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.
When one is appearing for an interview, they are being compared with other candidates as well. To think that I can crack it without any prep is good but one should never underestimate the competition as well. It is wise to keep one prepared for an interview. Now this “preparation” sounds vague. The preparation should be strategic, it should begin with an understanding of the company, job role, and culture of the company. And should be escalated to gaining additional knowledge of the domain the interview is for.
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!
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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.
The knowledge of data science would come to the rescue during the data analyst interview.
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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.
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Table of Contents
Top Data Analyst Interview Questions & Answers
1. What are the key requirements for becoming a Data Analyst?
These are standard data science interview questions frequently asked by interviewers to check your perception of the skills required. 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 well-versed with programming languages (XML, Javascript, or ETL frameworks), databases (SQL, SQLite, Db2, etc.), and also have extensive knowledge on reporting packages (Business Objects).
- 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.
- Proficient in using data visualization tools for comprehensible representation.
- A data analyst should be having knowledge of the data visualisation tools as well.
- Data cleaning
- Strong Microsoft Excel skills
- Linear Algebra and Calculation
Along with that, in order to these data analyst interview questions, make sure to represent the use case of all that you have mentioned. Bring a layer to your answers by sharing how these skills will be utilised and why they are useful.
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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 of what your job entails to deliver the impression of being well-versed in your job role and a competent contender for the position.
A data analyst is required to perform the
following tasks:
- 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.
- Implementing data visualization skills to deliver comprehensive results.
- Data preparation
- Quality Assurance
- Report generations and preparation
- Troubleshooting
- Data extraction
- Trends interpretation
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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. Although containing valuable information, an unstructured database is hard to move through and find valuable information. Data cleansing simplifies this process by modifying unorganized data to keep it intact, precise, and useful.
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.
- To answer these types of data analytics interview questions, go into a little explanation to demonstrate your domain knowledge. One can strategize to answer this to show what the journey of data looks like from beginning to end. For example,
- Removal of unwanted observations which are not in reference to the filed of study one is carrying.
- Quality Check
- Data standardisation
- Data normalisation
- Deduplication
- Data Analysis
- Exporting of data
-
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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. Such data science interview questions and data analyst behavioral interview questions are intended to test your knowledge and practical comprehension of the subject. Candidates with ample practical knowledge are the only ones to excel in this question. So make sure to practice tools and analytics questions for your analyst interview and data analyst behavioral interview questions
The most useful tools for data analysis are:
- Tableau
- Google Fusion Tables
- Google Search Operators
- KNIME
- RapidMiner
- Solver
- OpenRefine
- NodeXL
- io
- Apache Spark
- R Programming
- SAS
- Python
- Microsoft Power BI
- TIBCO Spotfire
- Qlik
- Google Data Studio
- Jupyter Notebook
- Looker
- Domo
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| 1 | Data Analysis Course | Inferential Statistics Courses |
| 2 | Hypothesis Testing Programs | Logistic Regression Courses |
| 3 | Linear Regression Courses | Linear Algebra for Analysis |
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, and length, along with their discrete values and value ranges. It assesses source data to understand structure and quality through data collection and performing quality checks on it.
As the name profiling suggests, the data profiling evaluates the data from the specified source, and once that has been done it helps in analysing the data. On the other hand, data mining prepares the statistics and insights of the data. It digs deeper into the data. To answer these data analyst interview questions, one can share how data mining finds the patterns in the data by understanding the correlation between the datasets. Whereas, data profiling analyses the data to understand the actual content and data present in the data set.
On the contrary, data mining aims to identify unusual records, analyze data clusters, and sequence discovery, to name a few. Data mining runs through the prebuilt database to find existing patterns and correlations to obtain value out of it by an optimum implementation. Data mining follows computer-led methodologies and complex algorithms to deliver results.
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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. In brief, the KNN computation method is used to predict the missing values in the dataset. It can be fine to say that it is used as a replacement for the traditional imputation techniques.
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.
- Model preparation for the missing data
- Predict the missing values
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.
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9. Define Outlier
A data analyst interview question and answers guide will not be 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. The outlier values vary greatly from the data sets. These could be either smaller, or larger but they would be away from the main data values. There could be many reasons behind these outlier values such as measurement, errors, etc. 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. Exploratory Data Analysis and its Importance to Your Business
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 groups unlabelled items into classes and groups of similar items. These cluster groups have the following properties:
- Hierarchical or flat
- Hard and soft
- Iterative
- Disjunctive
Clustering can be defined as categorising similar types of objects in one group. The clustering is done to identify similar types of data sets in one group. These data sets share one or more than one quality with each other.
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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. It computes the centroids assuming that it already knows the clusters. It confirms the business assumptions by finding which types of groups exist. It is useful for many reasons, first of all, because it can work with large data sets and is easily accommodative to the new examples.
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 interests. It is used to broaden the options the users could have. Online entertainment applications are another example of collaborative filtering. For example, Netflix shows recommendations basis the user’s behavior. It follows various techniques, such as-
- Memory-based approach
- Model-based approach
13. Name the statistical methods that are highly beneficial for data analysts?
Accurate predictions and valuable results can only be achieved through the right statistical methods for analysis. Research well to find the leading ones used by the majority of analysts for varied tasks to deliver a reliable answer in the analyst interview questions.
- Bayesian method
- Markov process
- Simplex algorithm
- Imputation
- Spatial and cluster processes
- Rank statistics, percentile, outliers detection
- Mathematical optimization
In addition to this, there are various types of data analysis as well, which the data analysts use-
- Descriptive
- Inferential
- Differences
- Associative
- Predictive
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).
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). The N-gram stands for the sequence of N words. It is a probabilistic model having its usage in machine learning, specifically Natural Language Processing (NLP). Speech recognition and predictive texting are the applications of N-gram as it produces the contiguous sequence of n items from the given speech or text. There could be a unigram, bigram, trigram, etc. for example,
| Trigram | Learn |
| Bigram | Learn at |
| Trigram | Learn at upGrad |
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.
A better way to prevent the hash collision would be to use good and appropriate hash functions. The reason is that a good hash function would uniformly distribute the elements. Once the values would be distributed evenly over the hash table there would be lesser chances of having collisions.
Basic Fundamentals of Statistics for Data Science16. 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.
Time Series Analysis analyses the sequence of data points that are collected over different times. This brings the structure to how the analysts record the data, instead of going ahead observing the data points randomly, they observe data over set intervals of time. There are various types of time series analysis-
- Moving average
- Exponential smoothing
- ARIMA
It is used for nonstationary data, data that is dynamic an dconstantly moving. It has applications in various industries such as finance, retail, economics, etc.
17. How should you tackle multi-source problems?
Multi-source problems are a group of computational data composed of dynamic, unstructured, and overlapping data that is hard to go through or obtain patterns from. 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.
The whole meaning of the data analysis is to help in effective decision-making. The data analysis projects are the steps towards achieving it. For example, while undergoing the above-said process, the analysts use the past data and once the data has been analysed it gets put in a presentable form so the decision-making process can be smoother.
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.
- It should be presented in a visualised format. So that the results could be understood and predicted easily.
- Good data is transparent and comprehendible.
- It should be derived from the correct data points and sources.
- It should be simple to understand, simplicity does not necessarily mean weak rather it should be simple and should make sense.
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). It measures how far each number is from the mean. In simple terms, it could be said to be a measure of variability. 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. And also how two variables are related to each other. Positive covariance would tell that two variables are positively related.
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. It requires each data to be analysed separately. It can be either inferential or descriptive. It could possibly give inaccurate results. An example of univariate data could be height. In a classroom of students, there is only one variable which is height.
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. An example of bivariate data would be the income of the employees and the years of experience they hold.
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. An example of multivariate data would be students getting awards in sports function, their class, age, and gender.
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. In simple terms, R Squared measures the regression fitment, whereas the higher R squared measures a good fitment and the lower R Squared measures the low fitment. Whereas, the Adjusted R Squared takes into account those variables which actually had an effect on the performance model.
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.
Version control can also be called source control. It tracks the changes that happen in software. Using certain algorithms and functions manages those changes so the team who is responsible for the task can effectively work on the software without losing efficiency. The version control happens with the use of certain version control tools. It is responsible to manage changes happening in a computer program and saving them. For example, in Google Word Doc, whatever has been added in the doc, can be accessed by the user the next time they visit without having the need to save each change. Also, the changes or edits appeared on a real-time basis to all the users having the access to the doc.
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.
Career as a Data Analyst
Topping as one of the most widely sought-after jobs in the current market and bagging a place in The Future of Jobs report 2020, the data analyst role is significant to brands dealing with and aiming to grow in the digital environment. Thanks to rapid digitization, an enormous amount of data demands a skilled set of hands, a data analyst being one of them.
With every brand leveraging digital interactions to fuel its growth, continuous data flow and profitable usage are necessary. Data analysts work in the same role to deal with heaps of unstructured data and extract value from it. Considering the ongoing digitization, demand for skilled data analysts in the IT market is nowhere going down in the near future.
The above analytics questions allow data analyst aspirants a glance at what they can expect and what they must prepare for the analyst interview questions.
Conclusion
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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What are the talent trends in the data analytics industry?
As Data Science is growing gradually, there is significant growth in some domains as well. These domains are: With the significant growth of the data science and data analysis industry, more and more vacancies of for the data engineers are generating which in turn increases the demand for more IT professionals. With the advancement of technology, the role of data scientists is evolving gradually. Analytics tasks are getting automated which has put the data scientists on the backfoot. Automation may take up the data preparation tasks where data scientists currently spend 70-80% of their time.
Explain cluster analysis and its characteristics
A process in which we define an object without labelling it is known as cluster analysis. It uses data mining to group various similar objects into a single cluster just like in discriminant analysis. Its applications include pattern recognition, information analysis, image analysis, machine learning, computer graphics, and various other fields. Cluster analysis is a task that is conducted using several other algorithms that are different from each other in many ways and thus creating a cluster. The following are some of the characteristics of cluster analysis: Cluster Analysis is highly scalable. It can deal with a different set of attributes. It shows high dimensionality, Interpretability. It is useful in many fields including machine learning and information gathering.
What are outliers and how to handle them?
Outliers are referred to the anomalies or slight variances in your data. It can happen during the data collection. There are 4 ways in which we can detect an outlier in the data set. These methods are as follows: Boxplot is a method of detecting an outlier where we segregate the data through their quartiles. A scatter plot displays the data of 2 variables in the form of a collection of points marked on the cartesian plane. The value of one variable represents the horizontal axis (x-axis) and the value of the other variable represents the vertical axis (y-axis). While calculating the Z-score, we look for the points that are far away from the center and consider them as outliers.
How can I introduce myself in a data analyst interview?
The introductions should be short and confident, you choose to mention the following- Name, Alma Mater Number of years of experience Projects undertaken recently Brief understanding of the most recent project (what was the aim, strategy to achieve) Positive motive behind choosing the field. (It would show the driving factor behind choosing the profession and fitment for the new role. Mention rational reasons like how you are a data-driven person and that is why you chose this field because you like numbers and you relate to the field, etc.)
What skills do you need to be a data analyst?
The following skills are needed to be a data analyst- Data visualisation Data segregation, mining, cleaning MATLAB Problem-Solving Statistical knowledge Python SQL and NoSQL R Critical thinking
What are the duties of a data analyst?
The following are the duties and responsibilities of a data analyst- Define the objective Determine the sources Gather data Clean, segregate, and mine the data Quality Assurance Analyse the data Observe, interpret and predict Cross team collaboration Report generation and presentation of the trends or insights
Does data analyst have future?
Data analysis is known to be a big industry. It is projected to have 11 million jobs by 2026. Source It is advisable to enter the industry now to have the first mover advantage.
What’s next after data analyst?
The career path for a data analyst looks as such- Senior Data Analyst Manager of Analytics Senior Manager of Analytics Director of Analytics Chief Data Officer
Does data analyst do coding?
The data analyst is not expected to do coding. But they should be having the skills to code and should be knowing other programming skills. Having the skillsets of SQL, R, Python, etc would help them to get hired faster. On a usual day, they mostly work with Google Analytics and other domains.
