Hands-on NumPy for Numerical Analysis

Unlock the Power of NumPy to Accelerate Data Analysis and Computing.

Key Features
● Master NumPy concepts with hands-on examples and real-world use cases.
● Learn efficient numerical data analysis and performance optimization.
● Explore advanced NumPy functions for data science and ML workflows.

Book Description
NumPy is the backbone of numerical computing in Python, powering everything from scientific research to machine learning and AI applications. Mastering NumPy is essential for anyone working with data, enabling faster computations, efficient data structures, and seamless integration with advanced analytical tools.

Hands-on NumPy for Numerical Analysis is a comprehensive guide that takes you from the fundamentals of NumPy to its advanced applications. Through hands-on examples and real-world scenarios, this book equips data scientists, analysts, and machine learning engineers with the practical skills needed to manipulate large datasets and optimize performance. Key topics include array operations, linear algebra, signal processing, and machine learning implementations, all covered with detailed explanations and step-by-step guidance.

Whether you're building your foundation in numerical computing or looking to enhance your data analysis workflows, this book will give you a competitive edge. Don't get left behind—harness the full power of NumPy to supercharge your data science and machine learning projects today!

What you will learn
● Master NumPy array operations for high-performance numerical computing.
● Optimize data analysis workflows with efficient NumPy techniques.
● Perform advanced linear algebra and matrix operations using NumPy.
● Conduct statistical and exploratory data analysis with NumPy tools.
● Build end-to-end data processing pipelines with NumPy.
● Leverage NumPy for predictive modeling and machine learning tasks.

Who is this book for?
This book is tailored for data scientists, analysts, engineers, and researchers looking to master NumPy for efficient numerical computing. A basic understanding of Python is recommended, but no prior expertise in numerical analysis is required.

1. Getting Started with NumPy
2. Understanding NumPy Array
3. Data Type (dtype) in NumPy Array
4. Indexing and Slicing in NumPy Array
5. NumPy Array Operations
6. NumPy Array I/O
7. Linear Algebra with NumPy
8. Advanced Numerical Computing
9. Exploratory Data Analysis
10. Performance Optimization
11. Implementing a Machine Learning Algorithm
Index

Rituraj Dixit brings over a decade of extensive experience in data engineering and analytics, specializing in enterprise-scale data solutions. As a Technical Manager at Cognizant Technology Solutions, Singapore, he leads complex data transformation initiatives, leveraging his expertise in ETL processes, data warehousing, big data architectures, and cloud platforms.

Throughout his career, he has successfully delivered innovative solutions for global organizations, driving business value through machine learning implementations, advanced analytics frameworks, and enterprise data platforms. His ability to seamlessly blend technical expertise with business acumen has enabled companies to maximize the value of data-driven insights.

A passionate advocate for technology education, Rituraj dedicates significant time to mentoring emerging data professionals, helping them navigate the complexities of the modern data ecosystem. He is also a member of several technology and professional organizations, including the Singapore Computer Society (SCS) and the Association for Computing Machinery (ACM).

With a unique combination of technical mastery and strategic vision, Rituraj consistently delivers solutions that align with business objectives while pushing the boundaries of data technology innovation.

ABOUT TECHNICAL REVIEWER

Abhinaba Banerjee has a background in electronics and communication engineering, holding both bachelor’s and master’s degrees. He also has an MSc in Big Data Analytics for Business from IESEG School of Management, Lille, France. Currently, he works as a Data Analyst, focusing on data analysis, dashboard preparation, and cleaning and preparing data from messy datasets. He has worked with Fintech and social-media startups in France and is currently involved with the Government of Andhra Pradesh. Additionally, he has published several research papers in Communication Engineering and Signal Processing.

He frequently shares blogs on Medium, creates projects, and posts on social media platforms such as Twitter and LinkedIn. Moreover, he has recently developed the habit of solving GitHub issues to learn and contribute to the community. 

Abhinaba Banerjee’s expertise ranges from Data Analytics using tools such as Python, SQL, Excel, and PowerBI to Data Science, where he uses libraries such as scikit-learn for Machine Learning and Hugging face for Natural Language

Processing. He utilizes GitHub for showcasing his projects, and is currently focused on building end-to-end MLOps projects.

During his leisure time, he enjoys listening to podcasts on history, technology, and horror.