# Data Science Math Skills

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Rating | 4.3★ based on 280 ratings |
---|---|

Length | 5 weeks |

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

Starts | Mar 4 (next 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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## What people are saying

We analyzed reviews for this course to surface learners' thoughts about it

**math skills**
in 19 reviews

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.

It is a gentle introduction to basic math skills that everyone should have.

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.

**high school**
in 8 reviews

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.

cannot believe I took a programming course without doing this - the math was taking me so long and it was because I hadn't finished high school math a decade ago (our school didn't require it) - really thankful to have found this course!

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.

**last week**
in 8 reviews

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.

The last week is very informative and helpful.

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.

**data science math**
in 6 reviews

A great fast introduction into Data Science Math Skills.

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

I am studying at the Data Science Math Skills to fill some math gaps, and also doing the Data Scientist Toolbox .

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.

**practice quizzes**
in 4 reviews

Thank you for the detailed guidance in the practice quizzes on incorrect answers as well!

The readings should also explain very weel what I'm doing and why I'm doing each step, and in the end explain the exercise as a whole.The practice quizzes should bring very real life examples (as thouse of VBS tests) and they have to match de guide text.The videos should be made only from the most comum doubts and mistakes in the practice quizzes.

Video companions and practice quizzes complement the lectures in an effective way and prepare the student well for the graded quizzes.

The explanations on the practice quizzes also fail in many cases to thoroughly explain why an answer is correct.

**two weeks**
in 3 reviews

The first two weeks were well paced, in week 3 I think too much is covered too quickly and in week 4 there is a further acceleration.

Overall good refresh The first two weeks are good.

It is also explained why and how a presented concept is related to data science.The last two weeks however are to shallow and abstract in the explanations.

This wasn't the case in the first two weeks.

Blows right through a lot of fundamental concepts without a deep enough explanation or enough practice material (especially in the last two weeks).

## 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 280 ratings |
---|---|

Length | 5 weeks |

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

Starts | Mar 4 (next 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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