You Need to Know these Data Analyst Interview Questions

My technical recruiter friends have emailed me the top questions they ask Data Analysts.

Below is a compilation of those questions. I’ve made the answers in simple bullet points.

Print this page and have it handy for your phone interview. Think of it as a quick reference guide, so you don’t get stuck.

Note, they normally ask only 3 or 4 of these questions on a 1st or 2nd phone/video interview.

Data Analyst Interview Questions

Q1: Can you explain the difference between descriptive, diagnostic, predictive, and prescriptive analytics?

• Descriptive: Summarizes historical data to identify patterns or trends

• Diagnostic: Analyzes historical data to determine causes of past outcomes

• Predictive: Utilizes historical data to forecast future events or trends

• Prescriptive: Recommends actions to optimize outcomes

Q2: How do you handle missing or inconsistent data in a dataset?

• Identify missing or inconsistent data using exploratory data analysis

• Impute missing values using methods like mean, median, or mode

• Use interpolation or regression techniques for time-series data

• Remove records with missing values if they represent a small portion of the dataset

• Create a separate category for missing values if they hold significant information

Q3: What are some key performance indicators (KPIs) you would use to measure the success of an e-commerce business?

• Conversion rate: Percentage of visitors making a purchase

• Average order value: Total revenue divided by the number of orders

• Customer acquisition cost: Cost of acquiring a new customer

• Customer lifetime value: Predicted net profit from a customer over their lifetime

• Cart abandonment rate: Percentage of users adding items to cart but not completing a purchase

Q4: How do you determine the sample size needed for a statistically significant survey result?

• Define the population size, margin of error, and desired confidence level

• Choose an appropriate probability distribution (e.g., normal distribution)

• Calculate the required sample size using a formula or calculator

• Adjust the sample size for finite populations, if necessary

• Ensure sample is representative of the target population

Q5: What are some common data quality issues and how would you address them?

• Incomplete data: Impute missing values or remove records if necessary

• Inaccurate data: Validate and correct data using reliable sources or domain knowledge

• Inconsistent data: Standardize units, formats, and conventions across the dataset

• Duplicate data: Identify and remove duplicate records using deduplication methods

• Outliers: Assess and treat outliers based on their impact on analysis or model performance

Q6: Explain the difference between a star schema and a snowflake schema in a data warehouse.

• Star schema: Central fact table connected to denormalized dimension tables

• Snowflake schema: Central fact table connected to normalized dimension tables

• Star schema offers faster query performance and simpler design

• Snowflake schema saves storage space and maintains data integrity

• Choice depends on organization's needs, such as query speed or storage optimization

Q7: How do you ensure that your data analysis results are reproducible?

• Maintain detailed documentation of data sources, methodologies, and assumptions

• Use version control systems to track changes in code and data

• Automate data processing and analysis workflows using scripts or tools

• Share code, data, and results with collaborators or stakeholders

• Validate results by cross-checking with alternative methods or independent sources

Q8: How do you select the most appropriate data visualization technique for a given dataset or problem?

• Identify the purpose of the visualization (e.g., comparison, distribution, or relationship)

• Consider the audience's technical knowledge and preferences

• Choose a chart type that accurately represents the data and desired insights

• Account for the complexity and size of the dataset

• Ensure the visualization maintains data integrity and avoids misleading representations

Q9: What is the purpose of A/B testing, and how do you analyze the results?

• A/B testing: Controlled experiment comparing two versions of a product or feature

• Purpose: Determine the most effective version based on a pre-defined metric

• Randomly assign users to treatment (A) or control (B) groups

• Monitor performance metrics and collect data during the testing period

• Analyze results using hypothesis testing, calculate p-value, and determine statistical significance

Q10: What steps would you take to conduct an exploratory data analysis (EDA)?

• Assess dataset structure, dimensions, and variable types

• Generate summary statistics to understand central tendency, dispersion, and distribution

• Visualize data using histograms, box plots, scatter plots, or heatmaps

• Identify outliers, missing values, and inconsistencies in the data

• Examine relationships and correlations between variables

Q11: What are the key differences between a relational database and a NoSQL database?

• Relational database: Structured data, schema-based, tables with relationships

• NoSQL database: Unstructured or semi-structured data, flexible schema, various data models

• Relational databases rely on SQL for querying, while NoSQL uses multiple query languages

• Relational databases prioritize consistency and integrity; NoSQL prioritizes scalability and performance

• Use case depends on data type, required scalability, and desired data consistency

Q12: How would you detect and handle outliers in a dataset?

• Visualize data using box plots, histograms, or scatter plots to identify potential outliers

• Use statistical methods like Z-scores, IQR, or Tukey's fences to define outlier thresholds

• Investigate potential data entry errors, data processing mistakes, or extreme but valid values

• Handle outliers by removing, transforming, or capping them based on their impact on analysis or models

Q13: What are some key differences between time series analysis and cross-sectional analysis?

• Time series analysis: Observations collected sequentially over time, focusing on trends and patterns

• Cross-sectional analysis: Observations collected at a single point in time, comparing different subjects

• Time series analysis deals with autocorrelation and seasonality; cross-sectional analysis does not

• Time series analysis often uses ARIMA, Exponential Smoothing, or LSTM models; cross-sectional analysis uses regression or classification models

• Time series analysis requires evenly spaced data points; cross-sectional analysis does not

Q14: Explain the concept of data normalization and its importance in data analysis.

• Data normalization: Scaling or transforming variables to a standard range or distribution

• Importance: Ensures fair comparison, equal weighting, and improved performance for distance-based algorithms

• Common techniques: Min-max scaling, Z-score standardization, log transformation

• Normalization may be necessary for certain models (e.g., k-means clustering or PCA)

Q15: Describe the process of creating and using a data dictionary for a dataset.

• Data dictionary: Comprehensive documentation of dataset variables, types, descriptions, and permissible values

  Creation process:

• Review data sources, data collection methods, and existing documentation

• Examine dataset variables, types, and values through exploratory data analysis

• Consult domain experts or stakeholders for clarification and validation

• Document variable names, descriptions, types, units, and permissible values

• Update data dictionary as new variables or changes are introduced

• Usage: Improve data understanding, maintain consistency, facilitate collaboration, and ensure accurate analysis

Q16: What are the key differences between data lakes and data warehouses?

• Data lakes: Store raw, unprocessed data in various formats (structured, semi-structured, unstructured) for flexible, large-scale analytics

• Data warehouses: Store structured data in an organized, schema-based manner for efficient querying and reporting

• Data lakes offer schema-on-read, while data warehouses use schema-on-write

• Data warehouses rely on ETL processes for data integration, while data lakes use ELT processes

• Data lakes are more suitable for exploratory analysis, machine learning, and real-time processing; data warehouses excel at historical analysis and reporting

Q17: How do you approach creating visualizations that display hierarchical or network data?

• Chart selection: Choose appropriate chart types for hierarchical or network data, such as tree maps, sunburst charts, or network graphs

• Layout: Optimize the layout to effectively represent hierarchical relationships or network connections while minimizing clutter

• Interactivity: Implement interactive features, such as zooming, panning, or tooltips, to help users explore complex data structures

• Visual encoding: Use visual elements, such as color, size, or line thickness, to encode additional data attributes or relationships

• Focus and context: Provide a clear focus on specific data points or levels while maintaining the broader context of the hierarchy or network

Q18: How do you create effective visualizations for geospatial data?

• Map type: Choose an appropriate map type (e.g., choropleth, heat map, point map) based on the data and analysis objectives

• Projections: Select suitable map projections to accurately represent spatial relationships and minimize distortion

• Colour mapping: Use colour effectively to represent data values or categories on the map

• Context: Provide contextual information, such as reference points or labels, to help users interpret the map

• Interactivity: Implement interactive features, such as zooming or tooltips, to allow users to explore geospatial data in more detail

Cheers Shano