Zurück zu Data Science Math Skills

Sterne

9,786 Bewertungen

•

2,209 Bewertungen

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!...

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.\n\nThank you for a great class!!

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)

Filtern nach:

von Aniket P P

•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

•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

•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

•7. März 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

•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 Annisaa H A

•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

•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 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.

von Allen F

•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 Md. Z M

•8. März 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

•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 Christopher M R

•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

•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, don’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

•24. März 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

•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

•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

•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

•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.

von Christopher B

•6. Juli 2020

The course has truly been helpful in showing me my level of understanding on the topics, as like all the other reviews it was a great refresher and more so it was truly helpful in helping me see areas i needed to improve on to become more advance in mathematics. i definitely recommend this course for those who want an understanding of the mathematics that is used in data science. this course by no means is an all you need math course to be successful with data science, but more of a stepping to stone or guide in my opinion in finding the right path to take to become far better in mathematics for this field.

von Mariana E

•8. Apr. 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.

von Laurent B

•19. Juni 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.

von Aditya K

•21. Sep. 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.

von Michelle C d J

•14. Mai 2021

I found the first half of the course quite easy, as it was a refresher on the math that I managed to learn and retain as a high school student. However as I progressed through the course and found myself revisiting calculus and statistics concepts I found it challenging as I hadn't so much as touched a mathematics textbook since I graduated from university. That said I found this course excellent at understanding the mathematical principles on which data science foundations are built. It's definitely worth taking in my opinion.

von Josmy A J

•25. Okt. 2020

I learned a lot through this course. Set Theory,it's applications ,many formulas,functions,graphs ,probability and it's applications etc etc..I was able to study everything very well.Teachers taught well.it was a good course and also a good experience.I was able to know a lot of things. It was a kind of class where everything could be understood. The teachers explained everything very well.The examples given was more helpful.Through this course I was able to do each problem better.Thanks to the teachers who taught.

von Baskaran V

•15. Jan. 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.

- Google Data Analyst
- Google-Projektmanagement
- Google-UX-Design
- Google IT-Support
- IBM Datenverarbeitung
- IBM Data Analyst
- IBM-Datenanalyse mit Excel und R
- IBM Cybersecurity Analyst
- IBM Data Engineering
- IBM Full Stack-Cloudentwickler
- Facebook Social Media Marketing
- Facebook Marketinganalyse
- Salesforce Sales Development Representative
- Sales Operations in Salesforce
- Buchhaltung mit Intuit
- Vorbereitung auf die Google Cloud-Zertifizierung: Cloud Architect
- Vorbereitung auf die Google Cloud-Zertifizierung: Cloud Data Engineer
- Eine Karriere starten
- Auf eine Zertifizierung vorbereiten
- Bringen Sie Ihre Karriere voran

- Kostenlose Kurse
- Lernen Sie eine Sprache
- Python
- Java
- Webdesign
- SQL
- Gratiskurse
- Microsoft Excel
- Projektmanagement
- Cybersicherheit
- Personalwesen
- Kostenlose Kurse in Datenverarbeitung
- Englisch sprechen
- Inhalte verfassen
- Full-Stack-Webentwicklung
- Künstliche Intelligenz
- C-Programmierung
- Kommunikationsfähigkeiten
- Blockchain
- Alle Kurse anzeigen

- Kompetenzen für Datenwissenschaftsteams
- Datengestützte Entscheidungsfindung
- Kompetenzen im Bereich Software Engineering
- Soft Skills für Ingenieurteams
- Management-Kompetenzen
- Marketing-Kompetenzen
- Kompetenzen für Vertriebsteams
- Produktmanager-Kompetenzen
- Kompetenzen im Bereich Finanzen
- Beliebte Kurse in Datenverarbeitung im Vereinigten Königreich
- Beliebte Technologiekurse in Deutschland
- Beliebte Zertifizierungen für Cybersicherheit
- Beliebte IT-Zertifizierungen
- Beliebte SQL-Zertifizierungen
- Karriereleitfaden für Marketing-Manager
- Karriereleitfaden für Projektmanager
- Python-Programmierkenntnisse
- Karriereleitfaden für Webentwickler
- Datenanalysefähigkeiten
- Kompetenzen für UX-Designer

- MasterTrack® Certificates
- Zertifikate über berufliche Qualifikation
- Universitätszertifikate
- MBA- und Business-Abschlüsse
- Abschlüsse in Data Science
- Abschlüsse in Informatik
- Abschlüsse in Datenanalyse
- Abschlüsse im Gesundheitswesen
- Abschlüsse in Sozialwissenschaften
- Management-Abschlüsse
- Abschlüsse von europäischen Spitzenuniversitäten
- Masterabschlüsse
- Bachelorabschlüsse
- Studiengänge mit Performance Pathway
- BSc-Kurse
- Was ist ein Bachelorabschluss?
- Wie lange dauert ein Masterstudium?
- Lohnt sich ein Online-MBA?
- 7 Finanzierungsmöglichkeiten für die Graduate School
- Alle Zertifikate anzeigen