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

8,907 ratings
2,020 reviews

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

May 5, 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!!

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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1426 - 1450 of 1,999 Reviews for Data Science Math Skills

By Hauwa A

Oct 24, 2017

Week 4 gets confusing as more examples could help explain concepts. Otherwise overall course is great for basic math skills for Data Science

By Gary H

Jun 4, 2018

Introduction to some basic mathematical terms. Conducted in an easy-understand way. Not very clear explanation in the part of probabilities.

By Ysa G

Jun 19, 2020

Great for those who need to review their fundamentals. Helpful for people like me who already forgot about derivatives, probabilities, etc.

By Aldiyar S

Nov 18, 2020

The last part, Theory of probabilities was very unclear, with not correct explanaitions. Needed another sources to learn. Other parts good

By Phoebe ( Z

Feb 6, 2018

the material could be a little more challenging. If more vector/matrix material is introduced would be more helpful to the data scientists

By Suriya S

Jun 9, 2020

This course helps me to learn basic math of Data Science very effectively but more math skills are required other than this maths skills!

By Mai D T C

Jan 7, 2021

It's a very nice course, but please could you provide answer keys to the final quizz? It would be very helpful to reinforce our learning


Jun 12, 2020

The last part of the course, week 4 is a bit short on example, more examples on how to calculate conditional probabilty will be helpful.

By Su M

May 11, 2017

Easy and suitable for beginners with high school math skills. If how the equations are deduced are introduced, it would be even better.

By Wouter S

Jul 3, 2020

Good video course and quizzes. Not too long.

Only remark is the code in the quizzes, sometimes fractions and formula's are not visible.

By Duc T

May 31, 2017

Good materials. But examples from learning sections are a lot easier than problems in quizzes. More difficult examples would be great.

By Giulio P

Oct 21, 2020

The probability part is not as clear as the previous ones with quizzes less easily solvable with the information given in the lecture

By vignesh a

Sep 14, 2019

Week 1 - 5/5

Week 2 - 4/5

Week 3 - 3/5

Week 4 - 4/5

A good introductory course which could do better in explaining some concepts clearly.

By Abdulrahman M

Sep 8, 2019

Every data scientist needs to know some statistics and probability theory. The amount of math you'll need depends on the role.


By Luan d S

Dec 30, 2018

It's a excellent course with all of the basic concepts to start in the Data Science World.

Great regards and thank you for the course.

By Vidya C

May 24, 2017

It was a good course for a beginner.But I think the proportion of the course allocated to probability is much less than it deserve.:)

By Hichem D

Jul 13, 2020

it's a good choice for anyone who wants to refresh or enforce his knowledge, even to discover what math skills a data scientist need

By Atsushi T

Sep 14, 2017

Very comprehensive and challenging - I feel a final quiz would be great but individually, very well though out content and questions

By Rinav S

May 19, 2020

The course offers a good insight to the maths skills utilized in Data Science. WIll be looking to program all concepts learnt here.

By Ricardo V

May 23, 2020

A very great course for begginners but it would be great if the math skills could be elaborated closer to the real world examples.

By Priyadarshini S

Oct 25, 2020


By Dyandra P W

Sep 16, 2020

Although I've already learned these but I had so much fun reviewing and gaining new insights on the materials! Thank you so much!

By Mohd F E B A

May 9, 2019

Though that this course was too simple but actually it is quite an eye opening and refreshing for the last two topics. Well done!

By Rohit

May 24, 2020

This is a interesting course . I learned lot of new things from this course .This course will be beneficial for me for my future

By Juan N R A

May 4, 2020

Me gustarían mas ejemplos en el tema de probabilidades. Bayesian y Binomial.

Gracias por la dedicación y compartir conocimiento