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Expert C++

You're reading from   Expert C++ Become a proficient programmer by learning coding best practices with C++17 and C++20's latest features

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781804617830
Length 604 pages
Edition 2nd Edition
Languages
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Authors (5):
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Araks Tigranyan Araks Tigranyan
Author Profile Icon Araks Tigranyan
Araks Tigranyan
Shunguang Wu Shunguang Wu
Author Profile Icon Shunguang Wu
Shunguang Wu
John Asatryan John Asatryan
Author Profile Icon John Asatryan
John Asatryan
Marcelo Guerra Hahn Marcelo Guerra Hahn
Author Profile Icon Marcelo Guerra Hahn
Marcelo Guerra Hahn
Vardan Grigoryan Vardan Grigoryan
Author Profile Icon Vardan Grigoryan
Vardan Grigoryan
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Toc

Table of Contents (24) Chapters Close

Preface 1. Part 1:Under the Hood of C++ Programming
2. Chapter 1: Building C++ Applications FREE CHAPTER 3. Chapter 2: Beyond Object-Oriented Programming 4. Chapter 3: Understanding and Designing Templates 5. Chapter 4: Template Meta Programming 6. Chapter 5: Memory Management and Smart Pointers 7. Part 2: Designing Robust and Efficient Applications
8. Chapter 6: Digging into Data Structures and Algorithms in STL 9. Chapter 7: Advanced Data Structures 10. Chapter 8: Functional Programming 11. Chapter 9: Concurrency and Multithreading 12. Chapter 10: Designing Concurrent Data Structures 13. Chapter 11: Designing World-Ready Applications 14. Chapter 12: Incorporating Design Patterns in C++ Applications 15. Chapter 13: Networking and Security 16. Chapter 14: Debugging and Testing 17. Chapter 15: Large-Scale Application Design 18. Part 3:C++ in the AI World
19. Chapter 16: Understanding and Using C++ in Machine Learning Tasks 20. Chapter 17: Using C++ in Data Science 21. Chapter 18: Designing and Implementing a Data Analysis Framework 22. Index 23. Other Books You May Enjoy

Summary

Data science is an interdisciplinary field that utilizes statistical methods, machine learning algorithms, and data visualization to extract insights from large volumes of data. It involves programming skills, mathematical expertise, and domain knowledge to explore, transform, and model data for informed decision-making and predictions.

The first step in the data science pipeline is data capturing and manipulation. This process involves collecting and organizing data from various sources into a structured format. Data scientists work with large datasets, employing efficient methods to manipulate and transform the data. This includes merging datasets, filtering out irrelevant information, and handling missing or inconsistent data, ensuring a solid foundation for analysis.

Data cleansing and processing are crucial to enhancing data quality. Data scientists address anomalies and errors by identifying and handling missing values, outliers, and inconsistencies. They use imputation...

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