MCQ for Data Science Users (Paperback)

Dhananjay Bisen

ISBN 10: 9355516215 / ISBN 13: 9789355516213

Editore: BPB Publications, New Delhi, 2024

Riguardo questo articolo

Paperback. This book is a comprehensive manual created to assess and improve your comprehension of many concepts and methodologies in data science. The course encompasses a broad spectrum of subjects, such as data preprocessing, Machine Learning techniques, data visualization, statistical analysis, and additional topics. Every chapter is organized with a series of multiple-choice questions that test your understanding and allow you to evaluate your expertise in the subject. The book's objective is to offer a pragmatic and captivating approach for readers to enhance their proficiency in data science through practical exercises. The book provides an extensive examination of several subjects in data science, encompassing data preprocessing, statistical analysis, Machine Learning techniques, data visualization, and additional areas. Comprehensive coverage of data science concepts and features. Multiple-choice questions to test and assess knowledge effectively. Over 5000 multiple-choice questions for practice. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Codice articolo 9789355516213

Dati bibliografici

• Titolo: MCQ for Data Science Users (Paperback)
• Editore: BPB Publications, New Delhi
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 9355516215
• ISBN 13: 9789355516213
• Rilegatura: Paperback
• Condizione: new

Descrizione articolo

Riassunto

This book intends to provide a collection of various MCQs on data science

Key Features

● Comprehensive coverage of data science concepts and features.

● Multiple-choice questions to test and assess knowledge effectively.

● Over 5000 multiple-choice questions for practice.

Description

This book is a comprehensive manual created to assess and improve your comprehension of many concepts and methodologies in data science. The course encompasses a broad spectrum of subjects, such as data preprocessing, Machine Learning techniques, data visualization, statistical analysis, and additional topics. Every chapter is organized with a series of multiple-choice questions that test your understanding and allow you to evaluate your expertise in the subject.

The book's objective is to offer a pragmatic and captivating approach for readers to enhance their proficiency in data science through practical exercises. The book provides an extensive examination of several subjects in data science, encompassing data preprocessing, statistical analysis, Machine Learning techniques, data visualization, and additional areas.

This extensive knowledge guarantees that readers acquire a full and all-encompassing comprehension of the subject matter. The chapters in this book adhere to a structured framework, which includes multiple-choice questions that enable readers to assess their understanding and grasp of the content.

What you will learn

● Mastering data science concepts through multiple-choice questions.

● Strengthening problem-solving skills by practicing diverse scenarios.

● Interpreting the results of data analyses and Machine Learning models effectively.

● Evaluating the performance of different Machine Learning models using metrics.

● Developing critical thinking skills to assess the suitability of various data science approaches.

● Preparing for exams, interviews, and quizzes etc.

Who this book is for

This data science MCQ book is perfect for anyone looking to test and improve their knowledge of data through multiple-choice questions.

Table of Contents

1. Fundamental of Data Science and Data Analytics

2. Data Science Tools and Applications

3. Fundamentals of Programming

4. Introduction to Python Programming

5. Data Analysis: NumPy and Pandas Library

6. Data Visualization: Matplotlib and Seaborn Library

7. Data Structures and Algorithms

8. Database Management and Warehousing

9. Data Acquisition, Data Mining and Big Data

10. Data Pre-processing and Feature Engineering

11. Probability and Statistics

12. Linear Algebra

13. Calculus and Optimization

14. Artificial Intelligence

15. Machine Learning

16. Deep Learning

17. Pattern Recognition and Knowledge Representation

18. Natural Language Processing and Text Analytics

19. Web Analytics and Mining

20. Computer Vision