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.

제공자:

## Data Science Math Skills

## About this Course

### 학습자 경력 결과

## 41%

## 34%

### 귀하가 습득할 기술

### 학습자 경력 결과

## 41%

## 34%

#### 공유 가능한 수료증

#### 100% 온라인

#### 유동적 마감일

#### 초급 단계

#### 완료하는 데 약 16시간 필요

#### 영어

### 제공자:

#### 듀크대학교

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.

## 강의 계획 - 이 강좌에서 배울 내용

**완료하는 데 18분 필요**

## 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

**완료하는 데 18분 필요**

**1개의 동영상**

**2개의 읽기 자료**

**완료하는 데 4시간 필요**

## 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.

**완료하는 데 4시간 필요**

**10개의 동영상**

**4개의 읽기 자료**

**4개 연습문제**

**완료하는 데 3시간 필요**

## 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.

**완료하는 데 3시간 필요**

**8개의 동영상**

**3개의 읽기 자료**

**3개 연습문제**

**완료하는 데 3시간 필요**

## 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.

**완료하는 데 3시간 필요**

**7개의 동영상**

**3개의 읽기 자료**

**3개 연습문제**

**완료하는 데 3시간 필요**

## 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.

**완료하는 데 3시간 필요**

**8개의 동영상**

**4개의 읽기 자료**

**4개 연습문제**

### 검토

#### 4.5

##### DATA SCIENCE MATH SKILLS의 최상위 리뷰

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.

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!

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.

## 자주 묻는 질문

강의 및 과제를 언제 이용할 수 있게 되나요?

강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

이 수료증을 구매하면 무엇을 이용할 수 있나요?

수료증을 구매하면 성적 평가 과제를 포함한 모든 강좌 자료에 접근할 수 있습니다. 강좌를 완료하면 전자 수료증이 성취도 페이지에 추가되며, 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 콘텐츠만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

환불 규정은 어떻게 되나요?

재정 지원을 받을 수 있나요?

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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