Mastering Data Analyst Interview Questions

data analyst interview questions

Mastering Data Analyst Interview Questions

Data analytics has become one of the most sought-after career paths, with businesses relying on data-driven insights for strategic decisions. Preparing for a data analyst interview can feel overwhelming, given the diverse skills required, from data manipulation and visualization to problem-solving and communication. To help you with this interview preparation, I have collected a list of questions covering various topics. Whether you are a fresher or an experienced professional, this guide will help you confidently tackle your next data analyst interview. Mastering Data Analyst Interview Questions

Without more delay, let’s dive into it.

data analyst interview questions

1. What do you mean by Data analysis?

Data analysis is a multidisciplinary field of data science in which data is analyzed using mathematical, statistical, and computer science with domain expertise to discover useful information or patterns from the data. It involves gathering, cleaning, transforming, and organizing data to draw conclusions, forecast, and make informed decisions. The purpose of data analysis is to turn raw data into actionable knowledge that may be used to guide decisions, solve issues, or reveal hidden trends.

2. How do data analysts differ from data scientists?

Data analysts are responsible for collecting, cleaning, and analyzing data to help businesses make better decisions. They typically use statistical analysis and visualization tools to identify trends and patterns in data. Data analysts may also develop reports and dashboards to communicate their findings to stakeholders.

Data scientists are responsible for creating and implementing machine learning and statistical models on data. These models are used to make predictions, automate jobs, and enhance business processes. Data scientists are also well-versed in programming languages and software engineering.

3. What do you mean by collisions in a hash table? Explain the ways to avoid it.

Hash table collisions are typically caused when two keys have the same index. Collisions, thus, result in a problem because two elements cannot share the same slot in an array. The following methods can be used to avoid such hash collisions:

  • Separate chaining technique: This method involves storing numerous items hashing to a common slot using the data structure.
  • Open addressing technique: This technique locates unfilled slots and stores the item in the first unfilled slot it finds.
4. What are the ways to detect outliers? Explain different ways to deal with it.

Outliers are detected using two methods:

  • Box Plot Method: According to this method, the value is considered an outlier if it exceeds or falls below 1.5*IQR (interquartile range), that is, if it lies above the top quartile (Q3) or below the bottom quartile (Q1).
  • Standard Deviation Method: According to this method, an outlier is defined as a value that is greater or lower than the mean ± (3*standard deviation).
5. Define the Data Analysis Process

Data analysis is the process of collecting, cleaning, transforming, and analyzing data to generate insights that can solve a problem or improve business results.

6. What Process Would You Follow While Working on a Data Analytics Project?

Some of the key steps are:

  • Understanding the business problem: This is the first step in the data analysis process. This will tell you what are the questions you’re seeking answers for, what hypothesis are you testing, what parameters to measure, how to measure them, etc.
  • Collecting data: An important function of the data analytics job is to find the data needed to provide the insights you’re seeking. Some of these might be existing data, which you can access instantly. You might also need to collect new data in the form of surveys, interviews, observations, etc. Gathering the information in an accurate and actionable way is crucial.
  • Data exploration and preparation: Now, understand the data itself. The parameters, empty fields, correlations, regression, confidence intervals, etc. Clean your data by removing errors and inconsistencies to make sure it’s ready for meaningful analysis.
  • Data analysis: Manipulate the data in various ways to notice trends and patterns. Pivot tables, plotting, and other visualization methods can help see the answers clearer. Based on the analysis, interpret and present your conclusions.
  • Presenting your analysis: As a data analyst, you will regularly take the findings back to the business teams in a form that they can understand and use. This could be as presentations, or through visualization tools like Power BI.
  • Predictive analytics: Depending on whether it’s your role or not, some data analysts also build machine learning models and algorithms as part of their day job.
7. Define the term ‘Data Wrangling in Data Analytics.

Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format. Techniques such as merging, grouping, concatenating, joining, and sorting are used to analyze the data. Thereafter it gets ready to be used with another dataset.

8. What are the common problems that data analysts encounter during analysis?

The common problems steps involved in any analytics project are:

  • Handling duplicate
  • Collecting the meaningful right data and the right time
  • Handling data purging and storage problems
  • Making data secure and dealing with compliance issues
9. Why did you choose a career in data analytics?

I chose data analytics because I enjoy problem-solving and working with data to uncover insights. It’s a field that combines technical skills with strategic thinking to make data-driven decisions.

10. What is the difference between descriptive and predictive analysis?

Descriptive Analysis: Descriptive analysis is used to describe questions like “What has happened in the past?” and “What are the key characteristics of the data?”. Its main goal is to identify the patterns, trends, and relationships within the data. It uses statistical measures, visualizations, and exploratory data analysis techniques to gain insight into the dataset.

Predictive Analysis: Predictive Analysis, on the other hand, uses past data and applies statistical and machine learning models to identify patterns and relationships and make predictions about future events. Its primary goal is to predict or forecast what is likely to happen in future.

11. Explain data cleansing.

Data cleaning, also known as data cleansing or data scrubbing or wrangling, is basically a process of identifying and then modifying, replacing, or deleting the incorrect, incomplete, inaccurate, irrelevant, or missing portions of the data as the need arises. This fundamental element of data science ensures data is correct, consistent, and usable.

12. What is the difference between data mining and data profiling.

Data mining Process: It generally involves analyzing data to find relations that were not previously discovered. In this case, the emphasis is on finding unusual records, detecting dependencies, and analyzing clusters. It also involves analyzing large datasets to determine trends and patterns in them.

Data Profiling Process: It generally involves analyzing that data’s individual attributes. In this case, the emphasis is on providing useful information on data attributes such as data type, frequency, etc. Additionally, it also facilitates the discovery and evaluation of enterprise metadata.

13. Explain Outlier.

In a dataset, Outliers are values that differ significantly from the mean of characteristic features of a dataset. With the help of an outlier, we can determine either variability in the measurement or an experimental error. There are two kinds of outliers i.e., Univariate and Multivariate. The graph depicted below shows there are four outliers in the dataset.

14. What is the difference between data analysis and data mining.

Data Analysis: It generally involves extracting, cleansing, transforming, modeling, and visualizing data in order to obtain useful and important information that may contribute towards determining conclusions and deciding what to do next. Analyzing data has been in use since the 1960s.

Data Mining: In data mining, also known as knowledge discovery in the database, huge quantities of knowledge are explored and analyzed to find patterns and rules. Since the 1990s, it has been a buzzword.

15. What is Metadata?

Metadata is data that talks about the data in a dataset. That is, it’s not the data you’re working with itself, but data about that data. Metadata can give you information on things like who produced a piece of data, how different types of data are related, and the access rights to the data that you’re working with.

16. Explain the KNN imputation method.

A KNN (K-nearest neighbor) model is usually considered one of the most common techniques for imputation. It allows a point in multidimensional space to be matched with its closest k neighbors. By using the distance function, two attribute values are compared. Using this approach, the closest attribute values to the missing values are used to impute these missing values.

17. What is the significance of Exploratory Data Analysis (EDA)?

Exploratory data analysis (EDA) helps to understand the data better.

  • It helps you obtain confidence in your data to a point where you’re ready to engage a machine learning algorithm.
  • It allows you to refine your selection of feature variables that will be used later for model building.
  • You can discover hidden trends and insights from the data.
18. What is univariate, bivariate, and multivariate analysis?

Univariate, Bivariate and multivariate are the three different levels of data analysis that are used to understand the data.

Univariate analysis: Univariate analysis analyzes one variable at a time. Its main purpose is to understand the distribution, measures of central tendency (mean, median, and mode), measures of dispersion (range, variance, and standard deviation), and graphical methods such as histograms and box plots. It does not deal with the courses or relationships from the other variables of the dataset.

Common techniques used in univariate analysis include histograms, bar charts, pie charts, box plots, and summary statistics.

Bivariate analysis: Bivariate analysis involves the analysis of the relationship between the two variables. Its primary goal is to understand how one variable is related to the other variables. It reveals, Are there any correlations between the two variables, if yes then how strong the correlations is? It can also be used to predict the value of one variable from the value of another variable based on the found relationship between the two.

Common techniques used in bivariate analysis include scatter plots, correlation analysis, contingency tables, and cross-tabulations.

Multivariate analysis: Multivariate analysis is used to analyze the relationship between three or more variables simultaneously. Its primary goal is to understand the relationship among the multiple variables. It is used to identify the patterns, clusters, and dependencies among the several variables.

Common techniques used in multivariate analysis include principal component analysis (PCA), factor analysis, cluster analysis, and regression analysis involving multiple predictor variables.

19. What Is Data Visualization? How Many Types of Visualization Are There?

Data visualization is the practice of representing data and data-based insights in graphical form. Visualization makes it easy for viewers to quickly glean the trends and outliers in a dataset.

There are several types of data visualizations, including:

  • Pie charts
  • Column charts
  • Bar graphs
  • Scatter plots
  • Heat maps
  • Line graphs
  • Bullet graphs
  • Waterfall charts
20. What Is a Hashtable?

A hashtable is a data structure that stores data in an array format using associative logic. The use of arrays means that every value is given its own index value. This makes accessing the data easy.

21. Mention some of the python libraries used in data analysis.

Several Python libraries that can be used on data analysis include:

  • NumPy
  • Bokeh
  • Matplotlib
  • Pandas
  • SciPy
  • SciKit, etc.
22. Name some of the most popular data analysis and visualization tools used for data analysis.

Some of the most popular data analysis and visualization tools are as follows:

Tableau: Tableau is a powerful data visualization application that enables users to generate interactive dashboards and visualizations from a wide range of data sources. It is a popular choice for businesses of all sizes since it is simple to use and can be adjusted to match any organization’s demands.

Power BI: Microsoft’s Power BI is another well-known data visualization tool. Power BI’s versatility and connectivity with other Microsoft products make it a popular data analysis and visualization tool in both individual and enterprise contexts.

Qlik Sense: Qlik Sense is a data visualization tool that is well-known for its speed and performance. It enables users to generate interactive dashboards and visualizations from several data sources, and it can be used to examine enormous datasets.

SAS: A software suite used for advanced analytics, multivariate analysis, and business intelligence.

IBM SPSS: A statistical software for data analysis and reporting.

Google Data Studio: Google Data Studio is a free web-based data visualization application that allows users to create customized dashboards and simple reports. It aggregates data from up to 12 different sources, including Google Analytics, into an easy-to-modify, easy-to-share, and easy-to-read report.

23. What is Time Series analysis?

Time Series analysis is a statistical technique used to analyze and interpret data points collected at specific time intervals. Time series data is the data points recorded sequentially over time. The data points can be numerical, categorical, or both. The objective of time series analysis is to understand the underlying patterns, trends and behaviours in the data as well as to make forecasts about future values.

24. What is Feature Engineering?

Feature engineering is the process of selecting, transforming, and creating features from raw data in order to build more effective and accurate machine learning models. The primary goal of feature engineering is to identify the most relevant features or create the relevant features by combining two or more features using some mathematical operations from the raw data so that it can be effectively utilized for getting predictive analysis by machine learning model.

25. How Would You Define a Good Data Model?

A good data model exhibits the following:

  • Predictability: The data model should work in ways that are predictable so that its performance outcomes are always dependable.
  • Scalability: The data model’s performance shouldn’t become hampered when it is fed increasingly large datasets.
  • Adaptability: It should be easy for the data model to respond to changing business scenarios and goals. Results-oriented: The organization that you work for or its clients should be able to derive profitable insights using the model.
26. How can you handle missing values in a dataset?

This is one of the most frequently asked data analyst interview questions, and the interviewer expects you to give a detailed answer here, and not just the name of the methods. There are four methods to handle missing values in a dataset.

  • Listwise Deletion: In the listwise deletion method, an entire record is excluded from analysis if any single value is missing.
  • Average Imputation: Take the average value of the other participants’ responses and fill in the missing value.
  • Regression Substitution: You can use multiple-regression analyses to estimate a missing value.
  • Multiple Imputations: It creates plausible values based on the correlations for the missing data and then averages the simulated datasets by incorporating random errors in your predictions.
27. What is Collaborative Filtering?

Collaborative filtering is a kind of recommendation system that uses behavioral data from groups to make recommendations. It is based on the assumption that groups of users who behaved a certain way in the past, like rating a certain movie 5 stars, will continue to behave the same way in the future. This knowledge is used by the system to recommend the same items to those groups.

28. What is data normalization, and why is it important?

Data normalization is the process of transforming numerical data into standardized range. The objective of data normalization is scaling the different features (variables) of a dataset onto a common scale, which make it easier to compare, analyze, and model the data. This is particularly important when features have different units, scales, or ranges because if we don’t normalize then each feature has different-different impact which can affect the performance of various machine learning algorithms and statistical analyses.

29. What’s the difference between structured and unstructured data?

Structured and unstructured data depend on the format in which the data is stored. Structured data is information that has been structured in a certain format, such as a table or spreadsheet. This facilitates searching, sorting, and analyzing. Unstructured data is information that is not arranged in a certain format. This makes searching, sorting, and analyzing more complex.

30. What is a Pivot table? Write its usage.

One of the basic tools for data analysis is the Pivot Table. With this feature, you can quickly summarize large datasets in Microsoft Excel. Using it, we can turn columns into rows and rows into columns. Furthermore, it permits grouping by any field (column) and applying advanced calculations to them. It is an extremely easy-to-use program since you just drag and drop rows/columns headers to build a report. Pivot tables consist of four different sections:

Value Area: This is where values are reported.

Row Area: The row areas are the headings to the left of the values.

Column Area: The headings above the values area make up the column area.

Filter Area: Using this filter you may drill down in the data set.

31. What Is the Difference Between Time Series Analysis and Time Series Forecasting?

Time series analysis simply studies data points collected over a period of time looking for insights that can be unearthed from it. Time series forecasting, on the other hand, involves making predictions informed by data studied over a period of time.

32. What Is Logistic Regression?

Logistic regression is a form of predictive analysis that is used in cases where the dependent variable is dichotomous in nature. When you apply logistic regression, it describes the relationship between a dependent variable and other independent variables.

33. What are the different types of Hypothesis testing?

Hypothesis testing is the procedure used by statisticians and scientists to accept or reject statistical hypotheses. There are mainly two types of hypothesis testing:

  • Null hypothesis: It states that there is no relation between the predictor and outcome variables in the population. H0 denoted it.
    Example: There is no association between a patient’s BMI and diabetes.
  • Alternative hypothesis: It states that there is some relation between the predictor and outcome variables in the population. It is denoted by H1.
    Example: There could be an association between a patient’s BMI and diabetes.
34. Explain the Type I and Type II errors in Statistics?

In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.

A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.

35. What Do You Mean by Hierarchical Clustering?

Hierarchical clustering is a data analysis method that first considers every data point as its own cluster. It then uses the following iterative method to create larger clusters:

  • Identify the values, which are now clusters themselves, that are the closest to each other.
  • Merge the two clusters that are most compatible with each other.
36. Explain Data Warehousing.

A data warehouse is a data storage system that collects data from various disparate sources and stores them in a way that makes it easy to produce important business insights. Data warehousing is the process of identifying heterogeneous data sources, sourcing data, cleaning it, and transforming it into a manageable form for storage in a data warehouse.

37. Explain N-gram

N-gram, known as the probabilistic language model, is defined as a connected sequence of n items in a given text or speech. It is basically composed of adjacent words or letters of length n that were present in the source text. In simple words, it is a way to predict the next item in a sequence, as in (n-1).

38. What are the advantages of using version control?

It is also known as source control, version control is the mechanism for configuring software. Records, files, datasets, or documents can be managed with this. Version control has the following advantages:

  • Analysis of the deletions, editing, and creation of datasets since the original copy can be done with version control.
  • Software development becomes clearer with this method.
  • It helps distinguish different versions of the document from one another. Thus, the latest version can be easily identified.
  • There’s a complete history of project files maintained by it which comes in handy if ever there’s a failure of the central server.
  • Securely storing and maintaining multiple versions and variants of code files is easy with this tool.
  • Using it, you can view the changes made to different files.
39. What Is the Difference Between Variance, Covariance, and Correlation?

Variance is the measure of how far from the mean is each value in a dataset. The higher the variance, the more spread the dataset. This measures magnitude.

Covariance is the measure of how two random variables in a dataset will change together. If the covariance of two variables is positive, they move in the same direction, else, they move in opposite directions. This measures direction.

Correlation is the degree to which two random variables in a dataset will change together. This measures magnitude and direction. The covariance will tell you whether or not the two variables move, the correlation coefficient will tell you by what degree they’ll move.

40. What Is a Normal Distribution?

A normal distribution, also called Gaussian distribution, is one that is symmetric about the mean. This means that half the data is on one side of the mean and half the data on the other. Normal distributions are seen to occur in many natural situations, like in the height of a population, which is why it has gained prominence in the world of data analysis.

 

These Interview questions will help you to prepare for your interview. Not only these but there are also many other interview questions which we will be discuss in other upcoming blogs.

All the best for your Interview.

-Vinay neeradi (techy_miki)

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