These are my own notes for the class which were trans-coded to book form. Introduction to computer science using the Python programming language.
It covers the basics of computer programming in the first part while later chapters cover basic algorithms and data structures.
This is a hands-on introduction to the Python programming language, written for people who have no experience with programming whatsoever.
After all, everybody has to start somewhere. This book is NOT introductory. The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied. This book is designed to introduce students to programming and computational thinking through the lens of exploring data. You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet.
This is a simple book to learn the Python programming language, it is for the programmers who are new to Python. This book describes Python, an open-source general-purpose interpreted programming language available for a broad range of operating systems.
This book describes primarily version 2, but does at times reference changes in version 3. The aim of this Wikibook is to be the place where anyone can share his or her knowledge and tricks on R. It is supposed to be organized by task but not by discipline. We try to make a cross-disciplinary book, i.
This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. My intent is to present a relatively brief, non-jargony overview of how practicing epidemiologists can apply some of the extremely powerful spatial analytic tools that are easily available to them.
An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. This hands-on guide takes you through Python a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. Updated to Python 3. This is an introduction to the basic concepts of linear algebra, along with an introduction to the techniques of formal mathematics.
It has numerous worked examples, exercises and complete proofs, ideal for independent study. This text gives a brisk and engaging introduction to the mathematics behind the recently established field of Applied Topology. This text has been written in clear and accurate language that students can read and comprehend. The author has minimized the number of explicitly state theorems and definitions, in favor of dealing with concepts in a more conversational manner.
This book is designed for an introductory probability course at the university level for sophomores, juniors, and seniors in mathematics, physical and social sciences, engineering, and computer science. This book gives a self- contained treatment of linear algebra with many of its most important applications. It is very unusual if not unique in being an elementary book which does not neglect arbitrary fields of scalars and the proofs of the theorems.
The probability and statistics cookbook is a succinct representation of various topics in probability theory and statistics. It provides a comprehensive mathematical reference reduced to its essence, rather than aiming for elaborate explanations. Get started with O'Reilly's Graph Databases and discover how graph databases can help you manage and query highly connected data.
A chapter summary has been provided at the end of each chapter to facilitate a quick review of the content. Includes MCQs About multiple choice questions were included, located at the end of each chapter. Includes summary A chapter summary has been provided at the end of each chapter to facilitate a quick review of the content. However, I am not sure if that creates a problem in understanding the methodology.
This is a comprehensive enough text, considering that it is not easy to create a comprehensive statistics textbook. It is suitable for an introductory statistics course for non-math majors. It contains twenty-one chapters, covering the wide range It contains twenty-one chapters, covering the wide range of intro stats topics and some more , plus the case studies and the glossary.
The book contains fairly recent data presented in the form of exercises, examples and applications. The topics are up-to-date, and appropriate technology is used for examples, applications, and case studies. The language is simple and clear, which is a good thing, since students are usually scared of this class, and instructors are looking for something to put them at ease.
I would, however, try to make it a little more interesting, exciting, or may be even funny. Consistency is good, the book has a great structure. I like how each chapter has prerequisites and learner outcomes, this gives students a good idea of what to expect.
Material in this book is covered in good detail. The text can be easily divided into sub-sections, some of which can be omitted if needed. The chapter on regression is covered towards the end chapter 14 , but part of it can be covered sooner in the course. The book contains well organized chapters that makes reading through easy and understandable.
The order of chapters and sections is clear and logical. The online version has many functions and is easy to navigate. This book also comes with a PDF version. There is no distortion of images or charts. The text is clean and clear, the examples provided contain appropriate format of data presentation.
The text uses simple and clear language, which is helpful for non-native speakers. I would include more culturally-relevant examples and case studies. Overall, good text. In all, this book is a good learning experience. It contains tools and techniques that free and easy to use and also easy to modify for both, students and instructors. I very much appreciate this opportunity to use this textbook at no cost for our students. This is a reasonably thorough first-semester statistics book for most classes.
It would have worked well for the general statistics courses I have taught in the past but is not as suitable for specialized introductory statistics courses for It would have worked well for the general statistics courses I have taught in the past but is not as suitable for specialized introductory statistics courses for engineers or business applications. That is OK, they have separate texts for that! The only sections that feel somewhat light in terms of content are the confidence intervals and ANOVA sections.
Given that these topics are often sort of crammed in at the end of many introductory classes, that might not be problematic for many instructors. It should also be pointed out that while there are a couple of chapters on probability, this book spends presents most formulas as "black boxes" rather than worry about the derivation or origin of the formulas.
The probability sections do not include any significant combinatorics work, which is sometimes included at this level. I did not find any errors in the formulas presented but I did not work many end-of-chapter problems to gauge the accuracy of their answers. There isn't much changing in the introductory stats world, so I have no concerns about the book becoming outdated rapidly.
The examples and problems still feel relevant and reasonably modern. As students increasingly buy TIs or Inspires, these sections of the book may lose relevance faster than other parts. The book gives a list of key terms and their definitions at the end of each chapter which is a nice feature.
It also has a formula review at the end of each chapter. I can imagine that these are heavily used by students when studying! Formulas are easy to find and read and are well defined. There are a few areas that I might have found frustrating as a student. For example, the explanation for the difference in formulas for a population vs sample standard deviation is quite weak. Again, this is a book that focuses on sort of a "black-box" approach but you may have to supplement such sections for some students.
This low rating should not be taken as an indicator of an issue with this book but would be true of virtually any statistics book.
Different books still use different variable symbols even for basic calculated statistics. However, I think it would be possible to skip some chapters or use the chapters in a different order without any loss of functionality. This book uses a very standard order for the material.
The chapter on regressions comes later than it does in some texts but it doesn't really matter since that chapter never seems to fit smoothly anywhere. There are numerous end of chapter problems, some with answers, available in this book.
I'm vacillating on whether these problems would be more useful if they were distributed after each relevant section or are better clumped at the end of the whole chapter. That might be a matter of individual preference. I found no errors. However, there were several sections where the punctuation seemed non-ideal. This did not affect the over-all useability of the book though. I'm not sure how well this book would work internationally as many of the examples contain domestic American references.
However, I did not see anything offensive or biased in the book. As the title implies, this is a brief introduction textbook. It covers the fundamental of the introductory statistics, however not a comprehensive text on the subject. A teacher can use this book as the sole text of an introductory statistics A teacher can use this book as the sole text of an introductory statistics. The prose format of definitions and theorems make theoretical concepts accessible to non-math major students.
The textbook covers all chapters required in this level course. It is accurate; the subject matter in the examples to be up to date, is timeless and wouldn't need to be revised in future editions; there is no error except a few typographical errors. There are no logic errors or incorrect explanations. This text will remain up to date for a long time since it has timeless examples and exercises, it wouldn't be outdated.
The information is presented clearly with a simple way and the exercises are beneficial to follow the information. The material is presented in a clear, concise manner. The text is easy readable for the first time statistics student. The structure of the text is very consistent. Topics are presented with examples, followed by exercises. Problem sets are appropriate for the level of learner. When the earlier matters need to be referenced, it is easy to find; no trouble reading the book and finding results, it has a consistent scheme.
This book is set very well in sections. There is no logic errors and incorrect explanations, a few typographical errors is just to be ignored. This book is pretty comprehensive for being a brief introductory book. This book covers all necessary content areas for an introduction to Statistics course for non-math majors. The text book provides an effective index, plenty of exercises, The text book provides an effective index, plenty of exercises, review questions, and practice tests.
It provides references and case studies. The glossary and index section is very helpful for students and can be used as a great resource.
Content appears to be accurate throughout. Being an introductory book, the book is unbiased and straight to the point. The terminology is standard. The content in textbook is up to date. It will be very easy to update it or make changes at any point in time because of the well-structured contents in the textbook. The author does a great job of explaining nearly every new term or concept.
The book is easy to follow, clear and concise. The graphics are good to follow. The language in the book is easily understandable.
I found most instructions in the book to be very detailed and clear for students to follow. Overall consistency is good. It is consistent in terms of terminology and framework. The writing is straightforward and standardized throughout the text and it makes reading easier. The authors do a great job of partitioning the text and labeling sections with appropriate headings.
The table of contents is well organized and easily divisible into reading sections and it can be assigned at different points within the course. Overall, the topics are arranged in an order that follows natural progression in a statistics course with some exception. They are addressed logically and given adequate coverage.
The text is not culturally insensitive or offensive in any way most of time. Some examples might need to consider citing the sources or use differently to reflect current inclusive teaching strategies. Overall, it's well-written and good recourse to be an introduction to statistical methods. Some materials may not need to be covered in an one-semester course. Various examples and quizzes can be a great recourse for instructor.
The text includes the introductory statistics topics covered in a college-level semester course. An effective index and glossary are included, with functional hyperlinks. The content of this text is accurate and error-free, based on a random sampling of various pages throughout the text. Several examples included information without formal citation, leading the reader to potential bias and discrimination.
These examples should be corrected to reflect current values of inclusive teaching. The text contains relevant information that is current and will not become outdated in the near future.
The statistical formulas and calculations have been used for centuries. The examples are direct applications of the formulas and accurately assess the conceptual knowledge of the reader. The text is very clear and direct with the language used. Graphs, tables, and visual displays are clearly labeled. The terminology and framework of the text is consistent. The hyperlinks are working effectively, and the glossary is valuable. Each chapter contains modules that begin with prerequisite information and upcoming learning objectives for mastery.
The modules are clearly defined and can be used in conjunction with other modules, or individually to exemplify a choice topic. With the prerequisite information stated, the reader understands what prior mathematical understanding is required to successfully use the module. I think this rearranged version of the index would better align with current Introductory Statistics texts. The structure is very organized with the prerequisite information stated and upcoming learner outcomes highlighted.
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