Data Analyst Written Exam Questions

Data Analyst Written Exam Questions

Note: This is a sample set of questions to give you an idea of what you might encounter in a data analyst written exam. The specific questions may vary depending on the company and the role you are applying for.

Section 1: Data Analysis Fundamentals

1. Statistical Concepts:

   • Explain the difference between mean, median, and mode.
   • What is standard deviation and how is it calculated?
   • Describe the concept of correlation and its types (positive, negative, no correlation).

2. Data Cleaning and Preparation:

   • What are common data quality issues and how can they be addressed?
   • Explain the process of data Phone Number normalization and its importance.
   • Describe the concept of outlier detection and its methods.

3. Data Visualization:

   • What are the key principles of effective data visualization?
   • Explain the concept of storytelling with data.

Section 2: SQL and Databases

1. SQL Queries:

   • Write a SQL query to retrieve the top 5 customers with the highest total sales.
   • How would you join two tables in SQL based on a common column?
   • Explain the difference between INNER JOIN, LEFT JOIN, and RIGHT JOIN.

2. Database Concepts:

   • What is a database management system (DBMS)?
   • Describe the difference between relational and non-relational databases.
   • Explain the concept of normalization and its benefits.
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     • Section 3: Data Analysis Tools an d Techniques

       1. Python for Data Analysis:

          • How would you load a CSV file into a Pandas DataFrame?
          • Write a Python code snippet to  Qatar Phone Number Resource  calculate the correlation between two variables.

       2. Machine Learning:

          • Explain the difference between supervised and unsupervised learning.
          • Describe the concept of regression AFB Directory analysis and its types (linear, logistic).
          • What is the purpose of a decision tree algorithm?

       3. Data Mining:

          • What is data mining and how does it differ from data analysis?
          • Explain the concept of association rule mining.
          • Describe the steps involved in the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology.