Retour à Data Science Math Skills

## Avis et commentaires pour d'étudiants pour Data Science Math Skills par Université Duke

4.5
étoiles
10,300 évaluations
2,294 avis

## À propos du cours

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!...

## Meilleurs avis

AS

11 janv. 2019

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)

VS

22 sept. 2020

This course syllabus is great. It starts wonderfully. Week 1 to 4 is taught by Paul Bendich, and Daniel Egger the instruction is awesome. Effective way to refresh and add the Data Science math skills!

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## 2201 - 2225 sur 2,296 Avis pour Data Science Math Skills

par ahmed s

29 nov. 2019

the lectures of last week r very bad

par THOTAKURA B

25 août 2020

It is not that much effective.

par Binoy K P

6 juil. 2020

T%his course is purely basic.

par the c

19 juin 2020

good course but expected more

par Aydar A

28 août 2017

too superficial to be usefull

11 janv. 2019

1st 2 weeks were very basic.

par Vijay G

12 juil. 2020

Great mathematical analysis

par Snigdha G

13 oct. 2020

Can I get a Certificate?

par Hugh J

27 sept. 2020

Needs more statistics

par Elvin G

19 oct. 2018

It is too low level

par Korkrid A

27 avr. 2018

Good for beginners.

par Vikas G

17 juin 2020

Fantastic teaching

par PARDESHI R H

17 mai 2020

Very useful course

par Shanmuga P

23 févr. 2018

Good!!

par MUTHU A P S

7 juin 2020

good

par Johnny

23 août 2021

Actually the lessons from first three weeks are really basic (junior to senior high level), and sometimes I can't find any strong relationship with data science. And the fourth week is about probability theorem including the marginal/joint probability and bayesian theory without introducing other fundamental concepts, which could make the student quite confused sometimes.

There are several typos by the first teacher, which most are all corrected but quite disturbing. And the handwriting of the second professor is sometimes sloppy. I'd rather check the "video companion" before taking the class. BTW, they are really good and concise to summarize the concept and understand what the professors are talking about.

About the quiz/exam, most are good with explanations. But a lot of question require electronic calculator. Some question do not provide basic value of number like log(2) or which is quite annoying.

It's not good as imagine, but it does not take much time to finish.

par Jose M M L

16 mai 2022

Weeks 1 - 3 can be understood with relative ease depending on your math skills. Week 4, however, is quite tough because both the instructional videos and the notes are difficult, if not impossible, to understand. I had to resort to resources both within and outside of coursera to understand Week 4, specifically YouTube, Khan Academy, free math tutorial videos from my public library, and a separate course on Coursera. I spoke to customer service, but they were unable to give me some sort of credit, or free month, or anything.

Coursera, if you're listening, please do a better job at quality control. Just because a professor is from a top university does not mean he can teach well.

par Carolin K S

21 juil. 2020

This class fails to give a single explanation as to how the math skills will apply to data science. The first three weeks are a nice basic explanation of some of the math that will be needed. If you haven't done coding before or have some background in coding, you will have no idea where this class is going to be applied. The fourth week is the worst explanation of probability you can imagine -- opaque derivations and almost no explanations. I do not recommend this class as it fails to address the "why" of the title -- how does math apply to data science.

par Dennis F

15 mai 2021

i passed all quizzes and exams at 100% most of them in one try.

presentations were a bit sloppy, and often i found i understood the concepts, but not how they were presented.

I did learn things from the course, but the experience could have been a lot better

especially week 4 was problematic, the bayesian probability and inverse probability was hard to understand from the videos or the notes alone, or in combination.

notes for one of the video had only notes for about the first half of the video, so no notes for the rest.

par Mike S

18 mars 2021

The content is OK; the execution isn't quite ready. Most is fairly basic, but is necessary and potentially a good review. But the course comes across like a first draft. Mistakes in videos are not re-recorded, but called out with a disclaimer. I believe there are two errors in answers to practices and quizzes, but it's possibly my own mistake on one. I noticed one instance where the correct answer is in a different font than incorrect answers. Overall, OK content but not very polished. It could use a second revision.

par Borja C

10 sept. 2020

The first 3 weeks are quite good... but then it makes a gigantic leap on the 4th week. Please update the explanations on Bayes and the examples... they are not optimal. Judging from the comments I have seen, I am not the only one.

Thinking about it, I think maybe it would be worth to spend more time with probability and a bit less with the other stuff or even add another week... definitely probability in one week is a far shot... and it is key to understand data science correctly

par Helene H

25 sept. 2020

A pretty good course even if week 3 and 4 are very difficult for beginners.

A few improvements are urgently needed:

-There are mistakes in the quizzes, which mistakes have been pointed out by generations of students for months/years in the forums, but have never been corrected. Such a disgrace and waste of student time!

-Also using Chrome makes you unable to read the formulas, which should be said in the course outset.

par Deepak R

27 déc. 2020

At week 4 the course was covered in a rush. This course could have been much better if the instructor could have taken the time out and walked the extra mile to simplify the concepts for a larger audience. I already had previous rigorous treatment of Probability theory hence didn't face many challenges. But I have empathy with students enrolling in this course who don't have prior probability background.

par Yury K

14 juil. 2020

Course has misleading title. It has no strong connection to data science problems or examples from the field (except confussion matrix). First 3 weeks are simple math. Third one is very brief tour to probability with quite brief explanations which will not be enough for beginners to complete the assignment. There are also errors in the asssigments which I had reported.

I will not recommend it.