Added A&DS for Massive Datasets

README.md

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 | ★★★ | [Algorithms](https://www.amazon.com/Algorithms-4th-Robert-Sedgewick/dp/032157351X), by Robert Sedgewick and Kevin Wayne | This book is neatly categorized, coupled with elaborate explanations and fantastic illustrations. It is used in some IOI training camps as a textbook. |
 |     | [Algorithms and Data Structures in Action](https://www.manning.com/books/algorithms-and-data-structures-in-action), by Marcello La Rocca | This book provides a different approach to algorithms, balancing theory with a more practical angle, with a section per-chapter focusing on how to apply algorithms to real-world use cases that can be found in your daily work, or in competitive programming; it also presents a blend of classic, advanced, and new algorithms. |
 | ★★★ | [Algorithms](https://jeffe.cs.illinois.edu/teaching/algorithms/), by Jeff Erickson | A free electronic version of a self-published textbook licensed under CC by 4.0. This is a well written book from lecture notes of theoretical computer science courses at the University of Illinois. Covers the main paradigms of backtracking, dynamic programming, greedy, and particularly graphs in depth. |
+| ★★★ | [Algorithms and Data Structures for Massive Datasets](https://www.manning.com/books/algorithms-and-data-structures-for-massive-datasets), by Dzejla Medjedovic, Emin Tahirovic, and Ines Dedovic | Algorithms and Data Structures for Massive Datasets teaches you to take advantage of data processing and analytics techniques specifically designed for large distributed datasets. |
+| ★★★ | [Graph Algorithms for Data Science](https://www.manning.com/books/graph-algorithms-for-data-science), by Tomaz Bratanic | This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. |
 
 ### Books for Mathematics
 
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 | ★★☆ | [Introduction to Probability](http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/book.html), by Charles M. Grinstead, J. Laurie Snell | This is a well-written introductory probabilities book. ... It's free for [download (pdf)](http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/amsbook.mac.pdf) (released under GNU Free Documentation License). |
 | ★★☆ | [How to Solve It: A New Aspect of Mathematical Method](https://www.amazon.com/How-Solve-It-Mathematical-Princeton/dp/069111966X), by G. Polya | An old-time classic. In this book, the author provides a systematic way to solve problems creatively. |
 | ★★☆ | [Intermediate Counting & Probability](https://artofproblemsolving.com/store/item/intermediate-counting), by David Patrick | Topics in counting and probability byformer USA Mathematical Olympiad winner David Patrick , topics  include inclusion-exclusion, 1-1 correspondences, the Pigeonhole Principle, constructive expectation, Fibonacci and Catalan numbers, recursion, conditional probability, generating functions, graph theory, and much more.. |
+| ★★☆ | [Math for Programmers](https://www.manning.com/books/math-for-programmers), by Paul Orland | Math for Programmers teaches the math you need to achieve a career in data science, machine learning, computer graphics, and cryptography, concentrating on what you need to know as a developer. |
 
 
 ## Sites for Practice