Abhishek Kumar
SKU: 9788196815059
ISBN: 9788196815059
eISBN: 9788196815035
Rights: Worldwide
Author Name: Abhishek Kumar
Publishing Date: 08-Aug-2024
Dimension: 7.5*9.25 Inches
Binding: Paperback
Page Count: 362
Download code from GitHubKey Features
● Explore diverse techniques and algorithms for data analytics using Java.
● Learn through hands-on examples and practical applications in each chapter.
● Master essential tools and frameworks such as JFreeChart for data visualization and Deeplearning4j for deep learning.
Book Description
This book is a comprehensive guide to data analysis using Java. It starts with the fundamentals, covering the purpose of data analysis, different data types and structures, and how to pre-process datasets. It then introduces popular Java libraries like WEKA and Rapidminer for efficient data analysis.
The middle section of the book dives deeper into statistical techniques like descriptive analysis and random sampling, along with practical skills in working with relational databases (JDBC, SQL, MySQL) and NoSQL databases. It also explores various analysis methods like regression, classification, and clustering, along with applications in business intelligence and time series prediction.
The final part of the book gives a brief overview of big data analysis with Java frameworks like MapReduce, and introduces deep learning with the Deeplearning4J library. Whether you're new to data analysis or want to improve your Java skills, this book offers a step-by-step approach with real-world examples to help you master data analysis using Java.
What you will learn
● Understand foundational principles and types of data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics.
● Master techniques for preprocessing data, including cleaning and munging, to prepare it for analysis.
● Learn how to create various charts and plots including bar charts, histograms, and scatter plots for effective data visualization.
● Explore Java-based libraries such as WEKA and Deeplearning4j for implementing machine learning algorithms.
● Develop expertise in statistical techniques including hypothesis testing, regression (linear and polynomial), and probability distributions.
● Acquire practical skills in SQL querying and JDBC for relational databases.
● Explore applications in business intelligence and deep learning, including image recognition and natural language processing.
Who is this book for?
This book is ideal for IT professionals, software developers, and data scientists interested in using Java for data analytics. It is also suitable for students and researchers seeking practical insights into Java-based data analysis. Readers should have a basic understanding of Java programming and fundamental concepts in data analysis.
2. Datasets
3. Data Visualization
4. Java Machine Learning Libraries
5. Statistical Analysis
6. Relational Databases
7. Regression Analysis
8. Classification Analysis
9. Sentiment Analysis
10. Cluster Analysis
11. Working with NoSQL Databases
12. Recommender Systems
13. Applications of Data Analysis
14. Big Data Analysis with Java
15. Deep Learning with Java
Index
Abhishek Kumar has been a pivotal figure in the design and development of complex enterprise-grade software for over 12 years. His professional journey has seen him contributing his extensive systems programming expertise to leading technology companies including Adobe, Intel, ARM, Samsung, and NVIDIA. Currently, he serves as a Senior Computer Scientist, where he continues to excel in his field.
Abhishek is deeply passionate about teaching programming and machine learning. This passion is reflected in his authorship of the book Rust Crash Course and the creation of several successful courses covering C++, Rust, Lua, Data Structures and Algorithms, and Machine Learning. His dedication to advancing the field is further demonstrated by his possession of a US patent in Computer Vision and Deep Learning.
Abhishek holds a Bachelor of Technology in Electrical Engineering and a Master of Technology in Information and Communication Technology, both from the prestigious Indian Institute of Technology (IIT) Delhi. His academic and professional achievements highlight his commitment to excellence and innovation in the technology sector.
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ABOUT TECHNICAL REVIEWER
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Priya Bhatia embarked on her journey in the realm of artificial intelligence with a robust academic foundation, completing her master's in AI from the esteemed Indian Institute of Technology (IIT) Hyderabad. This rigorous program not only provided her with deep theoretical insights but also honed her practical skills in the field, laying a solid groundwork for her future endeavors. In recognition of her academic prowess, Priya was awarded the prestigious Reliance Foundation scholarship in 2020-2021 for academic excellence in AI and computer science. This accolade is a testament to her dedication and outstanding performance in these disciplines.
Further solidifying her academic standing, Priya Bhatia authored a research paper on AI in the healthcare domain, which was published by IEEE, demonstrating her capability to contribute meaningful advancements in technology. Transitioning from academia to the professional arena, she has amassed over three years of rich experience in the Data Science domain, working with renowned MNCs like Thales and the vibrant startup iNeuron.ai. In these roles, she has not only applied her academic learnings but also adapted to real-world challenges, delivering data- driven solutions and innovations in various projects.
Beyond her professional and academic achievements, she is passionate about mentoring and education. For the past three years, she has been a public speaker and mentor in Data Structures and Algorithms (DSA) and Data Science, guiding and inspiring a new generation of enthusiasts and professionals in these fields. She extends her educational outreach through her YouTube channel, Priya Bhatia. Here, she shares quality videos on a spectrum of topics related to DSA and Data Science, catering to a wide audience eager to learn and grow in these areas.
Her path in the world of AI and Data Science is marked by a seamless integration of academic excellence, professional acumen, and a commitment to fostering knowledge and skill development in others, making her a distinguished figure in this dynamic and impactful field.