http://www.dspguide.com/pdfbook.htm FOUNDATIONS Chapter 1 - The Breadth and Depth of DSP The Roots of DSP Telecommunications Audio Processing Echo Location Image Processing Chapter 2 - Statistics, Probability and Noise Signal and Graph Terminology Mean and Standard Deviation Signal vs. Underlying Process The Histogram, Pmf and Pdf The Normal Distribution Digital Noise Generation Precision and Accuracy Chapter 3 - ADC and DAC Quantization The Sampling Theorem Digital-to-Analog Conversion Analog Filters for Data Conversion Selecting The Antialias Filter Multirate Data Conversion Single Bit Data Conversion Chapter 4 - DSP Software Computer Numbers Fixed Point (Integers) Floating Point (Real Numbers) Number Precision Execution Speed: Program Language Execution Speed: Hardware Execution Speed: Programming Tips FUNDAMENTALS Chapter 5 - Linear Systems Signals and Systems Requirements for Linearity Static Linearity and Sinusoidal Fidelity Examples of Linear and Nonlinear Systems Special Properties of Linearity Superposition: the Foundation of DSP Common Decompositions Alternatives to Linearity Chapter 6 - Convolution The Delta Function and Impulse Response Convolution The Input Side Algorithm The Output Side Algorithm The Sum of Weighted Inputs Chapter 7 - Properties of Convolution Common Impulse Responses Mathematical Properties Correlation Speed Chapter 8 - The Discrete Fourier Transform The Family of Fourier Transform Notation and Format of the Real DFT The Frequency Domain's Independent Variable DFT Basis Functions Synthesis, Calculating the Inverse DFT Analysis, Calculating the DFT Duality Polar Notation Polar Nuisances Chapter 9 - Applications of the DFT Spectral Analysis of Signals Frequency Response of Systems Convolution via the Frequency Domain Chapter 10 - Fourier Transform Properties Linearity of the Fourier Transform Characteristics of the Phase Periodic Nature of the DFT Compression and Expansion, Multirate methods Multiplying Signals (Amplitude Modulation)

提出实用技术中的数字信号处理，同时避免了详尽的数学和抽象理论的障碍。

The Scientist and Engineer's Guide to Digital Signal Processing第二版高清文字版，值得拥有

Probability & Statistics for engineer and scientist.pdf

Currently used at many colleges, universities, and high schools, this hands-on introduction to computer science is ideal for people with little or no programming experience. The goal of this concise book is not just to teach you Java, but to help you think like a computer scientist. You’ll learn how

How to Think Like a Computer Scientist 经典的计算机系列书籍，这是 C++ Version 也简称 thinkCScpp ，很适合大家学习

How to Think Like a Computer Scientist: C version

《How to Think Like a Computer Scientist C Version》这是我读过最易懂的C语言教材。 虽然它只讲解最基本的语法，但是写得特别好懂，深入浅出，读起来不觉得累，而且它还允许免费下载。我认为，这是C语言的首选入门教材。

1、 简介 管理运筹学软件2.0版是1.0版的升级版，是《管理运筹学》（高等教育出版/韩伯棠编著）的随书软件。 该软件的模块有：线性规划、运输问题、整数规划（0-1整数规划、混合整数规划和纯整数规划）、目标规划、最短路径、最小生成树、最大流量、最小费用最大流、关键路径、存贮论、排队论、决策分析、预测问题、对策论和层次分析法，共15个子模块该软件只可以作为学习和研究使用，请勿作其他用途。 1.1 运行环境 操作系统：Windows2000及以上版本（Windows XP请升级到SP2）。 1.2 使用协议 该软件（管理运筹学软件2.0）由北京理工大学管理与经济学院韩伯棠教授开发，作者保留所有权利。 请勿对该软件进行修改，反编译。 由于作者水平和时间有限，软件中问题和错误难免，欢迎您将使用中的意见和建议反馈给作者。 1.3 联系方式 联系地址：北京理工大学管理与经济学院 联系人：韩伯棠（教授） 邮编：100081 Email : hbt5@bit.edu.cn,jy07@bit.edu.cn 2、使用 具体使用方法请参照《管理运筹学》（高等教育出版/韩伯棠编著）书中附录。

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. Why exploratory data analysis is a key preliminary step in data science; How random sampling can reduce bias and yield a higher quality dataset, even with big data; How the principles of experimental design yield definitive answers to questions; How to use regression to estimate outcomes and detect anomalies; Key classification techniques for predicting which categories a record belongs to; Statistical machine learning methods that "learn" from data; Unsupervised learning methods for extracting meaning from unlabeled data.