Search Constraints
Number of results to display per page
Results for:
Bioinformatics and Data Analysis
Remove constraint Bioinformatics and Data Analysis
Year
2016
Remove constraint Year: 2016
« Previous |
1 - 10 of 13
|
Next »
Search Results
-
e-book
"A Brief Introduction to Engineering Computation with MATLAB is specifically designed for students with no programming experience. However, students are expected to be proficient in First Year Mathematics and Sciences and access to good reference books are highly recommended. Students are assumed to have a working knowledge of the Mac OS X or Microsoft Windows operating systems. The strategic goal of the course and book is to provide learners with an appreciation for the role computation plays in solving engineering problems. MATLAB specific skills that students are expected to be proficient at are: write scripts to solve engineering problems including interpolation, numerical integration and regression analysis, plot graphs to visualize, analyze and present numerical data, and publish reports."--BC Campus website.
- Subjects:
- Computing, Data Science and Artificial Intelligence and Mechanical Engineering
- Keywords:
- Systems engineering Textbooks
- Resource Type:
- e-book
-
e-book
This is a complete college textbook, including a detailed Table of Contents, seventeen Chapters (each with a set of relevant homework problems), a list of References, two Appendices, and a detailed Index. The book is intended to enable students to: Solve first-, second-, and higher-order, linear, time-invariant (LTI) ordinary differential equations (ODEs) with initial conditions and excitation, using both time-domain and Laplace-transform methods; Solve for the frequency response of an LTI system to periodic sinusoidal excitation and plot this response in standard form; Explain the role of the time constant in the response of a first-order LTI system, and the roles of natural frequency, damping ratio, and resonance in the response of a second-order LTI system; Derive and analyze mathematical models (ODEs) of low-order mechanical systems, both translational and rotational, that are composed of inertial elements, spring elements, and damping devices; Derive and analyze mathematical models (ODEs) of low-order electrical circuits composed of resistors, capacitors, inductors, and operational amplifiers; Derive (from ODEs) and manipulate Laplace transfer functions and block diagrams representing output-to-input relationships of discrete elements and of systems; Define and evaluate stability for an LTI system; Explain proportional, integral, and derivative types of feedback control for single-input, single-output (SISO), LTI systems; Sketch the locus of characteristic values, as a control parameter varies, for a feedback-controlled SISO, LTI system; Use MATLAB as a tool to study the time and frequency responses of LTI systems. The book's general organization is: Chapters 1-10 deal primarily with the ODEs and behaviors of first-order and second-order dynamic systems; Chapters 11 and 12 discuss the ODEs and behaviors of mechanical systems having two degrees of freedom, i.e., fourth-order systems; Chapters 13 and 14 introduce classical feedback control; Chapter 15 presents the basic features of proportional, integral, and derivative types of classical control; Chapters 16 and 17 discuss methods for analyzing the stability of classical control systems. The general minimum prerequisite for understanding this book is the intellectual maturity of a junior-level (third-year) college student in an accredited four-year engineering curriculum. A mathematical second-order system is represented in this book primarily by a single second-order ODE, not in the state-space form by a pair of coupled first-order ODEs. Similarly, a two-degrees-of-freedom (fourth-order) system is represented by two coupled second-order ODEs, not in the state-space form by four coupled first-order ODEs. The book does not use bond graph modeling, the general and powerful, but complicated, modern tool for analysis of complex, multidisciplinary dynamic systems. The homework problems at the ends of chapters are very important to the learning objectives, so the author attempted to compose problems of practical interest and to make the problem statements as clear, correct, and unambiguous as possible. A major focus of the book is computer calculation of system characteristics and responses and graphical display of results, with use of basic (not advanced) MATLAB commands and programs. The book includes many examples and homework problems relevant to aerospace engineering, among which are rolling dynamics of flight vehicles, spacecraft actuators, aerospace motion sensors, and aeroelasticity. There are also several examples and homework problems illustrating and validating theory by using measured data to identify first- and second-order system dynamic characteristics based on mathematical models (e.g., time constants and natural frequencies), and system basic properties (e.g., mass, stiffness, and damping). Applications of real and simulated experimental data appear in many homework problems. The book contains somewhat more material than can be covered during a single standard college semester, so an instructor who wishes to use this as a one-semester course textbook should not attempt to cover the entire book, but instead should cover only those parts that are most relevant to the course objectives.
- Keywords:
- Differential equations Engineering mathematics Differential equations Partial Textbooks
- Resource Type:
- e-book
-
e-book
Data structures and algorithms are among the most important inventions of the last 50 years, and they are fundamental tools software engineers need to know. But in my opinion, most of the books on these topics are too theoretical, too big, and too bottom-up: Too theoretical: Mathematical analysis of algorithms is based on simplifying assumptions that limit its usefulness in practice. Many presentations of this topic gloss over the simplifications and focus on the math. In this book I present the most practical subset of this material and eliminate the rest. Too big: Most books on these topics are at least 500 pages, and some are more than 1000. By focusing on the topics I think are most useful for software engineers, I kept this book under 250 pages. Too bottom-up: Many data structures books focus on how data structures work (the implementations), with less about how to use them (the interfaces). In this book, I go “top down”, starting with the interfaces. Readers learn to use the structures in the Java Collections Framework before getting into the details of how they work. Finally, many present this material out of context and without motivation: it’s just one damn data structure after another! I try to alleviate the boredom by organizing the topics around an application—web search—that uses data structures extensively, and is an interesting and important topic in its own right. This application also motivates some topics that are not usually covered in an introductory data structures class, including persistent data structures, with Redis, and streaming algorithms. This book also presents basic aspects of software engineering practice, including version control and unit testing. Each chapter ends with an exercise that allows readers to apply what they have learned. Each exercise includes automated tests that check the solution. And for most exercises, I present my solution at the beginning of the next chapter. This book is intended for college students in computer science and related fields, as well as professional software engineers, people training in software engineering, and people preparing for technical interviews. I assume that the reader knows Java at an intermediate level, but I explain some Java features along the way, and provide pointers to supplementary material. People who have read Think Java or Head First Java are prepared for this book.
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Java (Computer program language) Textbooks Data structures (Computer science)
- Resource Type:
- e-book
-
e-book
As currently taught in the United States, introductory courses in analytical chemistry emphasize quantitative (and sometimes qualitative) methods of analysis along with a heavy dose of equilibrium chemistry. Analytical chemistry, however, is much more than a collection of analytical methods and an understanding of equilibrium chemistry; it is an approach to solving chemical problems. Although equilibrium chemistry and analytical methods are important, their coverage should not come at the expense of other equally important topics. The introductory course in analytical chemistry is the ideal place in the undergraduate chemistry curriculum for exploring topics such as experimental design, sampling, calibration strategies, standardization, optimization, statistics, and the validation of experimental results. Analytical methods come and go, but best practices for designing and validating analytical methods are universal. Because chemistry is an experimental science it is essential that all chemistry students understand the importance of making good measurements. My goal in preparing this textbook is to find a more appropriate balance between theory and practice, between “classical” and “modern” analytical methods, between analyzing samples and collecting samples and preparing them for analysis, and between analytical methods and data analysis. There is more material here than anyone can cover in one semester; it is my hope that the diversity of topics will meet the needs of different instructors, while, perhaps, suggesting some new topics to cover.
- Subjects:
- Chemistry
- Keywords:
- Chemistry Analytic -- Quantitative Textbooks
- Resource Type:
- e-book
-
e-book
I never seemed to find the perfect data-oriented Python book for my course, so I set out to write just such a book. Luckily at a faculty meeting three weeks before I was about to start my new book from scratch over the holiday break, Dr. Atul Prakash showed me the Think Python book which he had used to teach his Python course that semester. It is a well-written Computer Science text with a focus on short, direct explanations and ease of learning.The overall book structure has been changed to get to doing data analysis problems as quickly as possible and have a series of running examples and exercises about data analysis from the very beginning. Chapters 2–10 are similar to the Think Python book, but there have been major changes. Number-oriented examples and exercises have been replaced with data- oriented exercises. Topics are presented in the order needed to build increasingly sophisticated data analysis solutions. Some topics like try and except are pulled forward and presented as part of the chapter on conditionals. Functions are given very light treatment until they are needed to handle program complexity rather than introduced as an early lesson in abstraction. Nearly all user-defined functions have been removed from the example code and exercises outside of Chapter 4. The word “recursion”1 does not appear in the book at all. In chapters 1 and 11–16, all of the material is brand new, focusing on real-world uses and simple examples of Python for data analysis including regular expressions for searching and parsing, automating tasks on your computer, retrieving data across the network, scraping web pages for data, object-oriented programming, using web services, parsing XML and JSON data, creating and using databases using Structured Query Language, and visualizing data. The ultimate goal of all of these changes is a shift from a Computer Science to an Informatics focus is to only include topics into a first technology class that can be useful even if one chooses not to become a professional programmer.
-
e-book
Recognizing that a course in economics may seem daunting to some students, we have tried to make the writing clear and engaging. Clarity comes in part from the intuitive presentation style, but we have also integrated a number of pedagogical features that we believe make learning economic concepts and principles easier and more fun. These features are very student-focused. The chapters themselves are written using a “modular” format. In particular, chapters generally consist of three main content sections that break down a particular topic into manageable parts. Each content section contains not only an exposition of the material at hand but also learning objectives, summaries, examples, and problems. Each chapter is introduced with a story to motivate the material and each chapter ends with a wrap-up and additional problems. Our goal is to encourage active learning by including many examples and many problems of different types. A tour of the features available for each chapter may give a better sense of what we mean: Start Up—Chapter introductions set the stage for each chapter with an example that we hope will motivate readers to study the material that follows. These essays, on topics such as the value of a college degree in the labor market or how policy makers reacted to a particular economic recession, lend themselves to the type of analysis explained in the chapter. We often refer to these examples later in the text to demonstrate the link between theory and reality. Learning Objectives—These succinct statements are guides to the content of each section. Instructors can use them as a snapshot of the important points of the section. After completing the section, students can return to the learning objectives to check if they have mastered the material.Heads Up!—These notes throughout the text warn of common errors and explain how to avoid making them. After our combined teaching experience of more than fifty years, we have seen the same mistakes made by many students. This feature provides additional clarification and shows students how to navigate possibly treacherous waters. Key Takeaways—These statements review the main points covered in each content section. Key Terms—Defined within the text, students can review them in context, a process that enhances learning. Try It! questions—These problems, which appear at the end of each content section and which are answered completely in the text, give students the opportunity to be active learners. They are designed to give students a clear signal as to whether they understand the material before they go on to the next topic. Cases in Point—These essays included at the end of each content section illustrate the influence of economic forces on real issues and real people. Unlike other texts that use boxed features to present interesting new material or newspaper articles, we have written each case ourselves to integrate them more clearly with the rest of the text. Summary—In a few paragraphs, the information presented in the chapter is pulled together in a way that allows for a quick review of the material.End-of-chapter concept and numerical problems—These are bountiful and are intended to check understanding, to promote discussion of the issues raised in the chapter, and to engage students in critical thinking about the material. Included are not only general review questions to test basic understanding but also examples drawn from the news and from results of economics research. Some have students working with real-world data.
- Subjects:
- Economics
- Keywords:
- Macroeconomics Textbooks
- Resource Type:
- e-book
-
Courseware
Introduction to seismic theory, measurements and processing of seismic data to final focussed image for geological and/or physical interpretation.This course deals with the most important aspects of reflection seismics. Theory of seismic waves, aspects of data acquisition (seismic sources, receivers and recorders), and of data processing (CMP processing, velocity analysis, stacking, migration) will be dealt with. The course will be supplemented by a practical of 6 afternoons where the students will see the most important data-processing steps via exercises (in Matlab).
- Subjects:
- Land Surveying and Geo-Informatics and Disaster Control and Management
- Keywords:
- Seismic prospecting Seismometry Earthquakes Seismic reflection method
- Resource Type:
- Courseware
-
Courseware
The course treats the following topics: - Relevant physical oceanography - Elements of marine geology (seafloor topography, acoustical properties of sediments and rocks) - Underwater sound propagation (ray acoustics, ocean noise) - Interaction of sound with the seafloor (reflection, scattering) - Principles of sonar (beamforming) - Underwater acoustic mapping systems (single beam echo sounding, multi-beam echo sounding, sidescan sonar) - Data analysis (refraction corrections, digital terrain modelling) - Applications (hydrographic survey planning and navigation, coastal engineering) - Current and future developments.
- Subjects:
- Land Surveying and Geo-Informatics
- Keywords:
- Underwater acoustics -- Remote sensing Ocean bottom Ocean bottom -- Remote sensing
- Resource Type:
- Courseware
-
Courseware
This course discusses the requirement, interpretation, methods and design of hydrological measurements. Following topics are covered: Accuracy requirements of measurements and error propagation: Related to a problem the required accuracy of measurements and the consequences for accuracy in the final result are discussed. Different types of errors are handled. Propagation of errors; for dependent and independent measurements, from mathematical relations and regression is demonstrated. Recapitulated is the theory of regression and correlation. Interpretation of measurements, data completion: By standard statistical methods screening of measured data is performed; double mass analysis, residual mass, simple rainfall-runoff modelling. Detection of trends; split record tests, Spearman rank tests. Methods to fill data gaps and do filtering on data series for noise reduction. Methods of hydrological measurements and measuring equipment: To determine quantitatively the most important elements in the hydrological cycle an overview is presented of most common hydrological measurements, measuring equipment and indirect determination methods i.e. for precipitation, evaporation, transpiration, river discharge and groundwater tables. Use, purpose and measurement techniques for tracers in hydrology is discussed. Advantages and disadvantages and specific condition/application of methods are discussed. Equipment is demonstrated and discussed. Areal distributed observation: Areal interpolation techniques of point observations; inverse distance, Thiessen, contouring, Kriging. Comparison of interpolation techniques and estimation of errors. Correlation analysis of areal distributed observation of rainfall. Design of measuring networks: Based on correlation characteristics from point measurements (e.g. rainfall stations) and accuracy requirements the design of a network of stations is demonstrated.
- Subjects:
- Hydraulic Engineering
- Keywords:
- Hydrology -- Measurement Hydrology
- Resource Type:
- Courseware
-
Courseware
This course is for all of those struggling with data analysis. You will learn: - Overcome data analysis challenges in your work and research - Increase your productivity and make better business decisions - Enhance your data analysis skills using spreadsheets - Learn about advanced spreadsheet possibilities like array formulas and pivottables - Learn about Excel 2013 features like PowerPivot & PowerMap - Learn to organize and test your spreadsheets