Retour à Data Science Math Skills

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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!...

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!

RS

5 mai 2020

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.

Thank you for a great class!!

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par Omar S

•30 mars 2020

very beginner misleading title, not a significant knowledge in Math for DS!

par Simon

•15 avr. 2020

I took this course as a refresher for maths rules I had seen in university, so my experience does not relate to those who will encounter this topics for the first time. I honestly do not recommend this course to beginners with no background in mathematics, because there is not much space given to theoretical explanations of the principles here. The course feels more like a quick slideshow of rules (logaritmic manipulations, exponential manipulations, conditional probability manipulation) followed by exercises to practice them. This is a stark contrast to my University Experience, where mathematics is about principles, logic and the demonstrations that underly theorems and their validity (e.g. ''why is the formula for combinations of "m draws on a set of n" built this way?''): deep study of these topics provides students with the mental skills to build a MODEL of whatever they come to face in real life. You may forget the rules, but those can be freshened up by a course like this. To build Logical and thinking skills instead requires a deeper understanding of mathematics and its underlying principles. I hope the professor who recorded this course will look forward to an opportunity to devise a deeper mathematics course to tackle these topics. Good luck to all fellow learners and thanks to Coursera for this opportunity!

par Numsap S

•21 mars 2017

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

par Shawn T R

•8 août 2018

Overall the course was great. Shored up my knowledge on a number of subjects. Could have used better explanations of certain topics though. There were a couple of instances where the instructors simply show you how to use a particular equation to deal with a particular kind of problem without explaining why it works. For concepts to really sink in you need to get a deep intuition for how they operate. I think these intuitions were provided for the main concepts, but there were some sub-concepts, where they essentially just gave you the method without getting into how it works. It's fine to know how to use it, but without a deep intuition it's just memorization.

Overall I really liked this course though. The quizzes and tests were challenging and fun and I came away with a much better knowledge of the subject matter, especially probability. I'm not a scientist, so I can't really speak to whether this will be all I need, but it was certainly helpful in the data science concepts I'be been exposed to so far.

par Benjamin L

•26 avr. 2017

A tremendously useful primer on the fundamentals of data science math. This course is a particularly good option for individuals who have seen some amount of calculus and algebra but haven't used those methods in a long while and need to review. Thorough, easy-to-understand material.

I would suggest to the course facilitators that they develop the provided lecture notes -- already a useful tool -- into a full-fledged text. I'm not suggesting something much longer than what they already have, but simply taking that document and adding a bit more rich content. While the notes are useful for more carefully studying the math being done in the lectures, even a bit of effort putting some pedagogy into the notes and combining them into a single document (which I did for the sake of printing) would create a hugely valuable resource.

par Richard C

•3 juin 2022

I have some mixed feelings about the course. I think the course is both too easy and too hard in different ways that pull against each other. The first two weeks are basically just an introduction to set theory and some really basic high school level algebra. It was too easy to the point of being almost absurd. At the third week, the logarithm unit was more difficult. While this is also high school math, a lot of people don't use it regularly and may find that the video lectures weren't super helpful in explaining (or refreshing) this material. The quiz questions seemed much harder than any of the examples discussed in the videos. The fourth week covered probability, which is something that I felt like I knew fairly well going into this. The quality of the lectures in the fourth week is lower. Too many different formulae are tossed at the audience without a lot of explanation and very few examples are given. Once again, the quiz questions were way more difficult than the examples used in the lecture.

There are tons of mistakes in the lectures, with a lot of popup boxes added in later on to correct them. But honestly, there are enough that these lectures should just be rerecorded. Write out decent scripts, work out the problems in advance, and remove the errors.

I would like to see more discussions about the math itself and how it is applied in data science.

par Susmito R

•13 juil. 2019

The first two weeks of the course were great! The instructor was very clear in his explanations and made the material very intuitive. The video companion pdf's were also very well written. But from the third week onward, when the other instructor took over, not only did the explanations suffer significantly, the video companion material also ceased to be of much help. He did not explain any of the intuition behind any of the formulas and he didn't even try to explain the intuition behind when and where the formulas would apply. I didn't take this course just to be given a bunch of formulas. I really wanted to understand the material because I knew these are foundational concepts that needed to be mastered. Khan Academy explains a lot of the material of weeks 3 and 4 much better. I really wish someone had explained how the version of the binomial theorem that was presented in this course is related to the traditional version that we learned in school while doing binomial expansions in algebra.

par Lauren D

•1 nov. 2020

I felt like this course was just okay, especially when compared with the Intro to Calculus course from the University of Sydney. The problem I had was that the quizzes often required applying more difficult scenarios than the examples in the video explained. I think it should be the other way around - videos should prepare you to solve difficult problems, but quizzes should not present scenarios that the student has not had experience with. It would've been helpful if they had provided notes to go along with the examples. I also felt like equal amounts of time were spent on very easy concepts and difficult, complicated concepts. I don't need an entire video on calculating the slope of a line, but I do probably need more background on all of these probability scenarios and how they play out in real life situations. I found on the Week 4 quiz I was just scratching my head and trying random formulas.

par Kartik S

•25 mai 2020

WEEK 4 needs to be covered with more examples and more clarity.

par Stephane F

•19 juin 2020

Don't waste your time with this. The first 3 weeks are insulting, teaching you basic highschool math (like what is "<", what is a function, etc.). The last week is more interesting as it gets to probabilities, and the quizzes are fun.

Reading materials are given and completely remove the need to look at the videos (pure waste of time). Formulas are given without any rigour.

par ChunChieh L

•20 sept. 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。

par Kostas H

•9 mars 2018

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. This is not a course that teaches data science, nor the math of data science (linear algebra, random processes, algorithms, etc). But rather it teaches the math behind the math of data science. It reviews the basics of sets, plotting, sigma notation, derivatives, logarithms, mean and variance, Bayes theorem, etc. It is a gentle introduction to basic math skills that everyone should have. This is a course to definitely take as a refresher or before venturing into more higher topics such as collegiate math, data analysis, machine learning, computer science, engineering, etc.

par srijana p

•31 janv. 2019

I learned many new things, ideas, knowledge,and skills from this course.I am very much thankful to both professors for teaching about all of those interesting lessons,providing many more things. now, I am able to give all of the answers frequently which I learned from this beneficial course.

par manivel s

•23 avr. 2020

The course has limited resource to understand. This course content is not sufficient to understand the topics. But that is fine as we come to know what we need to learn .Then , We have to put additional effort to understand the topics in external sources like textbooks, Internet, youtube.

par Miriam C

•23 juil. 2017

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!

par Abhishek R

•7 nov. 2019

Every part covered gives a good introduction to the world of data science as intended. However in my personal opinion the part on probability was covered a bit hastily, though the quizzes will force you get some in-depth understanding

par Ned T

•11 sept. 2017

This is a very interesting course for those who have not used math for many years and now want to pursue the field of data science. The basic concepts are presented coherently and understandably attracted me throughout the course.

par Sehresh M

•12 avr. 2020

It is really good data science math course, all described topics are highly important to know for everyone who need to know about data science. if anyone want to know about Data Science I will recommend to join this course.

par Lingde K

•7 mars 2019

As a non-native speaker, the first three parts are helpful in getting into math terminologies and reviewing basic math knowledges. The essence is all about the last part, which might be a little tough for new learners I guess.

par Jessica J

•16 déc. 2017

A great refresher course and a range of interesting and foundational concepts. Would recommend to anyone who has prior experience with calculus and probability theory and is just looking to remind themselves of key concepts.

par Jhon R

•2 mai 2020

Great option to get back to the Math worked, reviewing the basics of what needs to be known when working on data science and see where you need to put more effort. Hoping this helps while I continue taking other DS courses.

par Deleted A

•13 févr. 2020

It helped me reviewing and learning interesting mathematical points that will help me understand more about my Machine Learning course.

I believe the last week, about probability. could be more extensive and made more clear.

par Angelica D

•17 févr. 2020

This was a great beginner course on some of the math you might see in Data Science. I'd recommend this course to anyone that might not be confident in math who want to start a career in this field. A great refresher!

par Jayson S

•4 mars 2018

Fantastic course, especially when paired with or done before Andrew Ng's Machine Learning course as it matches up quite well! Thank you for the detailed guidance in the practice quizzes on incorrect answers as well!

par Zhenqing H

•17 déc. 2017

This course gives me the basic conceptions about the mathematics, especially parts about calculus and possibilities, however, if would be great if there are samples or basic practices related with the data science.

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