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

4.5
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2,197 個評分
489 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

AS

Jan 12, 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)

PS

Jul 23, 2017

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.

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451 - Data Science Math Skills 的 475 個評論(共 481 個)

創建者 G T

Mar 01, 2017

The material is very useful, however, the second teacher is not the best...

創建者 Federico V

Jan 26, 2018

It is more of an "appetizer" than a whole self-contained "meal".

創建者 Do B D

May 30, 2017

The contents were ok, but the videos were a bit boring

創建者 Rafael B

Feb 24, 2018

Lost if it is a basic or a intermediate level course.

創建者 Nathaniel L

Dec 28, 2017

The materials could be more advanced for data science

創建者 Bola T

Jun 02, 2017

last week is not good at all i didn't get it all

創建者 Piyush G

Sep 13, 2018

Probability concepts could be more descriptive.

創建者 Ahmed S H H

Nov 29, 2019

the lectures of last week r very bad

創建者 Aydar A

Aug 28, 2017

too superficial to be usefull

創建者 PRASAD K

Jan 11, 2019

1st 2 weeks were very basic.

創建者 Elvin G

Oct 19, 2018

It is too low level

創建者 Kyle A

Apr 28, 2018

Good for beginners.

創建者 Shanmuga P

Feb 23, 2018

Good!!

創建者 Md. Z M

Mar 08, 2019

For someone with a Computer Science background at the undergraduate level, I find the contents basic. However, the intention of the course was to give a refresher for data science professionals who find the mathematical jargon frequently used in practice hard to comprehend. In this sense, the first half of the course taught by Prof. Paul Bendich were good. The second part of the course taught by Prof. Daniel Egger needs a lot of improvement in content delivery and better explanation. The quizzes on probability are challenging and enjoyable. Also, when I took the course as on March 2019, there wasn't any activity on the discussion forum. It seems there are not many students taking the course with me, and it also wasn't monitored by the course staff.

創建者 Deleted A

Aug 20, 2017

The first two weeks are good. The material is explained in a fairly intuitive way. One can easily understand the theory. It is also explained why and how a presented concept is related to data science.

The last two weeks however are to shallow and abstract in the explanations. I had to check external websites to fully understand the material. The lectures also didn't prepare me good enough for the tests. Sometimes I felt lost and the video companions also didn't really help. This wasn't the case in the first two weeks. At the end I was able to complete all tests with 100% but only because I taught the material myself with the help of external websites.

創建者 Michael Q

Apr 07, 2017

Very rushed presentation. Blows right through a lot of fundamental concepts without a deep enough explanation or enough practice material (especially in the last two weeks). I feel like completing this class will require supplementation with better instruction.

創建者 Egor M

Jul 27, 2017

This course is very short. I've completed it in about 4 hours. Nothing was told about linear algebra, statistics, optimization. It is not enough even to learn Data Science.

創建者 silvia a t

Aug 06, 2019

Dear Professor,

Please improve your handwriting. Or at least prepare your materials using slides. It will help the students understand your information better.

創建者 Ashraf S

Jan 17, 2019

This course dos not contain enough examples which needed to train and practice ,PDF is not clear enough and does not contain any problems to practice.

Thanks

創建者 Vaibhav J

Feb 10, 2019

Found the title of the course mis-leading! School level Math skills are taught. Found the title to be similar to "click-baits"

創建者 Peter G

Mar 04, 2018

I enjoyed the first 2 weeks. Weeks 3 and 4 were harder to follow. Too few examples, particularly in week 4.

創建者 Numsap S

Mar 21, 2017

Too basic. Should give an example on how these math skills are used in data science.

創建者 Saurabh S

Jul 11, 2017

Week 1 and week 2 are good. rest of the weeks are very fast and not clear.

創建者 Jonathan H

Feb 08, 2017

Very basic course... probably won't teach you a lot of new things

創建者 A M A

Dec 24, 2017

Probability part is good others are elementary math