Data Science Math Skills(으)로 돌아가기

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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!...

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.

필터링 기준:

교육 기관: Robyn J

•Feb 08, 2017

This course is very strong with respect to presenting the concepts you need to know for data science. It is extremely WEAK in terms explaining those concepts. If you are like me and did this kind of math back in the 70's and 80's but have not used it since, be prepared to seek sources outside Coursera in order to understand the material and pass the quizzes. The instructors leave out explanations and skip important points leaving you confused about the concept.

Example: In the Permutations and Combinations sections, "results" of calculations are thrown at you with no explanation of how the instructor got the answer. 10 minutes later, totally as an aside, you get the explanation. The course is not taught in such a way that A leads to B, B leads to C, and C leads.....; instead the instructor will tell you about C, might explain A, and forget about mentioning B until the graded quiz. That is why you will need to fill in the gaps using websites like betterexplained.com or kahnacademy.com.

The student is better served by looking at the syllabus and then going to either of those sites - where the explanations are worth your time.

In addition to failing to present steps in a logical order, the course often teaches at an extremely basic level but tests at a much, much higher level. Again, to get to the higher level of understanding needed to pass ANY of the required, graded quizzes, the student will need to heavily utilize outside sources. The explanations on the practice quizzes also fail in many cases to thoroughly explain why an answer is correct.

Then there are the issues with Coursera itself, the course navigation using Chrome is quite bad. If I did not constantly monitor what part of a course I should be in versus what part of the course automatically loaded next, I often found myself taking a quiz for which no lectures had been presented. The TA's response to my complaint was flippant and WRONG. She then closed my question and I could not respond or ask for more details.

If I had it to do over again I would invest my time and money somewhere else. In my opinion, Coursera should rescind the instructors' rights to charge for this course until the instructors improve and meet higher teaching standards.

교육 기관: Roberto S

•Jul 24, 2017

For newbies, the set theory, real numbers, basic statistics and so on are quite well explained. The intro to probability, however, is shallow and quite confusing. It lacks some real-life examples to offer a better grasp of the theory. Coins and dices examples are a good start, but made up examples without a real base are not clarifying at all.

교육 기관: Danuel R

•Apr 01, 2017

Difficult content not explained well by the presenters.

교육 기관: Marcel S

•Apr 30, 2017

Week one starts with interesting material that relates probability to data science. Unfortunately as the course progress the course material and videos become less and less helpful. Ultimately the student has to visit other web sites and youtube to actual learn the expected material. The course notes are next to useless and the video are equally unhelpful. I am sure the teachers know their stuff but they have no idea on teaching it clearly based on the material presented in this course. Avoid this course, and head over to KhanAcedemy and complete their probability and statistics program and you will actually learn all the material in this course with a ton of examples and top class videos.

교육 기관: Mikhail G

•Mar 28, 2018

Please include integration, algorithm analysis (big O, theta, omega), recursion and induction. Your course is helpful, thank you. If you add those things I've mentioned it would be absolute gold.

교육 기관: Karl W P

•Feb 05, 2017

I graduated with a BS in computer science and mathematics 15 years ago. Since then I've been working as a Business Intelligence consultant and I've recently decided to look into the field of Data Science. I was looking for a math class to refresh my math skills rather than start from scratch and this was the perfect course for that. Kudos to Daniel Egger for creating the class. It saved my a lot of time.

교육 기관: Danny N

•Nov 18, 2019

I thought this course was a nice refresher on basic mathematical concepts and it introduced me to set theory and probability very well! I think I am better prepared for data science afterward!

교육 기관: Mario B H B

•Feb 06, 2020

Excelent course to begin!

교육 기관: Jason B

•Feb 13, 2020

The first two sections of the course are well designed making it easy and clear to understand and do the practice quizzes and graded quizzes. The third section is a sign of trouble, but doable. The lack of notes giving definitions and clear formulas along with bare minimum examples in the videos make the practice and graded quizzes feel like the questions came from someone trying to trick you at every turn. By section four, it was very annoying to need to learn the concepts on Khan Academy to pass the graded test.

교육 기관: Shawn T R

•Aug 08, 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.

교육 기관: Mahnaz K

•Oct 22, 2019

The course overall was great. So much math was covered that it is hard to believe it was all done in 4-5 weeks required to complete the course. It was well taught-- very relevant and clear for the most part. I have had all this math in the past so I had a frame of reference but without it I think it would be hard to follow. Having said that, I found the Probability lectures hard to follow. It seemed you need to know a lot of probability theory beforehand. Also the videos were too short in this sections and went very fast. The videos need to be 20-25 minutes with more examples. The quizzes in this section were the hardest because not many examples were given in the lecture. Overall though I feel accomplished and feel I can tackle the math that comes my way when I pursue my data science degree. Thank you for putting together a course with the background math needed for data science.

교육 기관: Benjamin L

•Apr 26, 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.

교육 기관: Kostas H

•Mar 09, 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.

교육 기관: Jennifer D C

•Apr 06, 2020

The course is a prerequisite for a Data Science course and its aim is to empowering your math skill :) With this purpouse, the course covers important mathematical topics; it can be used for advanced learners as a revision and a recap of fundamental subjects but someone may find it a little bit boring. I love maths so I really had enjoyed the course. For me, the last part (about probability, Bayes theorems, etc...) was the more challenging and interesting but I had also appreciated the first part with the funny explanations of prof. Bendich :) I think that the pdf companions are really useful to follow the lessons better.

교육 기관: Mariana E

•Apr 08, 2018

Este curso lo recomiendo mucho a quienes estén interesados en refrescar sus conocimientos de matemáticas para pasar a cursos de estadística o data science. Es muy compacto por lo que los temas se tratan de manera concisa, pero realmente se avanza si se invierte el tiempo necesario. Yo estoy interesada en la estadística y mi campo es la lingüística, así que me tocó trabajar muchas horas haciendo cuentas en el papel y en la calculadora, buscando cómo hacer para sacar las distribuciones binomiales y las funciones básicas, pero me pareció al final que he dado grandes avances, me encantan las matemáticas.

교육 기관: Laurent B

•Jun 19, 2017

While most of material is well known, it is presented in a great way, so it is a clean and smart refresher for Sets, basic Algebra and notations, Cartesian geometry and functions, and derivatives. I knew the material about logarithms, exponentials and probabilities, but I felt that I knew it better in the end of this courses. Material is great, and teachers are very clear. I wish they came with more material about calculus (matrices), vector spaces, Lagrangian, Hessian and so on, which are also really interesting in Data Sciences.

교육 기관: Aditya K

•Sep 21, 2017

This course offers a great refresher of the FUNDAMENTALS of Linear Algebra , Calculus and Probability.

Do note the strong emphasis on fundamentals.

All lectures are well produced and the material put forward in an unambiguous and layman language.

The concepts presented are very easy to grasp , all thanks to the brilliant efforts of professor Bendich and professor Egger.

This course , along with another course on Calculus would serve as a great starting point for all data science enthusiasts and I strongly recommend it to everyone.

교육 기관: Baskaran V

•Jan 15, 2017

One of the best course, i have ever learnt. Even though i have been learning the Data Science for the last few years, i had no idea how the algorithms are working in technical. Which i was always skeptical. But honestly, now i am able to get things really faster than before. I am very happy, i have joined this course. Thank you so much for coursera to bring this course and importantly thank you so much for the professors to explain things in an easy for the people to understand. God bless you both and your family.

교육 기관: Adler A

•Apr 08, 2020

I totally liked this course for clear explanations and plenty of practical exercises. Some of them were not easy for me, but thanks to comments after mistakes I could finally find the right solutions. Just in the last week in the final quiz, there were no explanations, but maybe it was a problem on the site side. This course made me think a lot, and I enjoyed it genuinely.

교육 기관: Murali M A

•Aug 27, 2017

Succinct explanation of the basics. Take more time at the Bayes theorem. It is worth it. Work out all the problems and keep reading the PDF notes accompanied with the videos. All in all, a great experience for those who have missed some basic math in earlier education. I am onward to my next course in machine learning and data science. Cheers

교육 기관: Gitashah

•Jan 31, 2019

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......

교육 기관: Garth Z

•Mar 11, 2017

If you are a right-brainer and/or rusty on math, I strongly recommend this course as a precursor to Duke's Intro to Probability and Data course. Some of the practice and final quiz questions really threw me (and that's good)... Most of them I was able to rethink and derive the correct answer and a few others remain a mystery... :-)

교육 기관: Deleted A

•Jan 22, 2017

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. It was nice seeing a Venn diagram again. I did have to supplement some of the material that was covered quickly with google searches, but filling in the blanks was quick and easy.

교육 기관: Ankur A

•Apr 18, 2018

Hi. A very good refresher course that serves as a pre-requisite to Machine Learning and Data Science courses. Probability could have been a little better explained, specially the processes and event part. I would also like to see Vectors and Matrices added to this course, which is equally vital for Data Science.

교육 기관: Preeti A

•Jan 31, 2019

Learning this course I have gain many new and interesting skills. I am very much glad to get the knowledge from two professors and they gives me more knowledge on those interesting courses. I was able to do the answers of the given courses.And I THANKS them to give me such opportunity to do these courses.