ZurÃ¼ck zu Data Science Math Skills

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10,921 Bewertungen

Data science courses contain mathâ€”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 Bayesâ€™ 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. Sep. 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!

AS

11. Jan. 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)

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von Subramanian N

â€¢20. Aug. 2017

This was an excellent review of the basic mathematical concepts useful in data science and machine learning. Thank you very much for the very concise and clear explanations of the various topics! Much appreciated!

von Frank G

â€¢29. Mai 2017

very nice course, and a good starting point to catch up data science and computer science math-skills. Helps to bring some of those rusty concepts back into memory, and from there you can expand further ...

von Aniket P P

â€¢16. Apr. 2020

Hi it is very helpful to me. Concept is properly explained. I enjoyed learning process. Expect some more courses on data science as well as on python which involves real time application.

Thanks a lot.

von John V

â€¢11. Dez. 2019

First 3 weeks were easy going and the last week was a bit more challenging. I think more examples could be included in the lectures to understand Bayes' Theorem at the most fundamental level.

von SHANTANU R

â€¢28. Apr. 2020

It was a very good opportunity to go through the course, and the content was good. I can say I definitely learnt a lot in this course. Thanks team and kudos to great work you guys are doing.

von Gaurav P

â€¢7. MÃ¤rz 2018

Looking forward to advanced courses on Linear algebra, eculidean geometry that would make the concepts of vectors, matrices, plane and any application of those in the data science problems.

von Abdul H S

â€¢4. Mai 2020

It covers all basics of mathematics and of-course intermediate concepts from Mathematics which are essential for data science in general, and very useful for data mining, data storage etc.

von Annisa H A

â€¢10. Juli 2020

the first 2 practices and quiz was not that challenging, but the starting from week 3 it's getting hard.

von Simas J

â€¢27. Juli 2020

The lectures notes did not contained too much of supportive material followed by the video lectures. I would suggest that video notes, would contain not just the same content that has been shown in the video lectures, but in addition a further reading material that would allow student to strengthen his understanding on the topic, as well as include exercises with answers (+ solutions) so a student could be firm in his knowledge before proceeding to the next section. At least some links to fill in the knowledge gaps or relevant subject.

The video lectures, although have been very informative and useful, I found a significant difference in how subject have been taught and discussed by both teachers. I would highly recommend to address such unbalances in teaching, as it discourages from continuing and finishing the course (initial lectures were greatly useful and the quality of the lessons deteriorated as the course progressed).

Overall, I have found this course useful, however I doubt I have gained much from the week 4 (probability subject) as the material was not really intuitive and hard to follow with great jumps in knowledge which one may not be aware, unless had previous experience on the topic.

I would recommend this course to people who have already an intermediate (or above) understanding of the subjects taught and/or would like to recap areas which has been forgotten over time. I would not recommend this course if you are a beginner or have large knowledge gaps on Maths as it will make the lectures hard to follow and probably difficult to identify the gaps in one's own knowledge.

von Johan M

â€¢6. Jan. 2023

The first 3 weeks were great. Most of the stuff I had already learned, but that was 30+ years ago. It was a good refresher course and I enjoyed myself. As I took a lot of notes I had to learn how to write mathematical formulas in markdown in Obsidian using MathJax, which was also fun. The fourth week was a bummer. The presentations weren't any good at all, the handwriting was bad, the explanations barely scratched the surface and I struggled with the quizzes.

Do you learn the necessary math skills for data science with this course? I have no idea.

Did I learn something? Yes, it was a good refresher course during the first 3 weeks, so much so that I want to study more math. As for the last week, I will have to look elsewhere for a book or course about statistics and probability.

Positive: The first 3 weeks were great and I really enjoyed them.

Negative: The last week was a waste of time, and I will have to find other sources. Compared to some other courses the videos and presentations lack professionalism. This course has been orphaned, as in the creators have abandoned this course. If you have a problem, the best you can hope for is the help of a fellow student.

Each of the first 3 weeks, despite some shortcomings: 4 stars

The fourth week: 1 star.

Average: 3 stars

von Allen F

â€¢5. Okt. 2019

The first part of this course was great. It was the right level of material, taught simply and effectively with quizzes and exams that were on par with the taught material. The second half was not so great. The teaching style of the second teacher did not convey the material as effectively as the first teacher. Also, I felt that the week 4 probability quiz and final exam had material way beyond what was taught during the lesson. There should have been some exercises to warm us up and get us to the difficulty level of the final. It felt like going from 0-100 mph. Overall because of the stark difference in teaching and difficulty of the final exam of part 4, I can only give this course three stars for the great start.

von Nestor S

â€¢25. Jan. 2023

Last part of the course (Bayes) is poorly explained, and the quizzes barely test (the little) taught on video.

von Ioannis-Panagiotis D

â€¢31. Jan. 2023

I was planning to rate it as 4 stars but the last week was terrible.

At the end of week 3 they introduced a new instructor. All things considered week 4 and the new instructor are terrible.

More specifically, the instructor lacks the ability to put across information and explain it in an understandable manner. His handwriting is quite bad as well and sometimes its hard to make out what is written on the board. Plenty of times he does calculations without explaining or even skips calculations and moves on. He takes many things for granted and doesn't care a lot about explaining topics and problems further or deeper.

There were times where he just changed slides to a slide full of pre-written information/calculations etc.. Why do that? Why not write them while on camera and go through your process of thinking so the viewer can understand better? Oh and there was also an instance where in the middle of a problem he started doing calculations with numbers that appeared out of nowhere and only after he finished did he explain how he got those numbers. But i - and probably many more- were already confused by then.

I suggest this course only if you want to remember old junior high - high school math topics. If you want to learn actual data science mathematics i personally believe that you should avoid this course. That's what i would do, now that i have completed the course.

von Md. Z M

â€¢8. MÃ¤rz 2019

For someone with a Computer Science background at the undergraduate level, I find the contents basic. However, the intention of the course was to give a refresher for data science professionals who find the mathematical jargon frequently used in practice hard to comprehend. In this sense, the first half of the course taught by Prof. Paul Bendich were good. The second part of the course taught by Prof. Daniel Egger needs a lot of improvement in content delivery and better explanation. The quizzes on probability are challenging and enjoyable. Also, when I took the course as on March 2019, there wasn't any activity on the discussion forum. It seems there are not many students taking the course with me, and it also wasn't monitored by the course staff.

von Deleted A

â€¢20. Aug. 2017

The first two weeks are good. The material is explained in a fairly intuitive way. One can easily understand the theory. 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. I had to check external websites to fully understand the material. The lectures also didn't prepare me good enough for the tests. Sometimes I felt lost and the video companions also didn't really help. This wasn't the case in the first two weeks. At the end I was able to complete all tests with 100% but only because I taught the material myself with the help of external websites.

von Bryan C

â€¢14. Sep. 2022

The lectures are confusing. Period. I have covered all this material and much further getting straight A's in everything from Algebra 1 and Geometry through Calculus and Statistics - and I find these lectures barely understandable. Main points and formulas are not adequately highlighted and examined and things are said in passing.... in addition to the errors being made. You would think the lectures, notes and quizzes would be thoroughly reviewed and corrected for mistakes before posting....

von David L

â€¢27. Jan. 2023

It helped me to refresh my knowledge from high school (26 years ago) before studying again. Other than that, I found it not really engaging. It's taught the way it has always been taught in schools and universities, a teacher delivering knowledge. Also I expected that more connections would be shown between the content and Data Science world. Examples with dices and urns are good to get the concept but it should be more connected to Data Science.

von RODRIGO E M P

â€¢2. Sep. 2022

First it is very easy and the last week is difficult. There is no relationship between the material explained and what is evaluated. The course is subject to much improvement.

von Christopher M R

â€¢31. Juli 2020

Audio is weak. Bendich is probably a good researcher, but not a good teacher. Doesn't make any effort to speak clearly, tone of voice is that the subject matter is beneath his genius, he's only "teaching" the course because Duke ordered him to teach it. Disappointed. I just copied down the curriculum then watched it on Khan. Sal Khan speaks clearly and covers the same boring 10 minute lecture in a lively half the time. Thanks anyway Coursera

von Timmy C

â€¢30. Juni 2020

I just finished the Data Science Math Skills on Coursera which is taught by Paul Bendich and Daniel Egger. Overall I learned a lot from this, but most of the stuff I learned already in Algebra class. For example, one thing that I already learned was the definition of infinite numbers on the number line.

One new thing I learned was about sets, which are a way to group numbers efficiently. It makes a big set of data easier to read and process. I also learned from this course is something called sigma notation, which is a way to solve a certain type of equation.

If this sounds really technical, donâ€™t worry. Honestly, the course was less about doing math and more about learning how math is related to data science and some basic techniques and definitions.

Overall this course surprised me because I thought it was going to be a little boring, but it turned out alright. The only bad thing I would say about it is that Paul Bendich made a quite amount of errors in his lesson, causing Coursera to pause the video and edit it

von Selva g V

â€¢24. MÃ¤rz 2021

If it is not for Dr.Paul Bendich, I would not have even continued with the course, let alone complete it. I wish I had such a teacher during my high school/college days. If I would have had one, my love for Mathematics would not have died. Dr.Paul Bendich is a God send. I thank God for making me attend this course. I am starting to love Mathematics again. I missed him during the last two weeks though. Dr.Egger was good. but for a student like me , Dr.Paul Bendich would have made a difference. different types of students need different types of teachers that suit them, right?

I am looking forward to more courses from Dr.Paul Bendich. Kudos to you Professor, I thank you for simplifying things, explaining things in such a way that even a lay man like me can understand and making me complete this course and rekindling my love towards Mathematics. I thank God for sending you.

von Hyo-Ju M

â€¢21. Mai 2021

So, I did well with Data Science Mathematical skills. First thing I did was the basic notions of theory, intersections, statistical quantities, Cartesian plane, measure distance and finding the equations of lines. So many vocabulary to go through, but a lot easier to understand. With any concepts, here is the real-world problem, turn to exponents, logarithms, rate of change for continuous growth, and much more. The final week is a lot difficult, but I finally did. I really did my best and focus what problems should come up. Probability and Bayes' Theorem. Those two are very important for me, but with certain questions and answer I know very well. This takes practice, but with enough effort that I made an improvement, I passed all grades. Though it's not always perfect, but I know what to do. I enjoyed this subject, pretty much I would say. Until next time.

von Karina K

â€¢31. Mai 2021

I had my degree in Mathematics in 2009 from a university in Indonesia. Joining this course was a refresher for me. I know that some people don't like courses with hand writing but I LOVE IT! This really reminds me of my time in Uni and the best part is, both Paul and Daniel really helped me to understand the concept completely! When I was in Uni, I got good grade because I'm good with exams however I was just remembering formulas and I didn't really know how I can apply it in the real world. Now that I re-learn it, I just fell in love with Math all over again and I understand the concept much better. It's really good to know how to apply Math in the real world, which is for data science, data analysis and machine learning. Thanks a lot, Daniel & Paul! <3

von Rodrigo C

â€¢21. Mai 2020

The course overall was great. It was well taught-- very relevant and clear for the most part. 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 longer, with 20-25 minutes and 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. I will certainly recommend the course to my friends who wish to have better knowledge in mathematics for data science.

von Jennifer D C

â€¢6. Apr. 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.

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