Data analysts are in high demand as companies increasingly rely on data to drive business decisions. As a result, data analyst interviews are becoming more complex and technical in nature. Employers want to ensure candidates have the hard skills needed to extract insights from data. Here are some of the most common technical questions asked in data analyst interviews.
Statistics
Statistics questions test a candidate’s ability to analyze and interpret data. Interviewers may ask about common statistical concepts like standard deviation, p-values, confidence intervals, and statistical significance. For example:
- What is the difference between mean, median and mode? When would you use each one?
- How can you determine if a variable follows a normal distribution?
- Explain what a p-value tells you about statistical significance.
Being able to accurately describe key statistics terminology and interpret statistical output demonstrates to interviewers that a candidate has the foundation to perform in a data analyst role.
SQL
Since SQL skills are required for extracting and manipulating data, interviewers will probe candidates’ SQL knowledge. Some example SQL interview questions include:
- Write a query to display customer names and order dates for all customers with more than 2 orders.
- How would you find duplicate records in a table? What are the pros and cons of removing them?
- Explain the difference between an inner and outer join.
Candidates should be prepared to write basic to intermediate level SQL queries. Being able to interpret and improve existing queries also demonstrates analytical abilities.
Data Visualization
Data visualization skills allow analysts to clearly communicate insights from data analysis. Interviewers often ask candidates about creating charts and graphs. For example:
- When would you choose a bar chart vs a pie chart?
- What are some best practices for dashboard design?
- Describe how you would visualize website traffic data to reveal trends.
Candidates should highlight experience visualizing and presenting real-world data. Knowledge of tools like Tableau, Power BI and advanced Excel is valued.
Python and R
Fluency in Python and R is becoming essential for data analysts. Candidates may be asked to describe their experience with Python/R packages for data wrangling and visualization. For example:
- How can you handle missing values in Pandas/dplyr?
- Give examples of effective data visualizations you have created in Matplotlib/ggplot.
- What machine learning models have you worked with using Scikit-learn/Caret?
Sharing sample code and analytics projects in Python/R on GitHub can demonstrate hands-on experience.
Big Data Systems
Companies with large, complex datasets often use big data tools like Hadoop, Spark and NoSQL databases. Interviewers may ask candidates about their experience with:
- Retrieving big data from sources like HDFS and S3.
- Loading data into a data lake and processing with Spark.
- Querying data with Hive, Pig or Spark SQL.
While junior analysts won’t be expected to be experts, familiarity with big data ecosystems is valued.
Technical Problems
Many interviews include technical problems where candidates have to analyze sample data sets. These allow employers to assess analytical thinking and coding skills in action. Some examples include:
- Clean and preprocess an imperfect real-world data set in Python/R.
- Build a dashboard in Tableau from sample sales data.
- Query a dataset and summarize insights into key metrics.
Approaching these technical challenges systematically and explaining the thought process aloud is key.
Domain Expertise
Depending on the industry, candidates may be tested on technical knowledge specific to that field. For example:
- Marketing: web analytics metrics, A/B testing, attribution modeling
- Finance: valuation techniques, risk analysis metrics, forecasting methods
- Healthcare: clinical study designs, health stats, patient segmentation
Researching the industry’s use of data analysis demonstrates passion and commitment to that field.
Communication Skills
While not purely technical, analytical roles require communicating complex data insights to stakeholders. Interviewers often assess candidates’ ability to explain technical concepts to a non-technical audience. Clear data storytelling and presentation skills are highly valued.
Conclusion
In summary, data analyst interviews will thoroughly assess both hard and soft skills. Technical questions test candidates on statistics, SQL, Python/R, visualization, big data and communication abilities. Preparation and hands-on experience with real-world data are key to stand out from other applicants. With the exponentially growing demand for data talent, mastering data analyst interview techniques can lead to exciting career opportunities.
