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所在平台: Udemy |
课程主页: https://www.udemy.com/course/the-data-science-course-2020-q2-updated-part-3/
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Coursera课程《二项分布、正态分布与数据科学矩阵》内容总结 本课程作为数据科学基础系列课程的第三部分，旨在巩固学习者在概率、描述性统计、数据可视化（直方图、箱线图、散点图）以及协方差与相关性等方面的基础。 课程重点内容包括： * **正态分布 (Normal Distribution)**：描述具有对称“钟形”特征的连续数据分布。 * **二项分布 (Binomial Distribution)**：描述有限样本中二元数据（成功/失败）的分布，计算n次试验中发生r次事件的概率。 * **Z分布 (Z-distribution)**：用于查找标准正态分布（X）的概率和百分位数，是衡量其他正态分布的标准。 * **中心极限定理 (Central Limit Theorem, CLT)**：在特定情况下，独立随机变量之和（经过适当归一化）趋向于正态分布，即使原始变量本身不是正态分布。 * **决策制定 (Decision making)**：通过计算事件发生的概率（事件发生的可能性数量 / 所有可能性的总数），帮助做出更明智的决策，尤其是在结果不确定的情况下。 * **CRISP-DM方法论 (CRISP-DM)**：一种跨行业的、结构化的数据挖掘项目规划流程。 * **假设检验 (Hypothesis testing)**：统计学中用于检验关于总体参数的假设，通过样本数据评估假设的合理性。 * **方差分析 (ANOVA)**：分析样本中各组均值差异的统计模型，通过评估组内和组间变异来分析数据。 * **矩阵、坐标几何、微积分与代数基础**。 本课程通过四部分系列课程，循序渐进地为学习者打下坚实的数据科学基础。学员反馈强调了课程的实用性和易懂性，帮助他们提升作为数据科学家的能力。
Building on the Foundation: In this course we continue to build your foundation on Data Science. In our Part 2 course you learned Probability, Descriptive Statistics, Data Visualization, Histogram, Boxplot & Scatter plot, Covariance & Correlation. In Part 3 we will help you learn Binomial & Normal Distribution, TOH, CRISP-DM, Anova, Matrices, Coordinate Geometry & Calculus.You will learn the following concepts with examples in this course:Normal distribution describes continuous data which have a symmetric distribution, with a characteristic 'bell' shape.Binomial distribution describes the distribution of binary data from a finite sample. Thus it gives the probability of getting r events out of n trials.Z-distribution is used to help find probabilities and percentiles for regular normal distributions (X). It serves as the standard by which all other normal distributions are measured.Central limit theorem (CLT) establishes that, in some situations, when independent random variables are added, their properly normalized sum tends toward a normal distribution (informally a bell curve) even if the original variables themselves are not normally distributed.Decision making: You can calculate the probability that an event will happen by dividing the number of ways that the event can happen by the number of total possibilities. Probability can help you to make better decisions, such as deciding whether or not to play a game where the outcome may not be immediately obvious.CRISP-DM is a cross-industry process for data mining. The CRISP-DM methodology provides a structured approach to planning a data mining project. It is a robust and well-proven methodology.Hypothesis testing is an act in statistics whereby an analyst tests an assumption regarding a population parameter. Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data. Such data may come from a larger population, or from a data-generating process.Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among group means in a sample. ANOVA was developed by statistician and evolutionary biologist Ronald Fisher.Basics of Matrices, Coordinate Geometry, Calculus & AlgebraThrough our Four-part series we will take you step by step, this course is our third part which will solidify your foundation.Testimonials:I have gained a strong foundation and understanding to help me be a better Data Scientist ~ Reginald Owusu Ansahconcepts are explained in a very easy way. Thank you, sir!! ~ Kiran NikumbhAmazing course. It helped me a lot. ~ Priyanka Agarwalthis course is very helpful ~ Muaadh
