4.5
10,222 個評分
2,282 條評論

## 課程概述

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!...

## 熱門審閱

VC

2020年5月16日

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)

RS

2020年5月5日

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!!

## 2151 - Data Science Math Skills 的 2175 個評論（共 2,281 個）

2021年1月11日

Explicações rápidas e não 100% claras e exatas. Exemplos um tanto quanto toscos

2020年12月23日

The course is not explained as I was expecting. Graded question is very tough.

2020年7月14日

A good platform for online study material and best experience teaching methods

2020年6月10日

Take this course as a review but not to learn the data science skills needed

2022年2月11日

Solid theory with distracting metaphores and use of non-mainstream symbols.

2020年11月16日

first half of course explained very well; not so for second half of course.

2017年3月1日

The material is very useful, however, the second teacher is not the best...

2021年9月25日

Week 2, Week 3, week 4 topics were not explained with enough examples.

2020年9月6日

Week 4 is poorly designed. Concepts are not clear. Please redesign it.

2020年9月8日

Course lacks many details especially differentiation and integration

2021年1月28日

Very basic course. Need some more challenging and advanced theory.

2020年5月10日

The course topics were good. But the video lectures were not good.

2021年7月4日

T​he first two weeks are great, the rest deteriorates in quality.

2020年5月7日

probability was not taken properly and many portions are missing

2018年1月26日

It is more of an "appetizer" than a whole self-contained "meal".

2020年7月29日

is a good class but is hard for me I will try again but not now

2020年5月7日

The probability part (Week 4) was disastrous. But rest is okay.

2022年5月16日

Can be confusing. Totally not for a beginner on my opinion.

2020年7月1日

The last section is much harder than the previous sections.

2020年4月17日

it required more content for data science mathematics

2017年5月30日

The contents were ok, but the videos were a bit boring

2018年2月24日

Lost if it is a basic or a intermediate level course.

2017年12月27日

The materials could be more advanced for data science

2020年5月1日

This require more practical refernce to data science

2020年6月5日

The probability part explanation is very confusing.