Zurück zu Data Science Math Skills

Sterne

9,317 Bewertungen

•

2,115 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!...

VC

16. Mai 2020

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 Robyn J

•7. Feb. 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.

von Marcel S

•30. Apr. 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.

von Roberto S

•24. Juli 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.

von Md. J R

•5. Juni 2020

This course syllabus is great. It starts wonderfully. Week 1 to 3 is taught by Paul Bendich. His instruction is awesome. However, in week 4 Daniel Egger starts teaching log, exponential , probability which is simply a disaster. I have not seen such bad instruction in any of coursera class I have taken. I saw in Coursera he is a "top instructor" which makes me worry about coursera content as a whole. If there is a prize for how not to teach math, Daniel egger would win it.

von Danuel R

•1. Apr. 2017

Difficult content not explained well by the presenters.

von Jason B

•13. Feb. 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.

von Austin S

•7. Jan. 2019

Silly course. Either you know so much math to be able to pass this course or you know nothing to find this course of zero value. Avoid.

von Mikhail G

•28. März 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.

von Andrey S

•12. 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)

von Judy M

•27. Apr. 2020

There are a great many errors in both the lesson videos and the quizzes in this course. In the first half, correction messages pop up during the videos of the lessons. Many additional errors were highlighted over 2 years ago in the discussion forums, so it appears as though no one is currently maintaining this course at all. The second instructor does not teach content at all. He simply records on his slide what he has calculated off screen. Numbers appear in formulas but rarely is there an explanation given as to where those numbers come from or why they belong in the formula where they are substituted. Furthermore, the slides are just a jumble of notations by the end of he slide so are of no value as a reference note. Many of the discussion forum comments were suggestions by others taking the course as to where to go to get an actual lesson. Someone who did not have a background in mathematics who actually cared about learning would be very stressed by the exceptionally poor way this course is designed and delivered. Final comment -- no connections are made between the math presented and data science.

von silvia a

•6. Aug. 2019

Dear Professor,

Please improve your handwriting. Or at least prepare your materials using slides. It will help the students understand your information better.

von Luis A C G

•5. Mai 2017

I deeply regret having paid for this course. Nothing in it was oriented to data science and weeks 3 y 4 are specially weak in contents on basics on calculus and probability. Not bad if you just want to remember some things from upper secondary maths but definetly not worth to pay for it.

von Adam R

•18. Apr. 2020

Lacked practice questions to reinforce concepts as is expected in any math course. Content in videos did not line up with assessments. Practice questions and examples would have bridged this gap. This course may function as a good refresher if you are already fairly confident in the topics but have not seen them in a while. Lecturers were very good, there was just a content mis-match between videos and assessments. Videos also contained multiple mistakes (which are noted), but should have been edited or bits re-recorded. Video editing is everywhere and accessible, there is very little excuse for not publishing a polished product considering the medium.

von Richard S

•6. 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!!

von Gökhan T

•12. Mai 2020

I understand that they wanted to explain necessary content starting gently and getting to more complex subjects gradually. But in reality they start as simple as an elementary school students finds easy. Which is not so bad. After that, complexity of the subjects may suddenly skyrocket without any explanation, even without giving necessary subjects formulas. There is assistance or further reading recommendations. No one is answering the questions on discussion forums except the students.

There are lots of other courses. This one teaches the easiest concepts in an unnecessarily long way and does "nothing" to teach complex ones which I guess why you are enrolling to this class. Shame on you Duke University. Crap course.

von vivek d c

•17. Mai 2020

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)

von Marcello P G d S

•12. Mai 2020

First three chapters extremely simple. Last (fourth) chapter has a quiz entirely different from the contents of video lessons as well as texts.

von Mahnaz K

•22. Okt. 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.

von Karl W P

•5. Feb. 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.

von Priyanka S

•23. Juli 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.

von Danny N

•18. Nov. 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!

von Omar S

•30. März 2020

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

von Mario B H B

•5. Feb. 2020

Excelent course to begin!

von Mukhtar A A

•14. Apr. 2020

I found week one amazing and the instructor have an excellent way to explain and make things simpler unfortunately I did not enjoy the 3rd and 4th week

von geary b

•11. Dez. 2017

TRASH!

- 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
- Facebook Social Media Marketing
- IBM Full Stack-Cloudentwickler
- Salesforce Sales Development Representative
- Sales Operations in Salesforce
- Soporte de Tecnologías de la Información de Google
- Certificado profesional de Suporte em TI do Google
- Google IT Automation with Python
- DeepLearning.AI TensorFlow
- Beliebte Zertifizierungen für Cybersicherheit
- Beliebte SQL-Zertifizierungen
- Beliebte IT-Zertifizierungen
- Alle Zertifikate anzeigen

- 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
- Android-Entwicklungsprojekte
- TensorFlow- und Keras-Projekte
- Python für alle
- Deep Learning
- Excel-Kenntnisse für Beruf
- Geschäftsgründungen
- Maschinelles Lernen
- AWS-Grundlagen
- Datenengineering-Grundlagen
- 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 Abschlüsse anzeigen