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!...

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)

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.

筛选依据：

创建者 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.

创建者 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.

创建者 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

创建者 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"

创建者 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.

创建者 Numsap S

•Mar 21, 2017

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

创建者 Saurabh S

•Jul 11, 2017

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

创建者 Jonathan H

•Feb 08, 2017

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

创建者 A M A

•Dec 24, 2017

Probability part is good others are elementary math

创建者 Derek S

•Mar 30, 2018

last week was very hard

创建者 Luis A C G

•May 05, 2017

I deeply regret having paid for this course. Nothing in it was oriented to data science and weeks 3 y 4 are specially weak in contents on basics on calculus and probability. Not bad if you just want to remember some things from upper secondary maths but definetly not worth to pay for it.

创建者 Austin S

•Jan 07, 2019

Silly course. Either you know so much math to be able to pass this course or you know nothing to find this course of zero value. Avoid.

创建者 ChunChieh L

•Sep 20, 2019

一些非常基礎的高中數學，而且不完整。

課程一開始還會講解得比較細部，後面愈跳愈多。

對於有數學基礎的人來說根本不用浪費時間，對於沒有數學基礎的人來說，看了也沒辦法真的學到多少東西。

创建者 Miguel P M

•Jul 03, 2019

Extremely Basic. Not useful for DataScience in my oppinion.

创建者 geary b

•Dec 11, 2017

TRASH!