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Learner Reviews & Feedback for Data Science Math Skills by Duke University

4.5
stars
11,647 ratings

About the Course

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!...

Top reviews

AS

Jan 11, 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

Sep 22, 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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2351 - 2375 of 2,586 Reviews for Data Science Math Skills

By Guo L

•

Nov 1, 2023

From week 3, students can only acquire a meagre amount of knowledge from the lecture video. As a result, extra learning from youtube videos and other materials is needed to pass the exams. Not recommended unless you only want the certificate.

By Gregory B

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May 5, 2020

The course has issues later on - there is inconsistent notation, no provided worksheets, formula sheets, documentation, or summaries of any kind. At first this isn't a problem when the course is simple but is much more problematic later on.

By Saeed M

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Sep 2, 2020

As a whole: very useful review of the themes.

However:

Quite a few Latex statements spread around the quizes.

Probability could be explained a bit more thoroughly. I had to look up external sources to get a better grasp of the subject matter.

By Andre G

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Jan 13, 2021

Sometimes a little bit confusing due to the handwritings. In addition I would not expect a wrong statements/mistake in 10 minute video, sure the corrections are fine, but I would recommend to record the video again without any mistakes.

By Celtikill

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Oct 11, 2020

Challenging module which lacks the practical application needed to feel confident going into quizzes. I found working through the quizzes themselves more valuable than the reading material. Plan to watch, rewatch, and take good notes.

By Georgina M

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Aug 2, 2020

Some really good course content, but a strange mix of levels/difficulty. I have some maths background so skipped most of the videos but using the notes and quizzes I still learned some new topics (like set notation and Bayes theorem).

By Matheus D M

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Nov 9, 2022

O curso foi bom mas as explicações dos módulos 3 e 4 foram muito corridas e não foram suficientes para uma boa base para a realização dos exercícios. Os exercícios estavam muito mais complicados do que a explicação teórica.

By sudip t

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Jul 12, 2020

It was nothing new and easy for me but if you have a gap in your study and forgot whatever you had studied in your school then this course is definitely for you to learn some math skills which is important for data science.

By Andrea P

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Dec 22, 2017

A good review of basic math skills, however I believed the "SUM RULE, CONDITIONAL PROBABILITY AND BAYES'THEOREM should be discussed much more in the last week module with more example and exercise. The 1,2,3 week are great.

By MJ A

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Jul 12, 2020

The first 2 parts(weeks) were good and easy to grasp, but the last two were a bit advanced and needed more time to handle the concepts, but overall a good course in general, get more more practice before attempting quiz

By Bobby

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Jul 19, 2020

The early videos are good. The videos toward the end were not as helpful for a person new to the subject. I had to look up other tutorials on youtube to understand the material enough to pass the quiz and final test.

By Michael L

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Oct 18, 2017

I feel the probability portion of the course was too quick for the material covered. Yet the quizzes for the probability section were very demanding. It was difficult to successfully complete the probability quizzes.

By Jason C

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Aug 3, 2020

The class starts off very well. When it moves to the full professor, the lesson quality falls, as the lectures lack the younger professor's examples and explanations which guided the learner into the quiz material.

By Siyabonga F C

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Feb 3, 2021

Had trouble with week four, I think the instructor tried to summarize Bayes theorem, but it made it vague, had to watch other videos from other sources to fully understand the concept. Otherwise it was good.

By theo

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Apr 24, 2020

The first three weeks are well explained, the last week is the most difficult and the professor does not provide examples. There are many mistakes in the quizzes and this seems to be done very haphazardly.

By Mei Y

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Aug 27, 2017

Broad coverage of topics in a compact course. Useful for those looking for a refresher course. Could be improved by explaining where in data science the chosen topics would be relevant to provide context.

By Supasuk L

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Aug 9, 2020

The part about baye's theprem is really hard to grasp, perhaps less equation and more diagram would be better for student to understand the concept. (for me I look at youtube for better understanding)

By vignaux

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Nov 2, 2020

The course is well but the last part of the course is boring because the principal interest of the course is data analyst with explanation of smart theorem as Baye's and this is not very well explain

By Karen B

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Mar 25, 2021

The first sections are very good. Nice review and learned some new concepts (or at least was a refresh). The probability section is a tad weak. Could use more explanation and more examples.

By Patricia C

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Jan 26, 2021

Good basic review, but I would have liked more examples. (Examples did not have to be in video format, but perhaps in supplementary material.) Very much appreciated that this was offered.

By Margaret C

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Nov 23, 2020

The course is really good until you get to the older professor. He doesn't explain the material as thoroughly and is lacking in enough examples to help you understand how to do the quizzes.

By Mariam I

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Dec 27, 2020

The second half of the course about probability theory was not explained thoroughly. Professor Daniel Egger rushed through it and did not spend much time explaining appropriate examples.

By Marcus C

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Nov 4, 2020

Covered the basics well, but I really struggled with the probability section and felt it could/should have been split into more sections with more examples to demonstrate the concepts.

By Luci S

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Mar 25, 2022

The course in Week 1 and 2 are quite interesting but gets challenging to the rest of the following weeks. Unfortunately it doesn't support with simple explanation by the lecturers.

By Mohammed K A

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Aug 29, 2022

Appreciate that content if free

but last part is worest part, i done first 3 week in almost 1 day and last part take along time due to ambiguous information and hard of examples