Data Structures and Algorithms with Python (Undergraduate Topics in Computer Science)

"This new edition is a concise introduction to the basic methods of computational physics. Readers will discover the benefits of numerical methods for solving complex mathematical problems and for the direct simulation of physical processes. The book is divided into two main parts: Deterministic methods and stochastic methods in computational physics. Based on concrete problems, the first part discusses numerical differentiation and integration, as well as the treatment of ordinary differential equations. This is extended by a brief introduction to the numerics of partial differential equations. The second part deals with the generation of random numbers, summarizes the basics of stochastics, and subsequently introduces Monte-Carlo (MC) methods. Specific emphasis is on MARKOV chain MC algorithms. The final two chapters discuss data analysis and stochastic optimization. All this is again motivated and augmented by applications from physics. In addition, the book offers a number of appendices to provide the reader with information on topics not discussed in the main text. Numerous problems with worked-out solutions, chapter introductions and summaries, together with a clear and application-oriented style support the reader. Ready to use C++ codes are provided online."--Publisher's website

Data modeling is one of the most critical phases in the database application development process, but also the phase most likely to fail. A master data modeler must come into any organization, understand its data requirements, and skillfully model the data for applications that most effectively serve organizational needs.

Mastering Data Modeling is a complete guide to becoming a successful data modeler. Featuring a requirements-driven approach, this book clearly explains fundamental concepts, introduces a user-oriented data modeling notation, and describes a rigorous, step-by-step process for collecting, modeling, and documenting the kinds of data that users need.

Assuming no prior knowledge, Mastering Data Modeling sets forth several fundamental problems of data modeling, such as reconciling the software developer's demand for rigor with the users' equally valid need to speak their own (sometimes vague) natural language. In addition, it describes the good habits that help you respond to these fundamental problems. With these good habits in mind, the book describes the Logical Data Structure (LDS) notation and the process of controlled evolution by which you can create low-cost, user-approved data models that resist premature obsolescence. Also included is an encyclopedic analysis of all data shapes that you will encounter. Most notably, the book describes The Flow, a loosely scripted process by which you and the users gradually but continuously improve an LDS until it faithfully represents the information needs. Essential implementation and technology issues are also covered.

You will learn about such vital topics as:

• The fundamental problems of data modeling
• The good habits that help a data modeler be effective and economical
• LDS notation, which encourages these good habits
• How to read an LDS aloud—in declarative English sentences
• How to write a well-formed (syntactically correct) LDS
• How to get users to name the parts of an LDS with words from their own business vocabulary
• How to visualize data for an LDS
• A catalog of LDS shapes that recur throughout all data models
• The Flow—the template for your conversations with users
• How to document an LDS for users, data modelers, and technologists
• How to map an LDS to a relational schema
• How LDS differs from other notations and why

Story interludes appear throughout the book, illustrating real-world successes of the LDS notation and controlled evolution process. Numerous exercises help you master critical skills. In addition, two detailed, annotated sample conversations with users show you the process of controlled evolution in action.

Booknews

Carlis (computer science, U. of Minnesota) and Maguire a program manager for Microsoft, explain to information systems analysts and database developers how to become a successful data modeler. Using their own Logical Data Structure for the data modeling notation, they describe in detail the process for collecting, modeling, and documenting data structures and flow. They also analyze all data shapes and provide several recipes for applying them. They provide no bibliographic references. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Data modeling is one of the most critical phases in the database application development process, but also the phase most likely to fail. A master data modeler must come into any organization, understand its data requirements, and skillfully model the data for applications that most effectively serve organizational needs. Mastering Data Modeling is a complete guide to becoming a successful data modeler. Featuring a requirements-driven approach, this book clearly explains fundamental concepts, introduces a user-oriented data modeling notation, and describes a rigorous, step-by-step process for collecting, modeling, and documenting the kinds of data that users need. Assuming no prior knowledge, Mastering Data Modeling sets forth several fundamental problems of data modeling, such as reconciling the software developer's demand for rigor with the users'equally valid need to speak their own (sometimes vague) natural language. In addition, it describes the good habits that help you respond to these fundamental problems. With these good habits in mind, the book describes the Logical Data Structure (LDS) notation and the process of controlled evolution by which you can create low-cost, user-approved data models that resist premature obsolescence. Also included is an encyclopedic analysis of all data shapes that you will encounter. Most notably, the book describes The Flow, a loosely scripted process by which you and the users gradually but continuously improve an LDS until it faithfully represents the information needs. Essential implementation and technology issues are also covered. You will learn about such vital topics as: The fundamental problems of data modeling The good habits that help a data modeler be effective and economical LDS notation, which encourages these good habits How to read an LDS aloud--in declarative English sentences How to write a well-formed (syntactically correct) LDS How to get users to name the parts of an LDS with words from their own business vocabulary How to visualize data for an LDS A catalog of LDS shapes that recur throughout all data models The Flow--the template for your conversations with users How to document an LDS for users, data modelers, and technologists How to map an LDS to a relational schema How LDS differs from other notations and why'Story interludes'appear throughout the book, illustrating real-world successes of the LDS notation and controlled evolution process. Numerous exercises help you master critical skills. In addition, two detailed, annotated sample conversations with users show you the process of controlled evolution in action. This Clearly Structured And Easy To Read Textbook Explains The Concepts And Techniques Required To Write Programs That Can Handle Large Amounts Of Data Efficiently. Project-oriented And Classroom-tested, The Book Presents A Number Of Important Algorithms Supported By Motivating Examples That Bring Meaning To The Problems Faced By Computer Programmers. The Idea Of Computational Complexity Is Also Introduced, Demonstrating What Can And Cannot Be Computed Efficiently So That The Programmer Can Make Informed Judgements About The Algorithms They Use. The Text Assumes Some Basic Experience In Computer Programming And Familiarity In An Object-oriented Language, But Not Necessarily With Python. Topics And Features: Includes Both Introductory And Advanced Data Structures And Algorithms Topics, With Suggested Chapter Sequences For Those Respective Courses Provided In The Preface Provides Learning Goals, Review Questions And Programming Exercises In Each Chapter, As Well As Numerous Illustrative Examples Offers Downloadable Programs And Supplementary Files At An Associated Website, With Instructor Materials Available From The Author Presents A Primer On Python For Those Coming From A Different Language Background Reviews The Use Of Hashing In Sets And Maps, Along With An Examination Of Binary Search Trees And Tree Traversals, And Material On Depth First Search Of Graphs Discusses Topics Suitable For An Advanced Course, Such As Membership Structures, Heaps, Balanced Binary Search Trees, B-trees And Heuristic Search Students Of Computer Science Will Find This Clear And Concise Textbook To Be Invaluable For Undergraduate Courses On Data Structures And Algorithms, At Both Introductory And Advanced Levels. The Book Is Also Suitable As A Refresher Guide For Computer Programmers Starting New Jobs Working With Python. Python Programming 101 -- Computational Complexity -- Recursion -- Sequences -- Sets And Maps -- Trees -- Graphs -- Membership Structures -- Heaps -- Balanced Binary Search Trees -- B-trees -- Heuristic Search -- Appendix A: Integer Operators -- Appendix B: Float Operators -- Appendix C: String Operators And Methods -- Appendix D: List Operators And Methods -- Appendix E: Dictionary Operators And Methods -- Appendix F: Turtle Methods -- Appendix G: Turtlescreen Methods -- Appendix H: Complete Programs. By Kent D. Lee, Steve Hubbard. Data modeling is one of the most critical phases in the database application development process, but also the phase most likely to fail. A master data modeler must come into any organization, understand its data requirements, and skillfully model the data for applications that most effectively serve organizational needs. Mastering Data Modeling is a complete guide to becoming a successful data modeler. Featuring a requirements-driven approach, this book clearly explains fundamental concepts, introduces a user-oriented data modeling notation, and describes a rigorous, step-by-step process for collecting, modeling, and documenting the kinds of data that users need. Assuming no prior knowledge, Mastering Data Modeling sets forth several fundamental problems of data modeling, such as reconciling the software developer's demand for rigor with the users' equally valid need to speak their own (sometimes vague) natural language. In addition, it describes the good habits that help you respond to these fundamental problems. With these good habits in mind, the book describes the Logical Data Structure (LDS) notation and the process of controlled evolution by which you can create low-cost, user-approved data models that resist premature obsolescence. Also included is an encyclopedic analysis of all data shapes that you will encounter. Most notably, the book describes The Flow, a loosely scripted process by which you and the users gradually but continuously improve an LDS until it faithfully represents the information needs. Essential implementation and technology issues are also covered. With the development of ever more powerful computers a new branch of physics and engineering evolved over the last few decades: Computer Simulation or Computational Physics. It serves two main purposes: - Solution of complex mathematical problems such as, differential equations, minimization/optimization, or high-dimensional sums/integrals. - Direct simulation of physical processes, as for instance, molecular dynamics or Monte-Carlo simulation of physical/chemical/technical processes. Consequently, the book is divided into two main parts: Deterministic methods and stochastic methods. Based on concrete problems, the first part discusses numerical differentiation and integration, and the treatment of ordinary differential equations. This is augmented by notes on the numerics of partial differential equations. The second part discusses the generation of random numbers, summarizes the basics of stochastics which is then followed by the introduction of various Monte-Carlo (MC) methods. Specific emphasis is on MARKOV chain MC algorithms. All this is again augmented by numerous applications from physics. The final two chapters on Data Analysis and Stochastic Optimization share the two main topics as a common denominator. The book offers a number of appendices to provide the reader with more detailed information on various topics discussed in the main part. Nevertheless, the reader should be familiar with the most important concepts of statistics and probability theory albeit two appendices have been dedicated to provide a rudimentary discussion. Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz’s popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It’s an ideal way to begin, whether you’re new to programming or a professional developer versed in other languages.Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3— the latest releases in the 3.X and 2.X lines—plus all other releases in common use today. You’ll also learn some advanced language features that recently have become more common in Python code.Explore Python’s major built-in object types such as numbers, lists, and dictionariesCreate and process objects with Python statements, and learn Python’s general syntax modelUse functions to avoid code redundancy and package code for reuseOrganize statements, functions, and other tools into larger components with modulesDive into classes: Python’s object-oriented programming tool for structuring codeWrite large programs with Python’s exception-handling model and development toolsLearn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz's popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It's an ideal way to begin, whether you're new to programming or a professional developer versed in other languages. Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow, self-paced tutorial gets you started with both Python 2.7 and 3.3-- the latest releases in the 3.X and 2.X lines--plus all other releases in common use today. You'll also learn some advanced language features that recently have become more common in Python code. Explore Python's major built-in object types such as numbers, lists, and dictionaries Create and process objects with Python statements, and learn Python's general syntax model Use functions to avoid code redundancy and package code for reuse Organize statements, functions, and other tools into larger components with modules Dive into classes: Python's object-oriented programming tool for structuring code Write large programs with Python's exception-handling model and development tools Learn advanced Python tools, including decorators, descriptors, metaclasses, and Unicode processing Annotation This book introduces Logical Data Structures (LDS), a powerful new approach to database design that can dramatically improve the cost-effectiveness and business value of any enterprise database system or database-driven application. The authors introduce a new notation, new diagramming approach, and new user-centered, high-ROI techniques for modeling even the most complex, high-volume database applications. This book starts from first principles, asking and answering crucial questions like: "To best serve users, how should the process of data modeling work? To create good, economical software systems, what kind of information should be on a data model? To become an effective data modeler, what skills should you master before talking with users?" Next, it teaches data modeling using LDS, designed to encourage a user-centered, requirements approach that leads directly to more effective applications. The authors walk you through the entire process of creating and enhancing a data model. For all database administrators, analysts, designers, and architects, and for all IT managers responsible for enterprise database system management or deployment A Python Q&a Session -- How Python Runs Programs -- How You Run Programs -- Introducing Python Object Types -- Numeric Types -- The Dynamic Typing Interlude -- String Fundementals -- Lists And Dictionaries -- Tuples, Files, And Everything Else -- Introducing Python Statements -- Assignments, Expressions, And Prints -- If Tests And Syntax Rules -- While And For Loops -- Iterations And Comprehensions -- The Documentation Interlude -- Function Basics -- Scopes -- Arguments -- Advanced Function Topics -- Comprehensions And Generations -- The Benchmarking Interlude -- Modules : The Big Picture -- Module Coding Basics -- Module Packages -- Advanced Module Topics -- Oop : The Big Picture -- Class Coding Basics -- A More Realistic Example -- Class Coding Detials -- Operator Overloading -- Designing With Classes -- Advanced Class Topics -- Exception Basics -- Exception Coding Detials -- Exception Objects -- Designing With Exceptions -- Unicode And Byt Strings -- Managed Attributes -- Decorators -- Metaclasses -- All Good Things. Mark Lutz. Includes Index. Describes the features of the Python 2.5 programming language, covering such topics as types and operations, statements and syntax, functions, modules, classes and OOP, and exceptions and tools.