Here are the top 10 skills you should verify and the key questions to ask to ensure you’re bringing the right talent on board:
1. Proficiency in SQL
🛠 Skill Verification:
Ability to write complex SQL queries.
Understanding of database management.
1.❓
Can you explain a recent project where you used SQL extensively?
How would you write a SQL query to join two tables and filter the results?
2. Data Cleaning and Preparation
🛠 Skill Verification:
Experience in cleaning and preparing datasets for analysis.
❓
Describe your process for cleaning a dataset with missing or inconsistent values.
How do you handle outliers in your data?
3. Statistical Analysis
🛠 Skill Verification:
Knowledge of statistical methods and their applications.
❓
Can you explain the difference between correlation and causation?
How would you perform a hypothesis test?
4. Data Visualization
🛠 Skill Verification:
Ability to create clear and effective visualizations using tools like Tableau, Power BI, or Excel.
❓:
Can you walk me through a dashboard you created and explain the insights it provides?
How do you decide which type of chart or graph to use for a given dataset?
5. Excel Proficiency-Advanced use of Excel functions and tools.
❓
How do you use pivot tables in Excel?
Can you give an example of a complex Excel formula you’ve used?
6. Proficiency in a programming language such as Python or R.
❓
What libraries or packages do you use in Python/R for data analysis?
Can you describe a script you wrote to automate a data analysis task?
7. Critical Thinking and Problem-Solving-Ability to approach data-related problems methodically.
❓:
Describe a challenging data analysis problem you encountered and how you solved it.
How do you prioritize and manage multiple data analysis projects?
8. Domain Knowledge
Understanding of the specific industry or domain relevant to the job.
❓
How does your experience in [specific industry] help you in analyzing data?
Can you provide an example of a project where domain knowledge was crucial?
9. Communication Skills
Ability to explain complex data insights to non-technical stakeholders.
❓
How do you present your findings to a non-technical audience?
Can you provide an example of a time when you had to explain a complex analysis to someone without a technical background?
10. Attention to Detail
Meticulousness in handling data and ensuring accuracy.
❓:
How do you ensure the accuracy of your analysis?
Can you describe a time when attention to detail helped you catch an error or avoid a potential problem?