Technical Interview Questions for Data Analytics

 


Technical Interview questions for Data Analytics 

1. General Data Analysis

  • What is the difference between descriptive, diagnostic, predictive, and prescriptive analytics?
  • Explain how you would handle missing data in a dataset.
  • What are outliers, and how do you deal with them in your analysis?
  • What is the difference between correlation and causation?
  • How do you ensure data quality and consistency in a dataset?
  • Can you explain the concept of sampling? What are different types of sampling techniques?

2. Data Cleaning & Preprocessing

  • How do you handle categorical variables in a dataset?
  • What are the common methods for handling missing values in a dataset?
  • Can you explain what data normalization and data standardization are and when each should be used?
  • How do you detect and handle duplicates in a dataset?
  • Explain how you would deal with imbalanced datasets in classification tasks.

3. Statistical Concepts

  • What is the difference between a population and a sample in statistics?
  • Explain the concept of p-value and how it is used in hypothesis testing.
  • What is the Central Limit Theorem and why is it important?
  • Explain the difference between Type I and Type II errors in hypothesis testing.
  • What are the different types of distributions you might encounter in data analysis? (e.g., Normal, Poisson, Binomial, etc.)
  • Can you explain the concept of a confidence interval?

4. SQL and Database Skills

  • How would you write a SQL query to find the second highest salary from an employee table?
  • Explain the difference between INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.
  • What is the purpose of an index in a database, and how does it improve query performance?
  • What is normalization in database design? Explain the different normal forms.
  • Write a SQL query to count the number of records for each category in a dataset.

5. Data Visualization

  • What is the difference between a histogram and a bar chart?
  • How would you decide which visualization is appropriate for a specific dataset or analysis?
  • Explain the importance of data visualization in data analysis. Can you give an example where it helped to convey insights?
  • What is a box plot, and what insights can you gain from it?
  • What is the difference between a heatmap and a scatter plot? When would you use one over the other?

6. Machine Learning (For More Advanced Analytics Roles)

  • Explain the difference between supervised and unsupervised learning.
  • What is cross-validation, and why is it used in machine learning?
  • How would you handle overfitting in a machine learning model?
  • What are precision, recall, and F1-score? How do they differ from accuracy?
  • Can you explain the bias-variance tradeoff?
  • Explain the differences between linear regression and logistic regression.
  • What is the purpose of feature engineering? Can you provide examples of features you might create from raw data?
  • What is a confusion matrix, and what information can you derive from it?

7. Advanced Analytics Techniques

  • Can you explain time series analysis and how you would approach forecasting with a time series dataset?
  • What is a decision tree, and how does it work?
  • Explain what Principal Component Analysis (PCA) is and when it might be used.
  • What is the difference between bagging and boosting?
  • What is the purpose of regularization in machine learning models?
8. Problem Solving and Case Study Questions
  • Given a dataset of sales over time, how would you predict future sales?
  • If you are given a dataset of customer transactions, how would you identify high-value customers?
  • Imagine you are tasked with analyzing website traffic data to determine the best time to launch a marketing campaign. How would you approach the analysis?
  • Suppose you are given a dataset with customer reviews. How would you extract sentiment or insights from that data?



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