# Data Science Math Skills

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Rating | 4.3★ based on 422 ratings |
---|---|

Length | 5 weeks |

Effort | Four weeks, 3-5 hours per week. |

Starts | Oct 12 (last week) |

Cost | $49 |

From | Duke University, University of Geneva via Coursera |

Instructors | Daniel Egger, Paul Bendich, Tina Ambos, Gilbert Probst, Lea Stadtler, Bruce Jenks, Stephan Mergenthaler, Julian Fleet, Cassandra Quintanilla, Claudia Gonzalez Romo, Sebastian Buckup |

Download Videos | On all desktop and mobile devices |

Language | English |

Subjects | Data Science Business Mathematics |

Tags | Data Science Data Analysis Business Math And Logic Leadership And Management |

## Get a Reminder

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## What people are saying

**
math skills
**

great course to refresh your math skills in data science projects improve week 4 videos Este curso lo recomiendo mucho a quienes estén interesados en refrescar sus conocimientos de matemáticas para pasar a cursos de estadística o data science.

This course is designed for those either without a college level math background (calculus, probability, etc) and thus need an introduction to fundamental math skills or for those who need a refresher.

Especially is possible understand everything by video companion which explain math skills in practice exercises.

For those with stronger math skills than me, it's probably a fairly easy course.

I wanted to review the concepts presented in this course to get back on track with my math skills.

Probability part is good others are elementary math A good review of basic math skills, however I believed the "SUM RULE, CONDITIONAL PROBABILITY AND BAYES'THEOREM should be discussed much more in the last week module with more example and exercise.

Suited for anyone with 12th grade math skills.

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**
last week
**

But last week 4 tutorials are covered at very high level , it was quite difficult to understand probability topic without referring to other online tutorials.

It seemed the last week course was a bit rushed and could have been extended into few more classes.

great introduction and refresher to maths skills last week was very hard This course was very good.

Also the agenda is very simple in the first couple of weeks until it gets to the last week.

last week is not good at all i didn't get it all Good materials.

the very last week was toughbeginner of data science will not understand.

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**
data science math
**

A great fast introduction into Data Science Math Skills.

It had better if you design a Data Science Math Skills specialization.

Would definitely recommend this course as an introduction or refresher to Data Science Math Skills!

:) Great Primer for Data Science Math!

A tremendously useful primer on the fundamentals of data science math.

First of all thanks to the data science math skill because i learned many new things,ideas,knowledge and skills from this course and more thankful to professors because of them i am able to give all the answers and it was too much interesting to do .

Thanks to all the teams of coursera as well as to the data science math skill...... Learning this course I have gain many new and interesting skills.

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**
high school
**

Generally pretty good, a little slow and simple to start The first three weeks are pretty easy high school math.

A little bit easy if you have solid high school math background.

Really helpful in understanding terms in simple way Good to revise mathematical concepts I found it quite engaging and challenging at times to get my head back around high school maths!

Muy buen curso como base para el analisis de datos Highly recommended.. Easy and suitable for beginners with high school math skills.

Basic math course help to get revise all the high school concept.

Suitable for high school students.

I loved this class, the only one of it's kind and much needed, unless you particularly want to re-do your long forgotten high school and college math.

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**
bayes theorem
**

It reviews the basics of sets, plotting, sigma notation, derivatives, logarithms, mean and variance, Bayes theorem, etc.

Although, more emphasis could have been placed on industrial examples but still the course is a great start for anyone The Bayes theorem and binomial theorem needs more examples.

Started off easy but got a little tricky in the end with Bayes Theorem.

This course helped me a lot in better understanding Bayes Theorem.

But I had to search around various other resources before I got the hang of Bayes theorem.

Also a tree diagram approach to both conditional and Bayes theorem will help get to the understanding faster.

Very well explained with real time examples Decent course to refresh the skills in probabilities for Data science cases A bit more information regarding Bayes theorem, Examples and how to tackle them would be better.

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**
easy to understand
**

It will refresh your memory and maybe it will also have an impact on your music : p This is a good learning course and nice to go through Easy to understand.

Very easy to understand explained clearly by the professors.

Very important Review course Very easy to understand and know how to succeed in the future on the learning path.

very good lecture and easy so very easy to understand the course Very nicely explained.

I would have appreciated some more in-depth explanation of the last week classes More practice problems would have made the course easier great sometime the lecture is too easy to understand and after some week it goes too hard to understand even it not a hard thing but sometime the lecturer make it so hard so it can make confuse.

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## Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

AD, Data Science $47k

Associate Data Science Supervisor $55k

Science writer / data analyst $63k

Genomic Data Science Programmer $75k

Volunteer Director of Data Science $78k

Expert Data Science Supervisor $79k

Supervisor 1 Data Science Supervisor $91k

Guest Director of Data Science $101k

Data Science Architect $105k

Head of Data Science $131k

Assistant Director 1 of Data Science $133k

Owner Director of Data Science $149k

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Rating | 4.3★ based on 422 ratings |
---|---|

Length | 5 weeks |

Effort | Four weeks, 3-5 hours per week. |

Starts | Oct 12 (last week) |

Cost | $49 |

From | Duke University, University of Geneva via Coursera |

Instructors | Daniel Egger, Paul Bendich, Tina Ambos, Gilbert Probst, Lea Stadtler, Bruce Jenks, Stephan Mergenthaler, Julian Fleet, Cassandra Quintanilla, Claudia Gonzalez Romo, Sebastian Buckup |

Download Videos | On all desktop and mobile devices |

Language | English |

Subjects | Data Science Business Mathematics |

Tags | Data Science Data Analysis Business Math And Logic Leadership And Management |

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