Search Constraints
Number of results to display per page
Results for:
Bioinformatics and Data Analysis
Remove constraint Bioinformatics and Data Analysis
Search Results
-
e-book
Introductory Business Statistics with Interactive Spreadsheets – 1st Canadian Edition is an adaptation of Thomas K. Tiemann's book, Introductory Business Statistics. This new edition still contains the basic ideas behind statistics, such as populations, samples, the difference between data and information, and sampling distributions as well as information on descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics. New topics include the chi-square test and categorical variables, null and alternative hypotheses for the test of independence, simple linear regression model, least squares method, coefficient of determination, confidence interval for the average of the dependent variable, and prediction interval for a specific value of the dependent variable. This new edition also allows readers to learn the basic and most commonly applied statistical techniques in business in an interactive way — when using the web version — through interactive Excel spreadsheets. For each topic, a customized interactive template has been created within which selected values can be repeatedly changed to observe how the entire process, as well as the outcomes, are automatically adjusted. Also, in this adapted edition, the real-world examples throughout the text, and the information in general, have been revised to reflect Canadian content.
- Subjects:
- Management and Statistics and Research Methods
- Keywords:
- Business -- Decision making Textbooks Microsoft Excel (Computer file) Commercial statistics
- Resource Type:
- e-book
-
e-book
The book "Introductory Business Statistics" by Thomas K. Tiemann explores the basic ideas behind statistics, such as populations, samples, the difference between data and information, and most importantly sampling distributions. The author covers topics including descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics. Using real-world examples throughout the text, the author hopes to help students understand how statistics works, not just how to "get the right number."
- Subjects:
- Mathematics and Statistics and Management
- Keywords:
- Textbooks Commercial statistics Industrial management -- Statistical methods
- 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
This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science: Data structures and algorithms: A data structure is a collection that contains data elements organized in a way that supports particular operations. For example, a dictionary organizes key-value pairs in a way that provides fast mapping from keys to values, but mapping from values to keys is generally slower. An algorithm is a mechanical process for performing a computation. Designing efficient programs often involves the co-evolution of data structures and the algorithms that use them. For example, the first few chapters are about graphs, a data structure that is a good implementation of a graph---nested dictionaries---and several graph algorithms that use this data structure. Python programming: This book picks up where Think Python leaves off. I assume that you have read that book or have equivalent knowledge of Python. As always, I will try to emphasize fundmental ideas that apply to programming in many languages, but along the way you will learn some useful features that are specific to Python. Computational modeling: A model is a simplified description of a system that is useful for simulation or analysis. Computational models are designed to take advantage of cheap, fast computation. Philosophy of science: The models and results in this book raise a number of questions relevant to the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, holism and reductionism, and Bayesian epistemology. This book focuses on discrete models, which include graphs, cellular automata, and agent-based models. They are often characterized by structure, rules and transitions rather than by equations. They tend to be more abstract than continuous models; in some cases there is no direct correspondence between the model and a physical system. Complexity science is an interdisciplinary field---at the intersection of mathematics, computer science and physics---that focuses on these kinds of models. That's what this book is about.
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Computational complexity Textbooks Python (Computer program language)
- Resource Type:
- e-book
-
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
-
Others
Scikit Learn provide simple and efficient tools for predictive data analysis. Assessible to everybody, and reusable in various contexts. It built on NumPy, SciPy, and matplotlib. It is open sources, commercially usable under the BSD License.
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Python (Computer program language)
- Resource Type:
- Others
-
MOOC
Data science has critical applications across most industries, and is one of the most in-demand careers in computer science. Data scientists are the detectives of the big data era, responsible for unearthing valuable data insights through analysis of massive datasets. And just like a detective is responsible for finding clues, interpreting them, and ultimately arguing their case in court, the field of data science encompasses the entire data life cycle. That starts with capturing lots of raw data using data collection techniques, and then building and maintaining data pipelines and data warehouses that efficiently “clean” the data and make it accessible for analysis at scale. This data infrastructure allows data scientists to efficiently process datasets using data mining and data modeling skills, as well as analyze these outputs with sophisticated techniques like predictive analysis and qualitative analysis. Finally, these findings must be presented using data visualization and data reporting skills to help business decision makers. Depending on the size of the company, data scientists may be responsible for this entire data life cycle, or they might specialize in a particular portion of the life cycle as part of a larger data science team
- Subjects:
- Computing, Data Science and Artificial Intelligence
- Keywords:
- Big data Data mining Machine learning
- Resource Type:
- MOOC
-
Others
Cotton Incorporated’s CottonWorks™ program is your industry resource as a professional or emerging professional in the apparel and textile industry. Develop expertise for every stage of the product development and marketing process by diving into our comprehensive resource with data and research, market and trend analysis, timely webinars, and informative videos.
- Course related:
- ITC3022T Yarn Technology, ITC2004T Textile Studies II, ITC2007T Textile Studies III, and ITC2002T Textile Studies I
- Subjects:
- Textiles
- Keywords:
- Cotton manufacture Cotton textiles Cotton
- Resource Type:
- Others
-
MOOC
Meeting growing global energy demand, while mitigating climate change and environmental impacts, requires a large-scale transition to clean, sustainable energy systems. Students and professionals around the world must prepare for careers in this future energy landscape, gaining relevant skills and knowledge to expedite the transformation in industry, government and nongovernmental organizations, academia, and nonprofits. The building sector represents a large percentage of overall energy consumption, and contributes 40% of the carbon emissions driving climate change. Yet buildings also offer opportunities for substantial, economical energy efficiency gains. From retrofit projects to new construction, buildings require a context-specific design process that integrates efficiency strategies and technologies. In this course, you'll be introduced to a range of technologies and analysis techniques for designing comfortable, resource-efficient buildings. The primary focus of this course is the study of the thermal and luminous behavior of buildings. You'll examine the basic scientific principles underlying these phenomena, and use computer-aided design software and climate data to explore the role light and energy can play in shaping architecture. These efficiency design elements are critical to the larger challenge of producing energy for a growing population while reducing carbon emissions.
- Subjects:
- Environmental Engineering, Building Services Engineering, and Building and Real Estate
- Keywords:
- Buildings -- Energy conservation Sustainable architecture Sustainable buildings -- Design construction
- Resource Type:
- MOOC
-
Others
Create interactive maps, and discover patterns in geospatial data.
- Subjects:
- Computing, Data Science and Artificial Intelligence and Land Surveying and Geo-Informatics
- Keywords:
- Python (Computer program language) Geospatial data
- Resource Type:
- Others