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Top Data Analyst Interview Questions

Here is a list of data analyst interview questions organized according to the career levels of a data analyst for preparation.

With the generation of an enormous amount of data regularly, analyzing and interpreting data is the need of the hour. Data science and data analytics fields are blooming and are expected to have exploding employment in the next coming years. According to Forbes, after reading the report ‘Gartner Top 10 Data and Analytics Trends for 2020‘, they suggest paying attention to three main trends in the industry: becoming a data analyst or scientist, automated decision making using AI, and data marketplaces and exchanges. The employment growth in data analytics results from companies’ demand and high-paying job profiles. Although there is tough competition around the job title, many opt to become data analysts for the thrill of data-driven processes and enthusiasm for data.

Critical requirements for Data Analysts

The minimum education qualification for data analysts is graduation or post-graduation in science with at least mathematics or statistics as a subject. It is a plus to have programming and business or finance knowledge. The basic skills required for the job include knowledge of programming, familiarity with data analysis and data visualization tools, and an understanding of statistics and machine learning algorithms. 

Responsibilities of Data Analysts

Data analysts seek insight into data for making data-driven decisions in the company. The key responsibilities are:

  • Provide reports on the analysis of data using statistical methods.
  • Identifying, analyzing, and interpreting data patterns and trends in datasets. 
  • Collecting, processing, and maintaining datasets and data systems. 
  • Working side-by-side with the management sector to prioritize business needs.
  • Designing new processes for improving data consumption and extraction. 

The data analytics interview questions can vary from company to company as the job profile of data analysts varies greatly. Although there is a specific need for data analyst jobs, the general subjects to keep in mind for a data analytics interview questions are programming in Python or R and SQL, statistics, machine learning, and tools like Excel, Power BI, and Tableau. Here is a list of data analyst interview questions organized according to the career levels of a data analyst. The list consists of data analyst interview questions and answers for preparation.

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List of Data Analyst Interview Questions

Beginner level

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

A good data model has four characteristics:

  • Easy consumption of data: The data in a good data model should be clean, transparent, comprehendible, and reflect insights into the data.
  • Scaling of data: A good data model should be capable of scaling in proportions when a change occurs in data. 
  • Predictable performance: A good data model should have room for performance improvements to get an accurate and precise estimate of the outcomes.
  • Adaptive and responsive: As growing businesses demand changes from time to time, a good data model should be adaptable and responsive to integrate the changes in the model and data.

2. Define overfitting and underfitting.

Overfitting and underfitting are modeling errors for which models fail to make accurate predictions. In overfitting, the model is fitted too well to the training data, as a result, the model produces accurate output on training data but is not able to make accurate predictions for new test data. On the contrary, in underfitting, the model is poorly fitted to the training data and is not able to capture enough trends or underlying patterns in the dataset to make predictions.

3. What is data cleansing?

Data cleansing or cleaning, or wrangling, is a process of identifying and modifying incorrect, incomplete, inaccurate or missing data. This process is important to ensure the data handled is correct and usable and that it won’t provide any further errors. There are five primary issues under data cleansing: dealing with missing data, duplicate data, structural errors, outliers, and multi-sourced data. Also, each issue can be solved with a different method, like deleting or updating missing data and fixing structural errors by thoroughly analyzing the dataset, and so on.  

4. Define data visualization and its types.

Data visualization is the process of representing data graphically to reflect the important information it contains. With visualization, the understanding and analysis of data are easier and more efficient. Many types of data visualization techniques include diagrams, graphs, charts, and dashboards. 

5. Differentiate between variance and covariance.

The statistical definition of variance is the deviation or spread of data set from its mean value, and covariance is the measure of how two random variables are related in a dataset. The main difference between variance and covariance is variance talks about the overall dataset, including all data points, and covariance focuses on two randomly chosen variables in the dataset.

6. Which Python libraries are used for data analytics?

The primary Python libraries used for data analytics are Pandas, NumPy, Matplotlib, and Seaborn. Pandas and NumPy are used for mathematical or statistical computations in the data frame, including describing, summarizing, computing means and standard deviations, updating/deleting rows and columns, and so on. And, Matplotlib and Seaborn are used for data visualization, including commands for graphs and plots, representing the correlation between variables in the data frame, and more. 

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Intermediate level

7. What is an outlier and how are they detected?

An outlier is a data point or value in the dataset that is far away from other recorded data points. It can indicate either variability in measurement or an experimental error. There are many ways to detect outliers, including the box plot method, the Z-score method, and so on.

8. What are the data validation techniques used in data analytics?

Data validation is the process of verifying the dataset through data cleaning and ensuring data quality. There are four main data validation techniques:

  • Field level validation: Data validation starts as the data enters the field, and errors can be fixed under ongoing processing of model building.
  • Form level validation: It is a user-based validation performed while collecting the data. The errors are highlighted as users submit the data and get it fixed.
  • Data saving validation: This validation technique is used when a file or database is saved entirely, and multiple data forms are validated at once.
  • Search criteria validation: The validation method is used when searching or querying the data. Validation at this stage provides users with accurate and relevant results. 

9. Differentiate between the WHERE clause and HAVING clause in SQL.

The WHERE clause operates on row data, and the filter occurs before any groupings are made. In contrast, the HAVING clause operates on aggregated data and filters values from a group.

The syntax of the WHERE clause is:

SELECT column_name(s)

FROM table_name

WHERE condition

The syntax of the HAVING clause is:

SELECT column_name(s)

FROM table_name

WHERE condition

GROUP_BY column_name(s)

HAVING condition

ORDER BY column_name(s)

10. Define a Pivot table in Excel.

A Pivot table in Excel is a way of summarizing large amounts of data. It brings together information from various locations in a workbook and presents it on a table. It is helpful to present data findings and analyze numerical data in detail, which helps query large amounts of data.

Experienced level

11. What is time series analysis and time series forecasting?

Time series analysis is the technique to learn new information from time series data by analyzing them using different statistical methods. Four primary variations are seen in time series analysis: seasonal, trend, cyclical, and random. Time series forecasting can be considered to be based on time series analysis, but in forecasting, the focus is on building a model for predicting future values from previously stored data. 

12. Define collaborative filtering. 

Collaborative filtering is a popular technique used in recommender systems where models provide automatic predictions or filter users’ interests based on past choices. The three major components of collaborative filtering are users, items, and interests. This method is based on user behavioral data, assuming that people who agree on particular items will likely agree again in the future.  

13. What is Hypothesis testing, and name a few forms of hypothesis tests?

Hypothesis testing is a statistical technique to determine the significance of a finding or statement. Two mutually exclusive statements are considered on a population or sample dataset, and this method decides which statement best reflects or is relevant to the sample dataset. There are many forms of hypothesis tests, including p-test, t-test, chi-square test, ANOVA test, and more. These tests have different criteria for considering a statement to be more relevant to the sample data like t-tests computes the difference between the means of a pair of groups, ANOVA compares more than two pair of groups, and so on. 

14. Explain clustering and name properties and types of clustering. 

Clustering is the process of classifying data points into clusters or groups using a clustering algorithm. It helps to identify similarities or similar properties between data points, which can be hierarchical or flat, hard or soft, iterative and disjunctive. The types of clustering are based on the similarities in data points and have four basic types: centroid-based clustering, density-based clustering, distribution-based clustering, and hierarchical clustering. 

15. State the benefits of using version control.

Version control or source control is a mechanism to configure the software so that the changes to the software code can be tracked and managed. There are five benefits of using version control:

  • The process of software development becomes clear and transparent.
  • It helps to distinguish between different document versions so that the latest version can be used.
  • With version control, the storing and maintenance of multiple variants of code files is easy.
  • Analysis of changes to a dataset or code file can be reviewed quickly. 
  • It provides security and can help revive the project in case of failure of the central server. 

As mentioned earlier, the interview questions on data analytics may vary according to the company’s needs. There can be more in-depth questions on Python libraries, Excel, SQL querying, and data visualization tools. This list is an overview of data analyst interview questions that a candidate must know. Prepare for the data analytics interview as per your interests and goal. All the very best!

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Gareema Lakra
Gareema Lakra
I'm an aspiring writer in the field of data science with enthusiasm for tech and content development. I have a master's degree in computer science. Besides work, I'm a bibliophile and dive into the fantasy genre whenever I can.


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