Python

金融風險管理已經成為各個金融機構必備的職能部門。特別是隨著全球金融一體化不斷地深入發展，金融風險管理越發重要，也日趨複雜。金融風險管理師（FRM）就是在這個大背景下推出的認證考試，FRM現在已經是金融風險管理領域頂級權威的國際認證考試。本叢書以FRM考試第一、二級考綱內容為中心，並且突出介紹實際工作所需的金融建模風險管理知識。本叢書將金融風險建模知識和Python編程有機地結合在一起，配合豐富的彩色圖表，由淺入深地將各種金融概念和計算結果可視化，幫助讀者理解金融風險建模核心知識，提高數學和編程水平。 本書是本系列圖書的第6本，共分12章。本書的第1章和第2章主要介紹Python基礎編程內容，比如數據類型、運算符、條件循環語句、讀寫操作、函數等。第3章和第4章主要介紹NumPy和Scipy等常見的數學工具包的典型應用。第5章和第6章討論采用Pandas進行數據分析。在前6章內容的基礎上，第7章介紹常見的可視化方案。然後結合Python編程，第8章和第9章介紹金融建模中常用的概率和統計知識。第10章和第11章討論金融建模中各種常見的初等和高等數學內容，這些內容是後續金融產品定價和風險分析的數學基礎。第12章主要研究固定收益定價和分析等內容。 本書適合所有金融從業者閱讀，特別適合金融編程零基礎讀者參考學習。本書適合FRM考生備考參考學習，可以幫助FRM持證者實踐金融建模。另外，本書也是鞏固金融知識、應對金融筆試和面試的利器。

Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for Python, Fourth Edition, is focused on the development and implementation of statistically motivated, data-dri

This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.

The parameter estimation and hypothesis testing are the basic tools in statistical inference. These technics occur in many applications of data processing. Besides that, methods of Monte Carlo type ha

Based on the latest version of the language, this book offers a self-contained, concise and coherent introduction to programming with Python. The book’s primary focus is on realistic case study applic

Compete on analytics: win by understanding your data more deeply than your competitors do! In Modeling Techniques in Predictive Analytics, the Python edition , the leader of Northwestern University

"This book is an introduction to numerical methods for students in engineering. It covers solution of equations, interpolation and data fitting, solution of differential equations, eigenvalue problems