Zurück zu Data Science Math Skills

4.5

Sterne

8,745 Bewertungen

•

1,983 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!...

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)

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!!

Filtern nach:

von Arun A

•20. Apr. 2018

Very good course. Really enjoyed the explanation very concise and clear. This is however not exhaustive for anyone to gather every bit of possible knowledge in Data Science, do keep that in mind. But this definitely sets the foundation correct.

von Yan T

•1. Nov. 2019

Great course brushing off math skills with quiz in the end of each module. The last module is the most difficult, it'll be better if more examples be demonstrated. Good pace and a good course for those who need to get an idea of some topics.

von MILDRED B R

•6. Juli 2020

Very informative and I learned a lot. I enjoyed taking the practice quizzes and the exams. I failed, I made mistakes yet what is the most important I learned a lot. Thank you Coursera and DOST-CARAGA, the Philippines for the opportunity.

von Marios P

•6. Nov. 2020

Effective way to refresh and add the Data Science math skills! The course overall was great. It was well taught-- very relevant and clear for the most part. Thanks a lot! I think I am better prepared for data science afterward!

von JUAN G

•25. Juni 2020

Pretty fun and understandable. The last week resources and content was quite deep and confusing when it was explained. From the exercises I've found some errors in the solutions. Briefly, It fulfills my expectations pleasantly.

von Shar M G

•7. Okt. 2020

Extra grateful for the refresher on essential math topics needed for data science. Week 1-2 is such a great way to review pre-calculus concepts. Week 3-4 modules were extremely helpful for those in need of Stat refreshers.

von J P K

•13. Okt. 2020

Good Course for Beginners, with lot of insights, and its entirely basic ,anyone without any prior knowledge can do the best with this kind of course;And I thank Coursera as well as the Instructor for offering this course.

von Deleted A

•1. Aug. 2020

Well, except for the last quiz of probability (basic and intermediate) others were too easy, and also this course is highly recommended for the ones who really are entirely new to math concepts present in this course.

von Alice L

•6. Sep. 2020

It was delightful. I think this was a great introductory course. I still had to go on for more learning material for the harder topics, but the structure of the course really provided the backbone on what to learn.

von Aldrich W

•13. Juli 2020

Maybe the first three chapters are a little bit easy; but the fourth chapter is challenging. This is my very first Coursera course, and I have learned a lot. Shout out to Prof. Daniel Egger and Prof. Paul Bendich!

von Alex A

•13. Juni 2020

I was looking for a math class to refresh my math skills in preparation for getting into the field of Data Science and I am very glad that I found this course. It was well taught and very clear. Thank you so much.

von Suparit S

•19. Juli 2020

The course provides comprehensive basic math skills for the ones who might have forgotten all the maths they have learnt. Highly recommended for ones who wants to restart the math skills with clear intuition!

von Yasir M

•13. Juli 2020

It was a good basic level course of math design by Duke University.I just say thanks to all of this course team and especially thanks to coursera for providing such a amazing opportunity to learn more skills.

von Hemali V

•13. Sep. 2020

It is an amazing course for everyone who want to learn Maths. after learning this basic math skill, i am capable to perform basic algorithms for Machine Learning.

It is very helpful course for me, Thank you!

von Eloisa R

•8. Juli 2020

If you have previous background is easier, but in Baye's theorem it was a little hard to understand it. Overall, it is an excellent course, but a little more explanations in Baye's theorem could be better.

von Muhammad H

•26. Juni 2020

The course was really great. You could include some more offline practice questions with solutions for better practice at least 10 for each topic. Otherwise explanations were easy to understand and follow.

von Guido T G

•17. Nov. 2020

If you want to review and engage knowledge this course its a good reason for start the apllication of maths. The course have for you a good material and very good questions for real deep learning

von Weicheng(Will) H

•12. Aug. 2020

For the part of Bayes' theorem, I think the explanation is no clear or through enough. Maybe more break-down is necessary. And the other parts are really good. I really enjoy taking this course.

von Nikisha E

•3. Jan. 2021

I want to thank my educators for this wonderful job that they are doing.i have learn so much from you guys and i want to thank you all for your kind support in this data science maths skills.

von Abu I M S A

•18. Okt. 2020

This is a great course, many things are basic but I have learned those a long day ago. Some explanations are from different perspective than conventional. However, definitely a great course.

von veer

•21. Juli 2020

A great foundation course for anybody whoose tryna find their feet in the excitng and vast world of data science,i learnt so many new things..it was truly an enriching experience.Thank you!!

von Altynay O

•7. Juli 2020

My first course on Data Science and was happy to share this experience with Duke University. The course was informational, very resourceful and hopefully beneficial for my future endeavors.

von Kensuke I

•30. Apr. 2020

Good learning overall.

Some part are pretty easy, which I remember learning in junior high,

but some theorems are hard to apply.

Now I want to use these theory in real world and data science!

von Bayu W

•9. Juli 2020

I learned basic math of data science from based to Bayes' theorem. Lecturer explained material clearly and easy to understand. It helps me to start my journey in data science. Thank you.

von KESHA R

•6. Juli 2020

The instructors for this course have a very calm, awesome demeanor. They are very confident and efficient in their methodology overall. This course has reawakened my desire for learning.

- Sinn und Zweck im Leben finden
- Medizinische Forschung verstehen
- Japanisch für Anfänger
- Einführung in Cloud Computing
- Grundlagen der Achtsamkeit
- Grundlagen des Finanzwesens
- Maschinelles Lernen
- Maschinelles Lernen mittels Sas Viya
- Die Wissenschaft des Wohlbefindens
- Contact-Tracing im Kontext von COVID-19
- KI für alle
- Finanzmärkte
- Einführung in die Psychologie
- Erste Schritte mit AWS
- Internationales Marketing
- C++
- Predictive Analytics und Data-Mining
- UCSD: Learning How to Learn
- Michigan: Programming for Everybody
- JHU: R-Programmierung
- Google CBRS CPI Training

- Natural Language Processing (NLP)
- KI für Medizin
- Guter Umgang mit Worten: Redaktionelles Schreiben
- Modellbildung von Infektionskrankheiten
- Die Aussprache des US-amerikanischen Englisch
- Software-Testautomatisierung
- Deep Learning
- Python für alle
- Data Science
- Geschäftsgründungen
- Excel-Kenntnisse für Beruf
- Data Science mit Python
- Finance for Everyone
- Kommunikationsfähigkeiten für Ingenieure
- Verkaufstraining
- Career Brand Management
- Wharton: Unternehmensanalytik
- Penn: Positive Psychology
- Washington: Maschinelles Lernen
- CalArts: Grafikdesign

- Zertifikate über berufliche Qualifikation
- MasterTrack-Zertifizierungen
- Google IT-Support
- IBM Datenverarbeitung
- Google Cloud Data Engineering
- IBM Applied AI
- Google Cloud Architecture
- IBM Cybersecurity Analyst
- Google IT Automation with Python
- IBM z/OS Mainframe Practitioner
- UCI: Angewandtes Projektmanagement
- Zertifizierung in Instructional Design
- Zertifizierung in Bauwesen und -management
- Zertifizierung in Big Data
- Zertifizierung Maschinelles Lernen für Analytics
- Zertifizierung in Innovation Management & Entrepreneurship
- Zertifizierung in Nachhaltigkeit und Entwicklung
- Zertifizierung in Soziale Arbeit
- Zertifizierung KI und maschinelles Lernen
- Zertifizierung in Räumliche Datenanalyse und Visualisierung

- Abschlüsse in Informatik
- Business-Abschlüsse
- Abschlüsse im Gesundheitswesen
- Abschlüsse in Data Science
- Bachelorabschlüsse
- Bachelor of Computer Science
- MS Elektrotechnik
- Bachelor Completion Degree
- MS Management
- MS Informatik
- MPH
- Master-Abschluss in Buchhaltung
- MCIT
- MBA online
- Master of Applied Data Science
- Global MBA
- Master in Innovation & Entrepreneurship
- MCS Data Science
- Master in Informatik
- Master-Abschluss in Public Health