21 Data Analytics Interview Questions You Should Prepare For

Data analysis is the act of gathering, cleaning, transforming, and analyzing data in order to provide insights that can be used to solve problems or enhance business results.

            The common process of Data Analysis with few key steps:

• Understanding the business problem: This first step determines why you need data analysis, considers which metrics to follow, what hypothesis you are testing, etc.
• Collecting data: Once you’ve defined the purpose, it’s time to collect data from sources. It can be existing data or will have to collect by conducting surveys, interviews, discussions, etc.
• Cleaning the data: This includes cleaning unwanted information to make it high-quality data by removing major errors, duplicates, and unwanted data points & filling the major gaps to bring structure to the data.
• Analyzing the data: This step carries out analyzing & manipulating the data. This can be done by different techniques based on the type of insights you are looking for. It includes Descriptive Analysis, Diagnostic Analysis, Predictive Analysis, and Prescriptive Analysis. (Read More About The Data Analysis Types Here.)
• Interpreting & sharing your insights: The final step is to interpret the results gained by data analysis to provide solutions to business problems and share the interpreted data through data visualization tools like Power BI or Tableau.

2. Define the difference between data profiling and data mining.

Data Profiling	Data Mining
It is analyzing & manipulating data from an existing source.	It is analyzing the gathered information via interviews, surveys, etc.
The purpose is to provide accurate, consistent, & error-free data.	The purpose is to build machine learning techniques for real-time problems.
The application involves targeted advertising, image recognition, fraud detection, etc.	The application involves credit analysis, business intelligence, customer behavior, etc.

3. Explain Data Validation & its methods.

Data validation is a process of ensuring the accuracy and quality of the source data before using or processing it. You risk making decisions based on incomplete data that isn't truly representative of the current situation if you don't validate your data. It is used in Data Warehousing & ETL process. 

Few types of Data Validation checks or rules:

• Data Type Check: This ensures that data entered into the field has the correct data field. 
• Code Check: It verifies that a field is selected from a valid list of values or that specific formatting guidelines are adhered to.
• Format Check: Most of the data types have a predefined format. This check ensures that the data is in the correct format.
• Uniqueness Check: It checks for duplicate entries based on one or two unique parameters defined.
• Length Check: It ensures that the correct number of characters is entered into the field.

4. What are the characteristics of a good data model?

An abstract model of ordered data items and their relationships based on actual objects is referred to as a data model.

Few characteristics or criteria of a good data model:

• Easily consumable data
• Large data changes can be easily scalable
• Provides predictable performance
• Can adapt to changes in requirements

5. Define Outlier.

An outlier is a data point that deviates significantly from the dataset's average features. There are two methods to treat an outlier: Box plot & Standard Deviation method.

6. What is Data Wrangling?

Data wrangling refers to a number of steps to formulate to transform unstructured data into formats that are easier to work with. The precise approaches vary from project to project depending on the data you're using and the objective you're trying to achieve.

7. What Is the Difference Between Variance, Covariance, and Correlation?

Variance, Covariance, and Correlation are statistical measures for determining the connection between data points in a data set.

Variance: It is a measurement of how much each value in a dataset deviates from the mean. The dataset is more evenly distributed the larger the variance.

Covariance: It measures how two randomly generated variables in a dataset will change collectively. Two variables travel together if their covariance is positive; if not, they move in opposite directions. 

Correlation: This quantifies the magnitude and direction. The correlation coefficient will tell you how much the two variables will move, while the covariance will tell you whether they move at all.

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Along with these technical questions, you should be prepared to answer a few personal & situational questions.

Common Personal Data Analytics Questions:

1. Brief us on your professional background or experience in data analytics.
2. What data analytics certifications or training have you received?
3. What is the largest set of data you have ever worked with?
4. How do you handle pressure and stress?
5. What are your long-term data analysis goals?
6. What would you bring to our company & why would it be a good decision to hire you?
7. What are your greatest strengths & weakness as a data professional?

Common Situational Data Analytics Questions:

1. How would you handle a situation where you receive a data set that you believe has suspicious or missing data?
2. What is the biggest challenge you’ve encountered in data analytics and how did you address it?
3. Please provide an example of a situation in which you demonstrated leadership capabilities on the job.
4. Tell me about the most recent data analytics project you worked on and the core steps you took to complete it.
5. Describe a time when you had to persuade others. How did you get buy-in?
6. How would you manage "messy" data?
7. If you've ever had to collaborate with stakeholders who have a weak technical foundation and knowledge of databases and data. What approach would you take?

Now that you are familiar with the kind of questions that are frequently asked in the Data Analytics job interviews, let’s also look at how you can get those interviews and crack them.

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• Master the art of extracting the data & doing complex analyses & manipulations by SQL 
• Build charts, interactive dashboards, and story interfaces for visualization of data and trends
• Learn key Data Analytics & Visualization tools like MySQL, Power BI, Tableau, Excel and Knime
• Use DAX language to build and handle data models through the use of formulas & expressions
• Learn how to automate tasks, perform ETL, create Data Models, and present insights using Dashboards
• Enter the world of Machine Learning via Predictive Analytics & Modeling with Knime along with case studies
• Master advanced Power Query, Computations & Visualizations

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