Attending a data analyst interview and wondering what are all the questions and discussions you will go through? Before attending a data analysis interview, it’s better to have an idea of the type of data analyst interview questions so that you can mentally prepare answers for them.
In this article, we will be looking at some most important data analyst interview questions and answers. Data Science and Data Analytics are both flourishing fields in the industry right now. Naturally, careers in these domains are skyrocketing. The best part about building a career in the data science domain is that it offers a diverse range of career options to choose from!
Organizations around the world are leveraging Big Data to enhance their overall productivity and efficiency, which inevitably means that the demand for expert data professionals such as data analysts, data engineers, and data scientists is also exponentially increasing. However, to bag these jobs, only having the basic qualifications isn’t enough. Having data science certifications by your side will increase the weight of your profile.
You need to clear the trickiest part – the interview. Worry not, we’ve created this Data analyst interview questions and answers guide to understand the depth and real-intend behind the questions.
Top Data Analyst Interview Questions & Answers
1. What are the key requirements for becoming a Data Analyst?
This data analyst interview question tests your knowledge about the required skill set to become a data scientist.
To become a data analyst, you need to:
- Be able to analyze, organize, collect and disseminate Big Data efficiently.
- You must have substantial technical knowledge in fields like database design, data mining, and segmentation techniques.
- Have a sound knowledge of statistical packages for analyzing massive datasets such as SAS, Excel, and SPSS, to name a few.
2. What are the important responsibilities of a data analyst?
This is the most commonly asked data analyst interview question. You must have a clear idea as to what your job entails.
A data analyst is required to perform the
- Collect and interpret data from multiple sources and analyze results.
- Filter and “clean” data gathered from multiple sources.
- Offer support to every aspect of data analysis.
- Analyze complex datasets and identify the hidden patterns in them.
- Keep databases secured.
3. What does “Data Cleansing” mean? What are the best ways to practice this?
If you are sitting for a data analyst job, this is one of the most frequently asked data analyst interview questions.
Data cleansing primarily refers to the process of detecting and removing errors and inconsistencies from the data to improve data quality.
The best ways to clean data are:
- Segregating data, according to their respective attributes.
- Breaking large chunks of data into small datasets and then cleaning them.
- Analyzing the statistics of each data column.
- Creating a set of utility functions or scripts for dealing with common cleaning tasks.
- Keeping track of all the data cleansing operations to facilitate easy addition or removal from the datasets, if required.
4. Name the best tools used for data analysis.
A question on the most used tool is something you’ll mostly find in any data analytics interview questions.
The most useful tools for data analysis are:
- Google Fusion Tables
- Google Search Operators
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5. What is the difference between data profiling and data mining?
Data Profiling focuses on analyzing individual attributes of data, thereby providing valuable information on data attributes such as data type, frequency, length, along with their discrete values and value ranges. On the contrary, data mining aims to identify unusual records, analyze data clusters, and sequence discovery, to name a few.
6. What is KNN imputation method?
KNN imputation method seeks to impute the values of the missing attributes using those attribute values that are nearest to the missing attribute values. The similarity between two attribute values is determined using the distance function.
7. What should a data analyst do with missing or suspected data?
In such a case, a data analyst needs to:
- Use data analysis strategies like deletion method, single imputation methods, and model-based methods to detect missing data.
- Prepare a validation report containing all information about the suspected or missing data.
- Scrutinize the suspicious data to assess their validity.
- Replace all the invalid data (if any) with a proper validation code.
8. Name the different data validation methods used by data analysts.
There are many ways to validate datasets. Some of the most commonly used data validation methods by Data Analysts include:
- Field Level Validation – In this method, data validation is done in each field as and when a user enters the data. It helps to correct the errors as you go.
- Form Level Validation – In this method, the data is validated after the user completes the form and submits it. It checks the entire data entry form at once, validates all the fields in it, and highlights the errors (if any) so that the user can correct it.
- Data Saving Validation – This data validation technique is used during the process of saving an actual file or database record. Usually, it is done when multiple data entry forms must be validated.
- Search Criteria Validation – This validation technique is used to offer the user accurate and related matches for their searched keywords or phrases. The main purpose of this validation method is to ensure that the user’s search queries can return the most relevant results.
9. Define Outlier
A data analyst interview question and answers guide will not complete without this question. An outlier is a term commonly used by data analysts when referring to a value that appears to be far removed and divergent from a set pattern in a sample. There are two kinds of outliers – Univariate and Multivariate.
The two methods used for detecting outliers are:
- Box plot method – According to this method, if the value is higher or lesser than 1.5*IQR (interquartile range), such that it lies above the upper quartile (Q3) or below the lower quartile (Q1), the value is an outlier.
- Standard deviation method – This method states that if a value is higher or lower than mean ± (3*standard deviation), it is an outlier.
10. What is “Clustering?” Name the properties of clustering algorithms.
Clustering is a method in which data is classified into clusters and groups. A clustering algorithm has the following properties:
- Hierarchical or flat
- Hard and soft
11. What is K-mean Algorithm?
K-mean is a partitioning technique in which objects are categorized into K groups. In this algorithm, the clusters are spherical with the data points are aligned around that cluster, and the variance of the clusters is similar to one another.
12. Define “Collaborative Filtering”.
Collaborative filtering is an algorithm that creates a recommendation system based on the behavioral data of a user. For instance, online shopping sites usually compile a list of items under “recommended for you” based on your browsing history and previous purchases. The crucial components of this algorithm include users, objects, and their interest.
13. Name the statistical methods that are highly beneficial for data analysts?
The statistical methods that are mostly used by data analysts are:
- Bayesian method
- Markov process
- Simplex algorithm
- Spatial and cluster processes
- Rank statistics, percentile, outliers detection
- Mathematical optimization
14. What is an N-gram?
An n-gram is a connected sequence of n items in a given text or speech. Precisely, an N-gram is a probabilistic language model used to predict the next item in a particular sequence, as in (n-1).
15. What is a hash table collision? How can it be prevented?
This is one of the important data analyst interview questions. When two separate keys hash to a common value, a hash table collision occurs. This means that two different data cannot be stored in the same slot.
Hash collisions can be avoided by:
- Separate chaining – In this method, a data structure is used to store multiple items hashing to a common slot.
- Open addressing – This method seeks out empty slots and stores the item in the first empty slot available.
16. Define “Time Series Analysis”.
Series analysis can usually be performed in two domains – time domain and frequency domain.
Time series analysis is the method where the output forecast of a process is done by analyzing the data collected in the past using techniques like exponential smoothening, log-linear regression method, etc.
17. How should you tackle multi-source problems?
To tackle multi-source problems, you need to:
- Identify similar data records and combine them into one record that will contain all the useful attributes, minus the redundancy.
- Facilitate schema integration through schema restructuring.
18. Mention the steps of a Data Analysis project.
The core steps of a Data Analysis project include:
- The foremost requirement of a Data Analysis project is an in-depth understanding of the business requirements.
- The second step is to identify the most relevant data sources that best fit the business requirements and obtain the data from reliable and verified sources.
- The third step involves exploring the datasets, cleaning the data, and organizing the same to gain a better understanding of the data at hand.
- In the fourth step, Data Analysts must validate the data.
- The fifth step involves implementing and tracking the datasets.
- The final step is to create a list of the most probable outcomes and iterate until the desired results are accomplished.
19. What are the problems that a Data Analyst can encounter while performing data analysis?
A critical data analyst interview question you need to be aware of. A Data Analyst can confront the following issues while performing data analysis:
- Presence of duplicate entries and spelling mistakes. These errors can hamper data quality.
- Poor quality data acquired from unreliable sources. In such a case, a Data Analyst will have to spend a significant amount of time in cleansing the data.
- Data extracted from multiple sources may vary in representation. Once the collected data is combined after being cleansed and organized, the variations in data representation may cause a delay in the analysis process.
- Incomplete data is another major challenge in the data analysis process. It would inevitably lead to erroneous or faulty results.
20. What are the characteristics of a good data model?
For a data model to be considered as good and developed, it must depict the following characteristics:
- It should have predictable performance so that the outcomes can be estimated accurately, or at least, with near accuracy.
- It should be adaptive and responsive to changes so that it can accommodate the growing business needs from time to time.
- It should be capable of scaling in proportion to the changes in data.
- It should be consumable to allow clients/customers to reap tangible and profitable results.
21. Differentiate between variance and covariance.
Variance and covariance are both statistical terms. Variance depicts how distant two numbers (quantities) are in relation to the mean value. So, you will only know the magnitude of the relationship between the two quantities (how much the data is spread around the mean). On the contrary, covariance depicts how two random variables will change together. Thus, covariance gives both the direction and magnitude of how two quantities vary with respect to each other.
22. Explain “Normal Distribution.”
One of the popular data analyst interview questions. Normal distribution, better known as the Bell Curve or Gaussian curve, refers to a probability function that describes and measures how the values of a variable are distributed, that is, how they differ in their means and their standard deviations. In the curve, the distribution is symmetric. While most of the observations cluster around the central peak, probabilities for the values steer further away from the mean, tapering off equally in both directions.
23. Explain univariate, bivariate, and multivariate analysis.
Univariate analysis refers to a descriptive statistical technique that is applied to datasets containing a single variable. The univariate analysis considers the range of values and also the central tendency of the values.
Bivariate analysis simultaneously analyzes two variables to explore the possibilities of an empirical relationship between them. It tries to determine if there is an association between the two variables and the strength of the association, or if there are any differences between the variables and what is the importance of these differences.
Multivariate analysis is an extension of bivariate analysis. Based on the principles of multivariate statistics, the multivariate analysis observes and analyzes multiple variables (two or more independent variables) simultaneously to predict the value of a dependent variable for the individual subjects.
24. Explain the difference between R-Squared and Adjusted R-Squared.
The R-Squared technique is a statistical measure of the proportion of variation in the dependent variables, as explained by the independent variables. The Adjusted R-Squared is essentially a modified version of R-squared, adjusted for the number of predictors in a model. It provides the percentage of variation explained by the specific independent variables that have a direct impact on the dependent variables.
25. What are the advantages of version control?
The main advantages of version control are –
- It allows you to compare files, identify differences, and consolidate the changes seamlessly.
- It helps to keep track of application builds by identifying which version is under which category – development, testing, QA, and production.
- It maintains a complete history of project files that comes in handy if ever there’s a central server breakdown.
- It is excellent for storing and maintaining multiple versions and variants of code files securely.
- It allows you to see the changes made in the content of different files.
26. How can a Data Analyst highlight cells containing negative values in an Excel sheet?
Final question in our data analyst interview questions and answers guide. A Data Analyst can use conditional formatting to highlight the cells having negative values in an Excel sheet. Here are the steps for conditional formatting:
- First, select the cells that have negative values.
- Now, go to the Home tab and choose the Conditional Formatting option.
- Then, go to the Highlight Cell Rules and select the Less Than option.
- In the final step, you must go to the dialog box of the Less Than option and enter “0” as the value.
With that, we come to the end of our list of data analyst interview questions and answers guide. Although these data analyst interview questions are selected from a vast pool of probable questions, these are the ones you are most likely to face if you’re an aspiring data analyst. These questions set the base for any data analyst interview, and knowing the answers to them is sure to take you a long way!
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How do I prepare for a data analyst interview?
To crack a data analyst interview firstly have a good business sense of the hiring company’s business model and what they are looking for have a good sense of data interpretation, also technical skills like database, python , R, Sql etc. and lastly attempt practice data analyst interview questions.
What are top skills for data analyst?
As a Data analyst is always looking for at data and trying to interpret it skill are very important for a data analyst some of the top skills are SQL, Microsoft Excel, R or Python–Statistical Programming,Critical Thinking, Presentation Skills, Data Visualization, Machine Learning etc
What are the key requirements for becoming a data analyst?
A degree in Data analytics always helps, or even domain expertise, also knowledge of programming languages, a good understanding of data analytics tools, understanding of statistics and machine learning algorithms, and last but not least is data visualization these are some of key requirements for becoming a data analyst.