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

4.5
stars
12,850 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

AP

Apr 17, 2020

Hi it is very helpful to me. Concept is properly explained. I enjoyed learning process. Expect some more courses on data science as well as on python which involves real time application.Thanks a lot.

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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2526 - 2550 of 2,852 Reviews for Data Science Math Skills

By MUXAMMAD Q J O

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Feb 20, 2025

Good

By Prashant M K

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Dec 9, 2020

good

By Prerna S

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Jul 10, 2020

good

By Nagraj t

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Jul 6, 2020

good

By Nishant S

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Jul 4, 2020

nice

By Perumali S

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Jun 27, 2020

good

By LIJIN W

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Jun 16, 2020

Nice

By AVINASH K

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Jun 9, 2020

Nice

By AVALA S

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Jun 2, 2020

Good

By Sunil R

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Jun 1, 2020

Gooo

By Mitesh M

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May 13, 2020

good

By TEJASWINI D

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May 7, 2020

GOOD

By Prateek G

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May 4, 2020

good

By SREERAM S

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Apr 10, 2020

good

By Mohammad M A

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Dec 9, 2022

4.5

By 17_KV D

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Oct 2, 2024

..

By MA'MURA Z R

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Sep 16, 2023

YA

By Abanoub S M

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Nov 8, 2021

4

By Fariha A S

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Sep 1, 2021

4

By Sakshi B

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Jul 22, 2021

i

By Jay Y

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May 7, 2020

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By Amin A

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Sep 4, 2019

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By Nagaraj A M

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May 9, 2019

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By nikhil k

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Feb 15, 2021

well the initial part of the course is very basic .. too basic.. the course doesn't scale up with the first 3 weeks. but the instructor is clear and makes concept easy to understand. but things change in the fourth week. the course shifts gears from very easy to intermediate. without much guidance.. or examples

math skills are to be practiced i hope the instructors level out the difficulty level in the course. An ideal math course should have many examples to practice with constant involvement of the instructors , support guys.

just 5 or 10 problems doesn't get you much and when asked to pass with 80% is tough.

. i left the course in week 4 and again completed after i understood week 4 content better.

this course doesn't have many many of the math skills required for data science.

ideally a math course should scale up from simple easy to intermediate to hard and later challenging questions and final assignment should have a combination of first 3 and passing of 70 to 75..

so the course requires

a. more examples for practice with solutions atleast 20 -25 both solved and unsolved

b. more math topics to make it more comprehensive for data science

c. more involvement overall