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.

提供方

## Data Science Math Skills

## 课程信息

### 学生职业成果

## 41%

## 34%

#### 可分享的证书

#### 100% 在线

#### 可灵活调整截止日期

#### 初级

#### 完成时间大约为13 小时

#### 英语（English）

### 您将获得的技能

### 学生职业成果

## 41%

## 34%

#### 可分享的证书

#### 100% 在线

#### 可灵活调整截止日期

#### 初级

#### 完成时间大约为13 小时

#### 英语（English）

### 提供方

#### 杜克大学

Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.

## 教学大纲 - 您将从这门课程中学到什么

**完成时间为 18 分钟**

## Welcome to Data Science Math Skills

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

**完成时间为 18 分钟**

**2 个阅读材料**

**完成时间为 4 小时**

## Building Blocks for Problem Solving

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.

**完成时间为 4 小时**

**10 个视频**

**4 个阅读材料**

**4 个练习**

**完成时间为 3 小时**

## Functions and Graphs

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.

**完成时间为 3 小时**

**8 个视频**

**3 个阅读材料**

**3 个练习**

**完成时间为 3 小时**

## Measuring Rates of Change

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.

**完成时间为 3 小时**

**7 个视频**

**3 个阅读材料**

**3 个练习**

**完成时间为 3 小时**

## Introduction to Probability Theory

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.

**完成时间为 3 小时**

**8 个视频**

**4 个阅读材料**

**4 个练习**

### 审阅

#### 4.5

##### 来自DATA SCIENCE MATH SKILLS的热门评论

Effective way to refresh and add the Data Science math skills! Thanks a lot! At the time of the study some of the quizzes content were not rendering correctly on mobile devices (both iPad and Android)

This is neat little course to revise math fundamentals. I generally find learning probability a little tricky. This course helped me a lot in better understanding Bayes Theorem. Thank you professors.

The content of the course as a whole is interesting as a good synthesis of basic skills, even though the content of the weeks 3 and 4 lacks of clarity compared to the content of the weeks 1 and 2.

Please include integration, algorithm analysis (big O, theta, omega), recursion and induction. Your course is helpful, thank you. If you add those things I've mentioned it would be absolute gold.

I thought this course was a nice refresher on basic mathematical concepts and it introduced me to set theory and probability very well! I think I am better prepared for data science afterward!

First 3 weeks were easy going and the last week was a bit more challenging. I think more examples could be included in the lectures to understand Bayes' Theorem at the most fundamental level.

Good refresher. Weeks 3 and 4 are much more difficult to follow than one and two. Part of this is due to the subject matter but also a change of teacher / and style makes it more difficult.

Looking forward to advanced courses on Linear algebra, eculidean geometry that would make the concepts of vectors, matrices, plane and any application of those in the data science problems.

Loved it! Started off easy but got a little tricky in the end with Bayes Theorem. Glad I know which data / math areas I need to brush up on for my job. Thanks, Duke University and Coursera!

sometime the lecture is too easy to understand and after some week it goes too hard to understand even it not a hard thing but sometime the lecturer make it so hard so it can make confuse.

A great review of A level statistics which I had long since forgotten but suddenly found myself needing again. A really good balance of work through the weeks and at just the right level.

The course was very good and well thought of, a great refresher for very important concepts, the instructors are very good at simplifying the material and making it very understandable.

Course is really good for beginner who wants to start career with data. Especially is possible understand everything by video companion which explain math skills in practice exercises.

I don't quite understand the last chapter about probability. Please use more examples like in quiz to demonstrate the concept one by one. Thanks for making education free to all of us.

Great course. First three weeks are a bit basic, and the fourth week is more challenging. Highly recommended. Hope there will be more on derivatives and courses like this. Thank you!

Could have a little more of theory on how to solve the problems. But otherwise a good understanding of the basics of the statistical and probability maths required for Data Analysis.

The Basics were covered pretty nicely. The instructors had given it their all to explain the concepts. The tests did test you pretty well on the concepts you learnt from the videos

It was wonderful course to do and content was good, but there are some improvement needed inside last chapter.\n\nplease add some more slides inside binomial and bayes ' theorems

Really good to remember some maths stuff and to learn and consolidate new maths stuff that I thought I knew (but actually only learnt about it in this course).\n\nGreat course

A great introduction to the basic Mathematics skill needed to start Data Science Career. A must course for does not have the mathematics background required for Data Science.

## 常见问题

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退款政策是如何规定的？

有助学金吗？

Will I receive a transcript from Duke University for completing this course?

No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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