Data Science For Dummies is the perfect starting point for IT professionals and students interested in making sense of their organization’s massive data sets and applying their findings to real-world business scenarios. From uncovering rich data sources to managing large amounts of data within hardware and software limitations, ensuring consistency in reporting, merging various data sources, and beyond, you’ll develop the know-how you need to effectively interpret data and tell a story that can be understood by anyone in your organization. Provides a background in data science fundamentals before moving on to working with relational databases and unstructured data and preparing your data for analysis Details different data visualization techniques that can be used to showcase and summarize your data Explains both supervised and unsupervised machine learning, including regression, model validation, and clustering techniques Includes coverage of big data processing tools like MapReduce, Hadoop, Dremel, Storm, and Spark It’s a big, big data world out there – let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

    If you have not yet bought the original copy, make sure to purchase it before buying this unofficial summary from aBookaDay. SPECIAL OFFER $2.99 (Regularly priced: $3.99) OVERVIEW This review of The Big Short: Inside the Doomsday Machine by Michael Lewis provides a chapter by chapter detailed summary followed by an analysis and critique of the strengths and weaknesses of the book. The main theme explored in the book is how corruption and greed in Wall Street caused the crash of the subprime mortgage market in 2008. Despite being completely preventable, the big firms in Wall Street chose to ignore the oncoming fall in favor of making money. Michael Lewis introduces characters—men outside of the Wall Street machine—who foresaw the crisis and, through several different techniques, were able to predict how and when the market would fall. Lewis portrays these men—Steve Eisman, Mike Burry, Charlie Ledley, and Jamie Mai—as the underdogs, who were able to understand and act upon the obvious weaknesses in the subprime market. Lewis’s overall point is to demonstrate how the Wall Street firms were manipulating the market. They used loans to cash in on the desperation of middle-to-lower class Americans, and then ultimately relied on the government to bail them out when the loans were defaulted. Using anecdotes and interviews from the men who were involved first-hand, the author makes the case that Wall Street, and how they conducted business in regards to the subprime mortgage market, is truly corrupt beyond repair, and the men he profiles in this novel were trying to make the best out of a bad situation. By having the words from the sources themselves, this demonstrates Lewis’s search for the truth behind what actually happened. Ultimately, we as an audience can not be sure if the intentions of these underdogs were truly good, but Lewis does an admirable job presenting as many sides to the story as possible. The central thesis of the work is that the subprime mortgage crisis was caused by Wall Street firms pushing fraudulent loans upon middle-to-lower class Americans that they would essentially not be able to afford. Several people outside of Wall Street were able to predict a crash in the market when these loans would be defaulted on, and bought insurance to bet against the market (essentially, buying short). Over a time period from roughly 2005-2008, the market crashed and huge banks and firms lost billions of dollars, filed for bankruptcy, or were bailed out by the government. These men, the characters of Lewis’s novel, were able to bet against the loans and made huge amounts of money, but it was not quite an easy journey. Michael Lewis is a non-fiction author and financial journalist. He has written several novels—notably Liar’s Poker in 1989, Moneyball in 2003, and The Blind Side in 2006. Born in New Orleans, he attended Princeton University, receiving a BA degree in Art History. After attending London School of Economics and receiving his masters there, he was hired by Salomon Brothers where he experienced much about what he wrote about in Liar’s Poker. He is currently married, with three children and lives in Berkeley, California. SUMMARY PROLOGUE: POLTERGEIST Michael Lewis begins his tale of the remarkable—and strange—men who predicted the immense fall of the housing market by immediately exposing himself as the exact opposite type of person from them. He explains to the reader that he has no background in accounting, business, or money managing.

    Provides an introduction to the Old Testament prophetic books, considering their historical and social setting while surveying theological themes.

    The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge."

    The manual also features a skills section, important notes from key sections of the text, and a list of important equations and concepts.

    The Calculus Lifesaver provides students with the essential tools they need not only to learn calculus, but to excel at it.All of the material in this user-friendly study guide has been proven to get results. The book arose from Adrian Banner's popular calculus review course at Princeton University, which he developed especially for students who are motivated to earn A's but get only average grades on exams. The complete course will be available for free on the Web in a series of videotaped lectures. This study guide works as a supplement to any single-variable calculus course or textbook. Coupled with a selection of exercises, the book can also be used as a textbook in its own right. The style is informal, non-intimidating, and even entertaining, without sacrificing comprehensiveness. The author elaborates standard course material with scores of detailed examples that treat the reader to an inner monologue--the train of thought students should be following in order to solve the problem--providing the necessary reasoning as well as the solution. The book's emphasis is on building problem-solving skills. Examples range from easy to difficult and illustrate the in-depth presentation of theory.The Calculus Lifesaver combines ease of use and readability with the depth of content and mathematical rigor of the best calculus textbooks. It is an indispensable volume for any student seeking to master calculus.Serves as a companion to any single-variable calculus textbookInformal, entertaining, and not intimidatingInformative videos that follow the book--a full forty-eight hours of Banner's Princeton calculus-review course--is available at Adrian Banner lecturesMore than 475 examples (ranging from easy to hard) provide step-by-step reasoningTheorems and methods justified and connections made to actual practiceDifficult topics such as improper integrals and infinite series covered in detailTried and tested by students taking freshman calculus

    Topics covered include matrix multiplication, row reduction, matrix inverse, orthogonality and computation. The self-teaching book is loaded with examples and graphics and provides a wide array of probing problems, accompanying solutions, and a glossary. Chapter 1: Introduction to Vectors; Chapter 2: Solving Linear Equations; Chapter 3: Vector Spaces and Subspaces; Chapter 4: Orthogonality; Chapter 5: Determinants; Chapter 6: Eigenvalues and Eigenvectors; Chapter 7: Linear Transformations; Chapter 8: Applications; Chapter 9: Numerical Linear Algebra; Chapter 10: Complex Vectors and Matrices; Solutions to Selected Exercises; Final Exam. Matrix Factorizations. Conceptual Questions for Review. Glossary: A Dictionary for Linear Algebra Index Teaching Codes Linear Algebra in a Nutshell.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    Written by a world-renowned industry expert, the reference discusses how to forecast returns under different parameters. Expected returns of major asset classes, investment strategies, and the effects of underlying risk factors such as growth, inflation, liquidity, and different risk perspectives, are also explained. Judging expected returns requires balancing historical returns with both theoretical considerations and current market conditions. Expected Returns provides extensive empirical evidence, surveys of risk-based and behavioral theories, and practical insights.

    Be sure to look inside the book to get a free sample of this quality product!

    What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory. Concepts are not only described, they are brought to life with actual trading strategies, which give the reader insight into how and why each strategy was developed, how it was implemented, and even how it was coded. This book is a valuable resource for anyone looking to create their own systematic trading strategies and those involved in manager selection, where the knowledge contained in this book will lead to a more informed and nuanced conversation with managers."--DAREN SMITH, CFA, CAIA, FSA, President and Chief Investment Officer, University of Toronto Asset Management"Using an excellent selection of mean reversion and momentum strategies, Ernie explains the rationale behind each one, shows how to test it, how to improve it, and discusses implementation issues. His book is a careful, detailed exposition of the scientific method applied to strategy development. For serious retail traders, I know of no other book that provides this range of examples and level of detail. His discussions of how regime changes affect strategies, and of risk management, are invaluable bonuses."--Roger Hunter, Mathematician and Algorithmic Trader

    If you are interested in the theory or practice of valuation, you should have Damodaran on Valuation on your bookshelf. You can bet that I do. -- Michael J. Mauboussin, Chief Investment Strategist, Legg Mason Capital Management and author of More Than You Know: Finding Financial Wisdom in Unconventional Places In order to be a successful CEO, corporate strategist, or analyst, understanding the valuation process is a necessity. The second edition of Damodaran on Valuation stands out as the most reliable book for answering many of today's critical valuation questions. Completely revised and updated, this edition is the ideal book on valuation for CEOs and corporate strategists. You'll gain an understanding of the vitality of today's valuation models and develop the acumen needed for the most complex and subtle valuation scenarios you will face.

    He worked with - and relaxed with - scientific giants of that time such as Edward Teller, Enrico Fermi, Stan Ulam, John von Neumann, and John Wheeler, and here offers illuminating insights into the personalities, the strengths, and the quirks of these men. Well known for his ability to explain physics to nonspecialists, Ford also brings to life the physics of fission and fusion and provides a brief history of nuclear science from the discovery of radioactivity in 1896 to the ten-megaton explosion of “Mike” that obliterated a Pacific Island in 1952. Ford worked at both Los Alamos and Princeton's Project Matterhorn, and brings out Matterhorn's major, but previously unheralded contribution to the development of the H bomb. Outside the lab, he drove a battered Chevrolet around New Mexico, a bantam motorcycle across the country, and a British roadster around New Jersey. Part of the charm of Ford's book is the way in which he leavens his well-researched descriptions of the scientific work with brief tales of his life away from weapons.Contents: The Big Idea The Protagonists The Choice The Scientists, the Officials, and the President Nuclear Energy Some Physics Going West A New World The Classical Super Calculating and Testing Constructing Matterhorn Academia Cowers New Mexico, New York, and New Jersey The Garwin Design Climbing Matterhorn It's More Than a Boy Readership: A memoir for general readership in the history of science.Key Features: It contains real physics, clearly presented for non-specialists Combining historical scholarship and his own recollections, the author offers important insights into the people and the work that led to the first H bomb Personal anecdotes enliven the book

    Written by the world’s foremost PowerPivot blogger and practitioner, the book’s concepts and approach are introduced in a simple, step-by-step manner tailored to the learning style of Excel users everywhere. The techniques presented allow users to produce, in hours or even minutes, results that formerly would have taken entire teams weeks or months to produce. It includes lessons on the difference between calculated columns and measures; how formulas can be reused across reports of completely different shapes; how to merge disjointed sets of data into unified reports; how to make certain columns in a pivot behave as if the pivot were filtered while other columns do not; and how to create time-intelligent calculations in pivot tables such as “Year over Year” and “Moving Averages” whether they use a standard, fiscal, or a complete custom calendar. The “pattern-like” techniques and best practices contained in this book have been developed and refined over two years of onsite training with Excel users around the world, and the key lessons from those seminars costing thousands of dollars per day are now available to within the pages of this easy-to-follow guide. This updated second edition covers new features introduced with Office 2015.

    "Using Econometrics: A Practical Guide "provides readers with a practical introduction that combines single-equation linear regression analysis with real-world examples and exercises. This text also avoids complex matrix algebra and calculus, making it an ideal text for beginners. New problem sets and added support make "Using Econometrics" modern and easier to use.