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.

# Data Science Math Skills

ofrecido por

## Data Science Math Skills

## Acerca de este Curso

### Resultados profesionales del estudiante

## 41%

## 34%

### Habilidades que obtendrás

### Resultados profesionales del estudiante

## 41%

## 34%

#### 100 % en línea

#### Fechas límite flexibles

#### Nivel principiante

#### Aprox. 16 horas para completar

#### Inglés (English)

## Programa - Qué aprenderás en este curso

**18 minutos para completar**

## 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

**18 minutos para completar**

**1 video**

**2 lecturas**

**4 horas para completar**

## 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.

**4 horas para completar**

**10 videos**

**4 lecturas**

**4 ejercicios de práctica**

**3 horas para completar**

## 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.

**3 horas para completar**

**8 videos**

**3 lecturas**

**3 ejercicios de práctica**

**3 horas para completar**

## 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.

**3 horas para completar**

**7 videos**

**3 lecturas**

**3 ejercicios de práctica**

**3 horas para completar**

## 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.

**3 horas para completar**

**8 videos**

**4 lecturas**

**4 ejercicios de práctica**

### Revisiones

##### Principales revisiones sobre DATA SCIENCE MATH SKILLS

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!

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.

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.

### Acerca de Universidad Duke

## Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?

Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

¿Qué recibiré si compro el Certificado?

Cuando compras un Certificado, obtienes acceso a todos los materiales del curso, incluidas las tareas calificadas. Una vez que completes el curso, se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes participar del curso como oyente sin costo.

¿Cuál es la política de reembolsos?

¿Hay ayuda económica disponible?

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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