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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/datasciencemathskills
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课程名称：数据科学数学技能 课程概述：数据科学课程包含数学——无法避免！本课程旨在教授学习者成功掌握几乎所有数据科学数学课程所需的基本数学知识，特别适合具有基本数学技能但可能没有修过代数或预备微积分的学习者。数据科学数学技能将逐步引入构成数据科学核心的数学内容，以简单明了的方式介绍不熟悉的概念和数学符号。完成本课程的学习者将掌握所有数据科学家必须了解的词汇、符号、概念和代数规则，为进一步学习更高级材料做好准备。 课程内容包括： - 集合论，包括维恩图 - 实数线的性质 - 区间符号和不等式代数 - 求和和西格玛符号的应用 - 笛卡尔平面上的数学，斜率和距离公式 - 在x-y平面上绘制和描述函数及其反函数 - 瞬时变化率和曲线切线的概念 - 指数、对数及自然对数函数 - 概率论，包括贝叶斯定理 本课程作为数据科学所需数学技能的一般介绍，可以视为有意向学习“精通Excel中的数据分析”课程的学习者的先修课程。掌握数据科学数学技能的学习者将为在“精通Excel中的数据分析”课程中学习更高级的数学概念做好充分准备。 祝好运，希望您享受本课程！ 课程大纲： 1. 欢迎来到数据科学数学技能 - 描述：此短模块包括课程结构、工作流程和证书、测验、视频讲座等重要信息的概述。请立即阅读，并在需要时参考。 2. 问题解决的基石 - 描述：本模块包含三个课程，构建基础数学词汇。第一节“集合及其用途”介绍集合论的基本概念，包括并集、交集与基数，并提供实际应用的医疗测试案例。第二节“实数的无限世界”解释了在实数线上讨论区间时使用的符号。最后一节“那锯齿状的S符号”将教你如何简洁地表达长序列的加法，并利用该技能定义统计量，如均值和方差。 3. 函数与图像 - 描述：本模块为平面函数图像绘制构建词汇。第一节“笛卡尔真聪明”将让你了解笛卡尔平面，测量距离并找到直线方程。第二节介绍了函数作为输入-输出机器的概念，展示如何在笛卡尔平面上绘图，并讲解相关重要词汇。 4. 变化率测量 - 描述：本模块开始温和地介绍微积分的导数概念。第一节“这是关于导数的东西”将给出基本定义，举例说明，并展示如何将这些概念应用于现实世界的优化问题。然后我们转向指数和对数，解释这些数学工具的规则和符号。最后，我们学习连续增长的变化率，以及捕捉这一概念的特殊常数“e”，约为2.718。 5. 概率论入门 - 描述：本模块介绍概率论的词汇和符号——用于研究不确定但具有可预测发生率的结果的数学。首先介绍概率的基本定义和规则，包括两个或多个事件同时发生的概率、和规则和乘积规则，然后进入贝叶斯定理及其在实际问题中的应用。
Name:Welcome to Data Science Math Skills
Description:This short module includes an overview of the course's structure, working process, and information about course certificates, quizzes, video lectures, and other important course details. Make sure to read it right away and refer back to it whenever needed
Name:Building Blocks for Problem Solving
Description:This module contains three lessons that are build to basic math vocabulary. The first lesson, "Sets and What They’re Good For," walks you through the basic notions of set theory, including unions, intersections, and cardinality. It also gives a real-world application to medical testing. The second lesson, "The Infinite World of Real Numbers," explains notation we use to discuss intervals on the real number line. The module concludes with the third lesson, "That Jagged S Symbol," where you will learn how to compactly express a long series of additions and use this skill to define statistical quantities like mean and variance.
Name:Functions and Graphs
Description:This module builds vocabulary for graphing functions in the plane. In the first lesson, "Descartes Was Really Smart," you will get to know the Cartesian Plane, measure distance in it, and find the equations of lines. The second lesson introduces the idea of a function as an input-output machine, shows you how to graph functions in the Cartesian Plane, and goes over important vocabulary.
Name:Measuring Rates of Change
Description:This module begins a very gentle introduction to the calculus concept of the derivative. The first lesson, "This is About the Derivative Stuff," will give basic definitions, work a few examples, and show you how to apply these concepts to the real-world problem of optimization. We then turn to exponents and logarithms, and explain the rules and notation for these math tools. Finally we learn about the rate of change of continuous growth, and the special constant known as “e” that captures this concept in a single number—near 2.718.
Name:Introduction to Probability Theory
Description:This module introduces the vocabulary and notation of probability theory – mathematics for the study of outcomes that are uncertain but have predictable rates of occurrence. We start with the basic definitions and rules of probability, including the probability of two or more events both occurring, the sum rule and the product rule, and then proceed to Bayes’ Theorem and how it is used in practical problems.
Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time. Learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material. Topics include: ~Set theory, including Venn diagrams ~Properties of the real number line ~Interval notation and algebra with inequalities ~Uses for summation and Sigma notation ~Math on the Cartesian (x,y) plane, slope and distance formulas ~Graphing and describing functions and their inverses on the x-y plane, ~The concept of instantaneous rate of change and tangent lines to a curve ~Exponents, logarithms, and the natural log function. ~Probability theory, including Bayes’ theorem. While this course is intended as a general introduction to the math skills needed for data science, it can be considered a prerequisite for learners interested in the course, "Mastering Data Analysis in Excel," which is part of the Excel to MySQL Data Science Specialization. Learners who master Data Science Math Skills will be fully prepared for success with the more advanced math concepts introduced in "Mastering Data Analysis in Excel." Good luck and we hope you enjoy the course!
