which can be very helpful in cleaning up data that is not well-formatted.

15. What are some specific techniques you have used to locate outliers in datasets?

There are a few different techniques that can be used to locate outliers in datasets. One common technique is to simply plot the data and look for points that are far away from the rest of the data. Another technique is to calculate the mean and standard deviation of the data and then look for points that are more than three standard deviations away from the mean.

16. What are some of the most common challenges you face during data wrangling projects?

There are a few common challenges that tend to crop up during data wrangling projects. One is dealing with data that is in a format that is difficult to work with, such as unstructured data. Another challenge is dealing with data that is incomplete or contains errors. Finally, data wrangling projects often involve working with large amounts of data, which can be difficult to manage.

17. What does a PivotTable in Excel do?

A PivotTable is a tool in Excel that allows you to summarize and analyze data. It can be used to create reports and to find patterns in data.

18. When should you use VLookup and when should you use Index/Match?

VLookup is best used when you have a simple table with a single column of data that you need to look up. Index/Match is better when you have a more complex table with multiple columns of data, and you need to be able to specify which column you want to use for the lookup.

19. Can you tell me how you would go about removing duplicates from a dataset?

There are a few different ways to remove duplicates from a dataset, but the most common method is to simply create a new dataset without the duplicates. This can be done by iterating through the original dataset and checking for duplicates as you go. If you find a duplicate, you can either remove it from the original dataset or simply not add it to the new dataset.

20. Can you tell me how you would implement a rolling average in excel?

To calculate a rolling average in excel, you would first need to decide on the size of the window that you want to use. For example, if you wanted to calculate a rolling average over the last 3 days, you would use a window size of 3. Then, you would calculate the average for the first 3 days, the second 3 days, and so on.