Top 20 Commonly Asked Python Interview Questions For Data Analyst or Business Analyst Roles

 Top 20 Commonly Asked Python Interview Questions For Data Analyst or Business Analyst Roles


1. What is Python, and what are its key features?

2. Explain the differences between Python 2 and Python 3.

3. How do you handle missing or null values in a dataset using Python?

4. What are the different data types available in Python?

5. How would you read a CSV file in Python and extract data from it?

6. What is the purpose of NumPy in Python? How would you use it for numerical operations?

7. Explain the concept of data cleaning and preprocessing in Python.

8. How can you handle outliers in a dataset using Python?

9. What is the difference between a list and a tuple in Python?

10. How do you perform data visualization in Python? Name some popular libraries used for this purpose.

11. Explain the concept of a lambda function in Python and provide an example.

12. How do you handle duplicate values in a dataset using Python?

13. What is the purpose of the Pandas library in Python, and how would you use it for data manipulation?

14. How can you merge two datasets in Python using Pandas?

15. What is the purpose of the Matplotlib library in Python, and how would you use it for creating plots and charts?

16. Explain the concept of regular expressions in Python and provide an example of their usage.

17. How do you handle imbalanced datasets in Python?

18. What is the purpose of the sci-kit-learn library in Python, and how would you use it for machine learning tasks?

19. How would you handle multicollinearity in a regression model using Python?

20. Explain the concept of cross-validation in machine learning and how it helps in model evaluation.


#datavisualization #python #data #interview #business #pandas #machinelearning #questions #helpingeachother #helpinghands #sharethis #share #dataanalysis #dataanalyst

Comments

Post a Comment

Popular posts from this blog

🔆Navigating the Skies: Exploring Data Analysis in Aerospace Engineering🔆

🔆Navigating the Skies: Exploring Data Analysis in Aerospace Engineering🔆 Introduction: Aerospace engineering stands at the forefront of innovation, driving advancements in aircraft design, space exploration, and aviation safety. In this dynamic field, data analysis plays a pivotal role in unraveling complex engineering challenges, optimizing performance, and ensuring the safety and reliability of aerospace systems. In this article, we delve into the multifaceted applications of data analysis in aerospace engineering and its transformative impact on the future of flight. 1. **Flight Performance Optimization**:    - Data analysis techniques are utilized to optimize the performance of aircraft, spacecraft, and propulsion systems. By analyzing flight data, aerodynamic parameters, and engine performance metrics, aerospace engineers can identify inefficiencies, improve fuel efficiency, and enhance overall performance.    - Computational fluid dynamics (CFD) simulations and finite element a
Read more

Introduction to Natural Language Processing (NLP)

  Introduction to Natural Language Processing (NLP) Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on enabling computers to understand, interpret, and interact with human language. It involves the development of algorithms, models, and techniques that enable machines to process, analyze, and generate natural language data. NLP plays a crucial role in bridging the gap between human communication and computer understanding. By leveraging NLP techniques, machines can perform tasks such as language translation, sentiment analysis, text summarization, information extraction, speech recognition, question answering, and more. Key Concepts in NLP: Tokenization: Tokenization is the process of breaking down text into individual units called tokens. Tokens can be words, sentences, or even smaller units like characters or subwords. Tokenization forms the foundational step for various NLP tasks. Part-of-Speech (POS) Tagging: POS tagging involves assignin
Read more

An Introduction to Data Governance and Data Quality

🔅 An Introduction to Data Governance and Data Quality 🔆 Introduction In today's data-driven world, organizations rely heavily on data to make critical decisions, gain insights, and drive business growth. However, the value of data lies not only in its quantity but also in its quality and governance. Data governance and data quality are two interrelated concepts that play a crucial role in ensuring data is accurate, consistent, secure, and aligned with business objectives. In this article, we will provide an overview of data governance and data quality, their significance, and the benefits they offer to businesses. Data Governance: Ensuring Data Accountability and Management Data governance refers to the overall management and control of an organization's data assets. It encompasses the processes, policies, standards, and guidelines that ensure data is used, stored, and managed appropriately across the organization. The primary goal of data governance is to establish accountab
Read more