Book Image

Expert C++ - Second Edition

By : Marcelo Guerra Hahn, Araks Tigranyan, John Asatryan, Vardan Grigoryan, Shunguang Wu
5 (1)
Book Image

Expert C++ - Second Edition

5 (1)
By: Marcelo Guerra Hahn, Araks Tigranyan, John Asatryan, Vardan Grigoryan, Shunguang Wu

Overview of this book

Are you an experienced C++ developer eager to take your skills to the next level? This updated edition of Expert C++ is tailored to propel you toward your goals. This book takes you on a journey of building C++ applications while exploring advanced techniques beyond object-oriented programming. Along the way, you'll get to grips with designing templates, including template metaprogramming, and delve into memory management and smart pointers. Once you have a solid grasp of these foundational concepts, you'll advance to more advanced topics such as data structures with STL containers and explore advanced data structures with C++. Additionally, the book covers essential aspects like functional programming, concurrency, and multithreading, and designing concurrent data structures. It also offers insights into designing world-ready applications, incorporating design patterns, and addressing networking and security concerns. Finally, it adds to your knowledge of debugging and testing and large-scale application design. With Expert C++ as your guide, you'll be empowered to push the boundaries of your C++ expertise and unlock new possibilities in software development.
Table of Contents (24 chapters)
1
Part 1:Under the Hood of C++ Programming
7
Part 2: Designing Robust and Efficient Applications
18
Part 3:C++ in the AI World

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...