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As an entry-level data analyst candidate, the job hunt can feel like a never-ending process.
I’ve applied to countless data analyst interviews at the beginning of my career and was often left feeling lost and confused.
There were often edge-cases, business problems, and tricky technical questions I struggled with, and after each interview round, I’d feel my confidence falter.
After spending 4 years in the industry and helping conduct entry-level interviews, however, I’ve learned more about what employers are looking for in data analyst candidates.
There are typically three areas of focus that we’ll dive into in this article — technical expertise, business problem-solving, and soft skills.
Every interview round will cover some aspect of these broader areas, although each employer places a higher emphasis on different sets of skills.
For example, management consulting firms are big on presentation skills. They want to know if you can present complex technical insights to business stakeholders.
In this case, your soft skills and ability to problem-solve are prioritized more than the technical skill. They don’t care as much about your clean Python code as they do your ability to explain the results of a hypothesis test to the stakeholder.
In contrast, product-based companies or tech startups tend to prioritize technical skills. They often test your ability to code, perform ETL tasks, and handle deliverables in a timely manner.
But I digress.
You came here to learn about how to get a job as a data analyst, so let’s dive straight into the questions you are likely to encounter during the interview process.
Round 1: Data Analyst Technical Interview
Typically, the first round of an entry-level data analyst interview comprises a list of technical questions.
This is either a timed technical test or a take-home assessment — the results of which will be used to determine if you progress to the next level.
Here are some questions you can expect during this interview round, with examples of how they can be answered:
1. What is hypothesis testing?
Sample answer:
Hypothesis testing is a technique used to identify and make decisions about population parameters based on a sample dataset.
It starts by formulating a null hypothesis (H0), which represents the default assumption that there is no effect.
A significance level is then chosen, which is typically 0.05 or 0.10. This is the probability threshold for which the null hypothesis will be rejected.
Statistical tests, such as the T-test, ANOVA, or the Chi-Squared Test will then be applied to test the initial hypothesis using data from the sample population.
A test statistic is then computed, along with a p-value, which is the probability of observing the test result under the null hypothesis.
If the p-value falls below the significance level, then the null hypothesis can be rejected, and there is enough evidence to support the alternative hypothesis.
2. What is the difference between a T-Test and a Chi-Squared Test and when would you use them?
Sample answer:
The T-Test and Chi-Squared test are statistical techniques used to compare the distribution of different groups of data. They are used in different scenarios.
- T-Test: This test is used to compare the means of two groups of quantitative and assess if they are statistically different from each other.
- Chi-Squared Test: This test is used to compare the distributions of categorical data to check if the variables are associated with each other.
Here are situations in which I’d use each test:
- T-Test: Suppose we’d like to understand the effect of an ad on product sales. We’d use a paired T-Test to compare the means of product sales before and after the ad was run.
- Chi-Squared Test: If you’re selling a product and would like to measure the relationship between gender and whether the individual likes the product, a Chi-Squared Test can be used.
3. How do you handle missing data in a dataset?
Sample answer:
There are various ways to handle missing data in a dataset depending on the problem statement and the variable’s distribution. Some common approaches include:
- Removal: If there are only a few missing data points that appear to be random, you can simply drop these entire rows from the dataset.
- Imputation: Depending on the underlying variable distribution, you can choose to impute missing values with the mean, median, or mode. For instance, if the feature is normally distributed, the mean can be used to preserve the overall distribution of the data.
- Forward/Backward Fill: In time-series data, the missing value is often imputed by the previous or next data point.
4. How would you detect and deal with outliers in a dataset?
Sample answer:
To detect outliers, I would visualize the variables using a box plot to identify the points outside the chart’s whiskers.
I would also calculate the Z-score for each variable and identify data points with a Z-score of +3 or -3 as they are typically outliers.
To reduce the impact of outliers, I would transform the dataset using a function like RobustScaler() in Scikit-Learn, which scales the data according to the quantile range.
I might also use a transformation like the log, square root, or BoxCox transformation to normalize the variable’s distribution.
5. Explain the difference between the “Where” and “Having” clauses in SQL.
Sample answer:
The “Where” clause is used to filter rows in a table based on individual conditions and is applied before any groupings are made.
In comparison, the “Having” clause is used to filter records after a table has been aggregated, and can only be used in conjunction with the “Group By” clause.
6. If Table 1 has 100 records and Table 2 has 200 records, what is the range of records you’d expect from an inner join between these tables?
Sample answer:
An inner join returns only records that have matching values between tables. If there are no matching values in the dataset, the result of the inner join might be 0.
If all the rows between Table 1 and Table 2 match, then the query will return the total number of records in Table 1, which is 100.
Therefore, the range of expected records from an inner join between these tables is anywhere between 0 to 100.
Preparing for the data analyst technical interview
Notice that the above questions are centered around data preprocessing and analysis, SQL, and statistics.
In some cases, you might be given an ER diagram and some tables and be asked to write an SQL query on the spot. You might even be expected to do pair programming, where you’re given a dataset and need to solve a problem together with the interviewer.
Here are a few resources that will help you ace the technical SQL interview:
1. How to learn SQL for data analysis in 2024
2. Learn SQL for data analytics in 4 hours
Round 2: Data Analyst Interview — Business Problem-Solving
Let’s say you’ve made it through the technical interview.
This means that you meet the technical requirements of the employer and are now one step closer to landing the job.
But you aren’t out of the woods just yet.
Most data analyst interviews comprise case-study-type questions, where you’ll be given a dataset and asked to analyze it to solve a business problem.
Here is an example of a case-study-type question that you might encounter in a data analyst interview:
How will you evaluate the success of a marketing campaign?
Business Case: We are launching a marketing campaign to increase product sales and brand awareness. The campaign will include a mix of in-store promotions and online ads. How will you evaluate its success?
Here is a sample answer to the question above, outlining each step that one might take when faced with the above scenario:
- Step 1: To assess the success of this marketing campaign, we first must define success metrics, such as an increase in sales, increased footfall to the store, and improved customer engagement.
- Step 2: Collect data from the online ad campaign and in-store attendance.
- Step 3: Compare current metrics like store footfall to similar metrics before the marketing campaign was launched.
- Step 4: Assess if any improvement in conversions or sales is statistically significant using methods like a paired T-Test. For proportions, like Click-Through-Rates, a Chi-Squared test can be implemented.
- Step 5: Perform A/B testing on ad creatives and social media posts to identify the most impactful drivers behind sales and conversions.
Preparing for the data analyst problem-solving interview
Similar to the technical interview, this might be an on-the-spot question, where you’re presented with the problem statement and need to work out the steps to achieve a solution.
Or it could even be a take-home assessment that takes about a week to complete.
Either way, the best way to prepare for this round is to practice.
Here are some learning resources I’d recommend exploring to ace this round of your data analyst interview:
1. How to solve a data analytics case study problem
2. Data analyst case study interview
Round 3: Data Analyst Interview — Soft-Skills and Culture Fit
Many people aren’t too concerned about the soft-skill round of their interview.
This is where candidates get confident that they’re about to be made an offer — since they’ve made it through the most “difficult” interview rounds.
But don’t get cocky just yet.
I’ve seen many promising prospects get rejected because they didn’t have the right attitude or didn’t match the company culture.
While this section of the interview cannot be quantified like the previous rounds and is mostly based on what impression you leave the interviewers with, it is often the qualifying factor that makes a company choose you over other candidates.
Here are some questions you might expect during this interview:
1. Describe a time when you explained a technical concept to a non-technical stakeholder.
Sample answer:
In my previous role, I was asked to present complex concepts to the marketing team at my organization.
They wanted to understand how our new customer segmentation model worked and how it could be used to improve campaign performance.
I started by illustrating each concept with a visual aid. I also created personas for each customer segment, assigning names to each user group to make them more digestible to stakeholders.
The marketing team clearly understood the value behind the segmentation model and used it in a subsequent campaign, which led to a 15% improvement in sales.
Note: If you have no prior experience and this is the first data analyst position you are applying for, then you can provide an example of how you would approach this situation if faced with it in the future.
2. Can you tell me about the latest data analytics project you worked on?
Sample answer:
In my latest data analytics project, I analyzed the demand for various skills required in data-related jobs in my country.
I collected data by scraping 5,000 listings on job platforms and preprocessed this data in Python.
Then, I identified the prominent terms in these job listings, such as “Python”, “SQL,” and “communication.”
Finally, I built a Tableau dashboard displaying the frequency at which each skill appeared in these job listings.
I wrote an article explaining my findings from this project and uploaded my code to GitHub.
3. According to you, what is the most important trait a data analyst should have and why?
Sample answer:
I believe that the most important trait for a data analyst to have is curiosity.
In all my past projects, I’ve been driven to learn more about the data I was presented with due to curiosity.
My first data analytics project, for example, was created solely due to curiosity. I wanted to understand whether female representation in Hollywood had improved over the years, and how the gender dynamic had changed over time.
Upon collecting and exploring the data, I discovered that movies with female directors typically had lower ratings than those with male directors.
Instead of stopping at this surface-level analysis, I was curious to understand why this was the case.
I performed further analysis by collecting the genres of these movies and gaining a better understanding of the target audience and realized that the female-directed movies in my dataset had lower ratings due to them being concentrated in a genre that was more poorly rated.
It was correlation, not causation.
I believe that it takes a curious person to uncover these insights and dive deeper into observed trends instead of simply taking them at face value.
Preparing for the data analyst behavioral interview
I recommend actually writing down your answers to some of these questions beforehand — just as you would in any other interview round.
Culture and personality fit is really important to hiring managers since an individual who doesn’t adhere to the team’s way of operating can cause friction further down the line.
You must research the company’s culture and overall direction, and learn about how this aligns with your overall goals.
For example, if the company’s environment is fast-paced and everyone is working on cutting-edge technology, gauge whether this is a place you’d thrive in.
If you’re someone who wants to keep up with industry trends, learn as much as possible, and move up the career ladder quickly, then this is the place for you.
Make sure to convey that message to your interviewer, who likely shares a similar ambition and passion for growth.
Similarly, if you’re the kind of person who prefers a consulting environment because you enjoy client work and breaking down solutions to non-technical stakeholders, then find a company that aligns with your skills and gets the message across.
In simple terms, play to your strengths, and make sure they are conveyed to the employer.
While this might sound too simplistic, it is a better approach than simply applying to every open position you see on Indeed and wondering why you’re getting nowhere in the job hunt.
10 Data Analyst Interview Questions to Get a Job — Next Steps
If you’ve managed to follow along this far, congratulations!
You now understand the 3 types of questions asked in data analyst interviews and have a strong grasp of what employers are looking for in entry-level candidates.
Here are some potential next steps you can take to improve your chances of landing a job in the field:
1. Create Projects
Projects are a great way for you to stand out amongst other candidates and start getting job offers. You can watch this video to learn more about how to create projects to land your first job in the field.
2. Build a Portfolio Website
I also recommend building a portfolio website to showcase all your work in one place. This will improve your visibility and maximize your chances of getting a data analyst role.
If you don’t know where to start, I have an entire video tutorial teaching you to build a portfolio website from scratch with ChatGPT.
3. Improve Your Technical Skills
Brush up on skills like statistics, data visualization, SQL, and programming. There are countless resources that go into these topics in greater detail, and my favorites include Luke Barousse’s YouTube channel,W3Schools, and StatQuest.
 
 
Natassha Selvaraj is a self-taught data scientist with a passion for writing. Natassha writes on everything data science-related, a true master of all data topics. You can connect with her on LinkedIn or check out her YouTube channel.