Google’s rigorous technical interviews are well-known across the tech industry, and their Data Analytics interviews are no exception. Recently, Google released an “Interview Warmup” tool to aid candidates applying for Data Analytics positions. This tool offers a glimpse into the types of questions candidates might encounter in the interview process. Here are 15 essential data analytics questions Google expects candidates to know:
1. Data Cleaning Challenges
Not all data arrives in perfect condition. Candidates should be prepared to identify potential errors or problems that might arise during the data-cleaning process.
2. Data Storage Location Significance
Explaining why the geographical location of data storage in multinational companies matters is crucial in understanding data privacy laws and regulations across different countries.
3. Joining Data Sets
Candidates should understand and articulate the common types of joins used to combine data sets from various sources.
4. SQL Subquery
An explanation of what a subquery is in SQL and its relevance in database operations.
5. Presentation Review for Data Visualizations
Highlighting the steps one would take in the final review of a presentation to ensure clear communication of information through data visualizations.
6. Programming Language Selection
Explaining the scenarios where a programming language like R is preferable over spreadsheets or SQL for data analysis tasks.
7. Focus Groups vs. Surveys
Distinguishing between suitable questions for focus groups and those more aptly addressed through survey methodologies.
8. Handling Data Entry Variations
Strategies for dealing with inconsistencies in data entry formats, such as different phone number formats.
9. Mean Age Calculation Issue
Troubleshooting potential reasons for receiving an “N/A” result when calculating the mean age of individuals in a dataset using a programming language.
10. Visualization Selection
Understanding when to utilize scatterplots versus heatmaps to represent relationships between variables in a dataset.
11. Identifying Bias Sources
Recognizing various sources of bias that could influence data analysis and affect the validity of insights.
12. Reports vs. Dashboards
Distinguishing between reports and dashboards and scenarios where a dashboard might be preferable over a report for presenting information to clients.
13. Sample Size and Bias
Discussing the misconception regarding sample size and its correlation with the presence of bias in data.
14. Importance of Keys in Database Management
Explaining the significance of primary and foreign keys in effective database management.
15. Understanding Metadata
Defining metadata and emphasizing its importance in database discussions for organizing and understanding data structures.
