Tag Archives: analysts

Sabotage: America’s Enemies Within the CIA

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Sabotage: America's Enemies Within the CIA By Rowan Scarborough
2007 | 229 Pages | ISBN: 1596985100 | PDF | 3 MB

Sabotage: America’s Enemies Within the CIA
How Bush-hating CIA Bureaucrats Are Sabotaging the War on Terror Since the attacks on September 11, 2001, intelligence collection has become the number-one weapon in the effort to defeat al Qaeda and Osama bin Laden. A Description penetrated is an attack stopped. And to the outside observer, the CIA has performed well as a key partner in the Bush administration's War on Terror. But as Rowan Scarborough reveals in this groundbreaking new book, significant elements within the CIA are undermining both the president and national security through leaks, false allegations, and outright sabotage. Using his first-rate sources in all levels of national security–from field officers to high-ranking analysts to former intelligence heads–Scarborough paints a disturbing picture of partisan politics endangering the success of our campaigns abroad and the very lives of our soldiers and agents. In Sabotage, you'll learn: * How CIA analysts repeatedly leak details about classified intelligence programs with the dual intent of ending them and damaging the president * How, on at least eight occasions, intelligence officials have made serious allegations of wrongdoing against the president's men–which turned out to be false * Why, contrary to popular belief, the CIA has become predominantly liberal * How a CIA turf battle prevented special operators from pursuing and capturing a notorious Taliban leader * How current and former CIA officers fueled conspiracy theories that President Bush orchestrated the 9/11 attacks on America * How a CIA leak to the New York Times deprived the U.S. of critical information in the War on Terror * How press leaks by the CIA have damaged relations with our foreign allies in the War on Terror * How a CIA analyst worked with Democrats to sabotage the nomination of John Bolton to the UN * How Clinton's downsizing of the CIA led to the closing of stations in scores of jihadist breeding grounds–including Hamburg, Germany, where the 9/11 Description was hatched The CIA's job is to collect facts and let the White House, the Pentagon, and the State Department make national security policy. But, as Scarborough conclusively demonstrates, an agency that is supposed to be scrupulously nonpartisan has become increasingly political–during a time of war–against America's elected commander in chief.
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Modern HPLC for Practicing Scientists by Michael W. Don

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Modern HPLC for Practicing Scientists by Michael W. Dong
English | June 12, 2006 | ISBN: 047172789X | 304 Pages | PDF | 5 MB
Written for practitioners by a practitioner, Modern HPLC for Practicing Scientists is a concise text which presents the most important High-Performance Liquid Chromatography (HPLC) fundamentals, applications, and developments.

Modern HPLC for Practicing Scientists by Michael W. Don
It describes basic theory and terminology for the novice, and reviews relevant concepts, best practices, and modern trends for the experienced practitioner. Moreover, the book serves well as an updated reference guide for busy laboratory analysts and researchers.
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Women Entrepreneurs and the Global Environment for Growth: A Research Perspective

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Women Entrepreneurs and the Global Environment for Growth: A Research Perspective by Candida G. Brush, Anne de Bruin, Elizabeth J. Gatewood and Colette Henry
English | ISBN 10: 1847209149 | 2010 | PDF | 362 pages | 2,2 MB
Women's entrepreneurship research and the understanding of factors influencing the growth of women-owned business have advanced significantly over the last decade. Yet, challenges remain.

Women Entrepreneurs and the Global Environment for Growth: A Research Perspective
Women Entrepreneurs and the Global Environment for Growth provides wide-ranging insights on the challenges that women entrepreneurs face growing their businesses and how these may be addressed. This volume is rooted in research and considers growth challenges, both contextually and firm specific, provoking current thought and enriching the current literature on gender and entrepreneurship. Part I highlights how contextual factors, and especially social and familial settings of entrepreneurs, have a differential impact on men and women. Part II examines strategies, constraints and enablers of growth and performance.

The authors aptly demonstrate that a well-focused gender lens is necessary to better explain the phenomenon of women's entrepreneurship. Extending previous studies about women's entrepreneurship, this volume is unique in its application of research from the Diana Project, a path-breaking initiative dating from 1999 to study female entrepreneurial success. Contributions from an international cast of authors make this a comprehensive and broadly appealing reference work. Lending a fresh perspective to the field, this book will serve not only as a learning tool and teaching implement but will cultivate further progress in women's entrepreneurship. As such, it is ideally suited for students and scholars of entrepreneurship and women's studies, policy-makers, economic development analysts and gender researchers.

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Python for Finance: Analyze Big Financial Data

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Yves Hilpisch, "Python for Finance: Analyze Big Financial Data"
English | ISBN: 1491945281 | 2015 | 606 pages | True PDF | 10 MB

Python for Finance: Analyze Big Financial Data
The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance.

Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include:
Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices
Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression
Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies
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Python for Finance: Analyze Big Financial Data

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Yves Hilpisch, "Python for Finance: Analyze Big Financial Data"
English | ISBN: 1491945281 | 2015 | 606 pages | True PDF | 10 MB

Python for Finance: Analyze Big Financial Data
The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance.

Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include:
Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices
Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression
Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies
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Python for Finance: Analyze Big Financial Data (PDF)

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Yves Hilpisch, "Python for Finance: Analyze Big Financial Data"
English | ISBN: 1491945281 | 2015 | 606 pages | True PDF | 10 MB
The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance.

Python for Finance: Analyze Big Financial Data (PDF)
Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include:
Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matDescriptionlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices
Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression
Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies

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Europe in Crisis: Bolt from the Blue?

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Ivan Berend, "Europe in Crisis: Bolt from the Blue?"
English | ISBN: 0415637244, 0415637228 | 2012 | PDF | 184 pages | 5,9 MB

Europe in Crisis: Bolt from the Blue?
This book analyzes the European Great Recession of 2008-12, its economic and social causes, its historical roots, and the policies adopted by the European Union to find a way out of it. It contains explicit debates with several economists and analysts on some of the most controversial questions about the causes of the crisis and the policies applied by the European Union.

It presents the cases of Iceland, Greece and Ireland, the countries that first declined into crisis in Europe, each of them in a different way. Iceland is a case study for reckless banking practices, Greece of reckless public spending, and Ireland of reckless household indebtedness. At least seven other countries, mostly from the peripheries of Europe, had similarly reckless banking and spending practices.

In the center of the book are the economic and social causes of the crisis. Contemporary advanced capitalism became financialized, de-industrialized and globalized and got rid of the "straitjacket" of regulations. Solid banking was replaced by high-risk, "casino-type" activity. The European common currency also had a structural problem – monetary unification without a federal state and fiscal unification. The other side of the same coin is European hyper-consumerism. A new lifestyle emerged during two super-prosperous periods in the 1950s to 1960s, and during the 1990s to 2006. Trying to find an exit policy, the European Union turned to strict austerity measures to curb the budget deficit and indebtedness. This book critically analyzes the debate around austerity policy.

The creation of important supra-national institutions, and of a financial supervisory authority and stability mechanisms, strengthens integration. The correction of the euro's structural mistake by creating a quasi-fiscal unification is even more important. The introduction of mandatory fiscal rules and their supervision promises a long-term solution for a well-functioning common currency. These measures, meanwhile, create a two-tier European Union with a fast-track core. This book suggests that the European Union will emerge stronger from the crisis.

This book will be of particular interest to students and researchers of economics, history, political science and international finance, but will also prove profitable reading for practitioners and the interested public.
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Scikit-Learn Cookbook

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Scikit-Learn Cookbook by Trent Hauck
2014 | ISBN: 1783989483 | English | 214 pages | PDF | 3 MB

Scikit-Learn Cookbook
Over 50 recipes to incorporate scikit-learn into every step of the data science pipeline, from feature extraction to model building and model evaluation

About This Book

Learn how to handle a variety of tasks with Scikit-Learn with interesting recipes that show you how the library really works
Use Scikit-Learn to simplify the programming side data so you can focus on thinking
Discover how to apply algorithms in a variety of situations
Who This Book Is For

If you're a data scientist already familiar with Python but not Scikit-Learn, or are familiar with other programming languages like R and want to take the plunge with the gold standard of Python machine learning libraries, then this is the book for you.

In Detail

Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. Its consistent API and plethora of features help solve any machine learning problem it comes across.

The book starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets.
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