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所在平台: Udemy |
课程主页: https://www.udemy.com/course/master-complete-statistics-for-computer-science-i/
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**课程名称：**Master Complete Statistics For Computer Science - I **课程概述：** 本课程是为计算机科学专业学生精心设计的统计学入门课程，旨在系统讲解概率论与统计学的基础知识及其在数据分析和结果解读中的应用。在全球工程教育中，统计学已成为分析数据和解释结果的关键工具，对于学生进行项目和研究工作尤为重要。 本课程内容组织严谨，循序渐进，从最基础的概念讲起，逐步深入到更高级的统计学主题。课程共包含150多节视频讲解，内容涵盖随机变量、概率分布、统计量、相关性、回归分析、特征函数、矩生成函数以及概率界限等核心概念。此外，课程还提供了90多个包含详细解答的实例，帮助学习者巩固和检验所学知识。 **课程主要内容模块：** * **引言** * **离散随机变量** * **连续随机变量** * **累积分布函数** * **特殊分布** * **二维随机变量** * **随机向量** * **一个随机变量的函数** * **两个随机变量的一个函数** * **两个随机变量的两个函数** * **集中趋势的度量** * **数学期望与矩** * **离散程度的度量** * **偏度和峰度** * **统计量的解题示例** * **二维随机变量的期望值** * **线性相关** * **相关系数** * **相关系数的性质** * **等级相关系数** * **线性回归** * **回归直线方程** * **Y对X和X对Y的估计标准误** * **特征函数与矩生成函数** * **概率界限** 本课程结构清晰，讲解生动，通过丰富的实例帮助学生深入理解统计学概念，为应对各类考试和未来的学术研究打下坚实基础。
In today's engineering curriculum, topics on probability and statistics play a major role, as the statistical methods are very helpful in analyzing the data and interpreting the results.When an aspiring engineering student takes up a project or research work, statistical methods become very handy.Hence, the use of a well-structured course on probability and statistics in the curriculum will help students understand the concept in depth, in addition to preparing for examinations such as for regular courses or entry-level exams for postgraduate courses.In order to cater the needs of the engineering students, content of this course, are well designed. In this course, all the sections are well organized and presented in an order as the contents progress from basics to higher level of statistics.As a result, this course is, in fact, student friendly, as I have tried to explain all the concepts with suitable examples before solving problems.This 150+ lecture course includes video explanations of everything from Random Variables, Probability Distribution, Statistical Averages, Correlation, Regression, Characteristic Function, Moment Generating Function and Bounds on Probability, and it includes more than 90+ examples (with detailed solutions) to help you test your understanding along the way. "Master Complete Statistics For Computer Science - I" is organized into the following sections:IntroductionDiscrete Random VariablesContinuous Random VariablesCumulative Distribution FunctionSpecial DistributionTwo - Dimensional Random VariablesRandom VectorsFunction of One Random VariableOne Function of Two Random VariablesTwo Functions of Two Random Variables Measures of Central TendencyMathematical Expectations and MomentsMeasures of DispersionSkewness and KurtosisStatistical Averages - Solved ExamplesExpected Values of a Two-Dimensional Random VariablesLinear CorrelationCorrelation CoefficientProperties of Correlation CoefficientRank Correlation CoefficientLinear RegressionEquations of the Lines of RegressionStandard Error of Estimate of Y on X and of X on YCharacteristic Function and Moment Generating FunctionBounds on Probabilities
