# 學生對 杜克大学 提供的 Data Science Math Skills 的評價和反饋

4.5
4,595 個評分
1,036 條評論

## 課程概述

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!...

## 熱門審閱

##### RS

May 06, 2020

This was mostly review for me though probability especially Beyes Theorem derivation was new. The instructors provided clear often refreshing ways to look at material.\n\nThank you for a great class!!

##### VC

May 17, 2020

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)

## 51 - Data Science Math Skills 的 75 個評論（共 1,045 個）

Mar 30, 2020

very beginner misleading title, not a significant knowledge in Math for DS!

Dec 11, 2017

TRASH!

May 21, 2020

The course overall was great. It was well taught-- very relevant and clear for the most part. I found the Probability lectures hard to follow. It seemed you need to know a lot of probability theory beforehand. Also the videos were too short in this sections and went very fast. The videos need to be longer, with 20-25 minutes and with more examples. The quizzes in this section were the hardest because not many examples were given in the lecture. Overall though I feel accomplished and feel I can tackle the math that comes my way when I pursue my data science degree. I will certainly recommend the course to my friends who wish to have better knowledge in mathematics for data science.

Apr 06, 2020

The course is a prerequisite for a Data Science course and its aim is to empowering your math skill :) With this purpouse, the course covers important mathematical topics; it can be used for advanced learners as a revision and a recap of fundamental subjects but someone may find it a little bit boring. I love maths so I really had enjoyed the course. For me, the last part (about probability, Bayes theorems, etc...) was the more challenging and interesting but I had also appreciated the first part with the funny explanations of prof. Bendich :) I think that the pdf companions are really useful to follow the lessons better.

Apr 08, 2018

Este curso lo recomiendo mucho a quienes estén interesados en refrescar sus conocimientos de matemáticas para pasar a cursos de estadística o data science. Es muy compacto por lo que los temas se tratan de manera concisa, pero realmente se avanza si se invierte el tiempo necesario. Yo estoy interesada en la estadística y mi campo es la lingüística, así que me tocó trabajar muchas horas haciendo cuentas en el papel y en la calculadora, buscando cómo hacer para sacar las distribuciones binomiales y las funciones básicas, pero me pareció al final que he dado grandes avances, me encantan las matemáticas.

Jun 19, 2017

While most of material is well known, it is presented in a great way, so it is a clean and smart refresher for Sets, basic Algebra and notations, Cartesian geometry and functions, and derivatives. I knew the material about logarithms, exponentials and probabilities, but I felt that I knew it better in the end of this courses. Material is great, and teachers are very clear. I wish they came with more material about calculus (matrices), vector spaces, Lagrangian, Hessian and so on, which are also really interesting in Data Sciences.

Sep 21, 2017

This course offers a great refresher of the FUNDAMENTALS of Linear Algebra , Calculus and Probability.

Do note the strong emphasis on fundamentals.

All lectures are well produced and the material put forward in an unambiguous and layman language.

The concepts presented are very easy to grasp , all thanks to the brilliant efforts of professor Bendich and professor Egger.

This course , along with another course on Calculus would serve as a great starting point for all data science enthusiasts and I strongly recommend it to everyone.

Jan 15, 2017

One of the best course, i have ever learnt. Even though i have been learning the Data Science for the last few years, i had no idea how the algorithms are working in technical. Which i was always skeptical. But honestly, now i am able to get things really faster than before. I am very happy, i have joined this course. Thank you so much for coursera to bring this course and importantly thank you so much for the professors to explain things in an easy for the people to understand. God bless you both and your family.

Apr 18, 2020

I thoroughly enjoyed taking this course because of the effective syllabus that reviews the math skills for Data Science. I liked having both Test and Graded Quiz to check the understandings of the subject.

Test Quiz gives good feedback on both correct and incorrect answer that helps to compare the problem-solving strategy and solution of the students with the correct solution.

Graded Quiz gradually becomes more challenging and the week 4 graded quiz is the most challenging quiz of all the graded quizzes.

May 15, 2020

I enjoyed the course content and lectures. The quizzes were a good test of understanding. I was wondering if there could have been a few more additional lectures and practice problems on probability. I request the team to check the answer to the 11th question on the last quiz of week 4.

Prof. Egger's lectures were very interesting and I only wish he had a larger writing board or apparatus. Thank you for getting me interested in a subject that is not my core area of work!

Apr 26, 2020

From 3 courses I've taken so far, this one was the best, because it covers a lot from basics to complex math. By the end of the course it does try to cover very complicated topics, which if you don't have training in, you will feel the need to supplement from another resources, even though the reading section of this course helps a lot. Lectures on Khan academy were also very helpful in remembering lost concepts.

Apr 19, 2020

The course was easy and comprehensible as long as you have done basic maths at some point in your life. For those who don't I would suggest to go and take a calculus and statistics course and revisit. Otherwise, you would have to do some research by your own, in order to be able to follow. Keep in mind that if you do not see the exponents etc, change browser. Chrome seems to work better than my Firefox.

Apr 10, 2020

I am fairly new to the field of Data Science and Machine Learning, and I felt like i had to strengthen my math skills, hence why I enrolled in the class. The professor did a great job explaining everything in detail, and brushing up on simple math terms. I feel more confident now to move forward into data science that I have a basic knowledge and understanding of the math concepts.

Apr 26, 2020

First of all I would like to thanks Coursera for providing the different types of courses by the world best instructors from the top universities. From this course, I get to know more deeply knowledge about data science maths skills .By, using those knowledge I can apply those in real life problem related to this.

Thank you very much Coursera for providing this type of platform

Apr 08, 2020

I totally liked this course for clear explanations and plenty of practical exercises. Some of them were not easy for me, but thanks to comments after mistakes I could finally find the right solutions. Just in the last week in the final quiz, there were no explanations, but maybe it was a problem on the site side. This course made me think a lot, and I enjoyed it genuinely.

Aug 27, 2017

Succinct explanation of the basics. Take more time at the Bayes theorem. It is worth it. Work out all the problems and keep reading the PDF notes accompanied with the videos. All in all, a great experience for those who have missed some basic math in earlier education. I am onward to my next course in machine learning and data science. Cheers

Jan 31, 2019

First of all thanks to the data science math skill because i learned many new things,ideas,knowledge and skills from this course and more thankful to professors because of them i am able to give all the answers and it was too much interesting to do .

Thanks to all the teams of coursera as well as to the data science math skill......

Mar 11, 2017

If you are a right-brainer and/or rusty on math, I strongly recommend this course as a precursor to Duke's Intro to Probability and Data course. Some of the practice and final quiz questions really threw me (and that's good)... Most of them I was able to rethink and derive the correct answer and a few others remain a mystery... :-)

Jan 22, 2017

I loved this class, the only one of it's kind and much needed, unless you particularly want to re-do your long forgotten high school and college math. It was nice seeing a Venn diagram again. I did have to supplement some of the material that was covered quickly with google searches, but filling in the blanks was quick and easy.

Apr 18, 2018

Hi. A very good refresher course that serves as a pre-requisite to Machine Learning and Data Science courses. Probability could have been a little better explained, specially the processes and event part. I would also like to see Vectors and Matrices added to this course, which is equally vital for Data Science.

May 20, 2020

This course provided clear, expert teaching at a very good pace. The materials were very helpful & directly applicable. The videos were well portioned, and the professors are well spoken & highly competent. I highly recommend Coursera, these professors & this course. I plan on pursuing more in this subject.

Jan 31, 2019

Learning this course I have gain many new and interesting skills. I am very much glad to get the knowledge from two professors and they gives me more knowledge on those interesting courses. I was able to do the answers of the given courses.And I THANKS them to give me such opportunity to do these courses.

Apr 04, 2018

Everything was great except probability theory. The videos were hard to follow and understand because everything was a kind of mess. Reading materials would be much better for probability section. Overall it was very helpful for me and I am very grateful for this wonderful course!!!

Apr 16, 2020

The course is completely related to prerequisite data science skills. There are lots of useful materials. However, the last module (probability) is kind of introduction and superficial, and do not discuss probabilities concepts and distributions in depth.

In general, the course is Great

Aug 22, 2017

Good course because it focuses on basic statistical science needed in Data Science. Only issue I had with this course was it was pretty short. Shorter than I thought by looking at the syllabus. Also the agenda is very simple in the first couple of weeks until it gets to the last week.