Retour à Data Science Math Skills

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

4.5
étoiles
10,458 évaluations

## 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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## 1901 - 1925 sur 2,336 Avis pour Data Science Math Skills

par ayush y

12 avr. 2020

It provides very good knowledge and in very easy way

par Santiago G B

2 juil. 2020

Grat lessons but lack of examples during de videos.

par Phyu W N

3 oct. 2020

This class give me a lot of thinking of Math skill

par Gloria P

28 juil. 2020

Very engaging instruction and interesting content.

par Kaue C C

9 mai 2020

Great introductory course for people outside STEM.

par Mohammed M

5 mai 2020

thx you i have learned alote from this Coutsera...

par Claudia R R

17 avr. 2020

Good course. But course material could be improved

par fabio J

21 sept. 2019

Great introduction / refresh to math & probability

par Jaime M

16 mai 2017

Muy buen curso como base para el analisis de datos

par Ali N

13 avr. 2020

Overall, the course was good, but I expected more

par Anurag K V

2 févr. 2022

It was a good experience...I enjoyed it totally.

par Gilmar N

28 mai 2020

It lacks some more problem solving explanations.

par SACHIN N P

19 mai 2020

this course is useful to get the logic of maths.

par James M

31 mars 2018

great introduction and refresher to maths skills

par MYO T H

24 mai 2020

Probability Course is difficult to understand

par Joshua C

29 août 2017

The first three chapters are relatively easy.

par John R

18 juil. 2022

the probability section was kinda ridiculous

par Subrata M

5 déc. 2021

Probability part is not very well explained.

par Odhiambo N

25 nov. 2021

It was awesome learning from Duke University

1 juil. 2021

The explanation is kind of hard to uderstand

par Ro

6 déc. 2020

Long but very informative course to work on!

par Anshul v

30 mai 2020

interesting and learning more inside course.

par Lei H

27 avr. 2020

Good examples and illustration. Good tests.

par Belen R R

26 oct. 2019

A veces las explicaciones no son tan claras