Zurück zu Algorithmic Thinking (Part 1)

4.7

Sterne

321 Bewertungen

•

65 Bewertungen

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part course builds on the principles that you learned in our Principles of Computing course and is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to real-world computational problems.
In part 1 of this course, we will study the notion of algorithmic efficiency and consider its application to several problems from graph theory. As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. The main focus of these tasks is to understand interaction between the algorithms and the structure of the data sets being analyzed by these algorithms.
Recommended Background - Students should be comfortable writing intermediate size (300+ line) programs in Python and have a basic understanding of searching, sorting, and recursion. Students should also have a solid math background that includes algebra, pre-calculus and a familiarity with the math concepts covered in "Principles of Computing"....

OT

28. Sep. 2018

very educational. I've learnt not only about graph theory but also how to use matplotlib and timeit libraries. The assignments were quite challengeable but rewarding.

MR

16. Sep. 2019

The class is very useful, I already see the improvement in the codes that I write. And the assignments are very well-designed and truly helpful.

Filtern nach:

von Ian B

•25. Juli 2017

Excellent course.

Algorithmic Thinking is significantly more difficult than Principles of Computing. I felt there was a big step up, and I had to do a lot of research and reading outside of the course just to keep up. The listed 7-10 hrs/wk is too low IMO. I spent 14.5 hrs/wk on average (for parts 1 and 2). Maybe if you're already really good at math and experienced with matplotlib you'll take less time.

Anyway, the material is great, and gives you a good glimpse of how difficult problems can be solved efficiently. It's one thing to be shown an algorithm and how it was created, but it's entirely different thing to do it yourself from scratch. But at least I'm now aware of the strategy and can practice it. I learned a lot from these courses (the whole specialty), and am miles ahead of where I was when I started.

von Yair B

•30. Apr. 2016

The lectures will not get you near the understanding you'll need to complete the assignments. though the resources will.

The application is great, and hard so you'll actually have to understand the material.

You have been warned, this course is very different to the other courses in the specialization!

von Alvin L

•22. Nov. 2017

What the professor explains he explains well, but there is a lot of stuff in the homework assignments that is not explained

von Zoltán T

•18. Juli 2019

There are some videos where the lecturer can't even use a computer. Then there are a homework which is completely unrelated to everything taught during the lectures. Regarding the practice examples, key information are missing from the descriptions. I ended up frozing my computer several times because the problem was very ill-written...

von Max B

•21. März 2019

Oh man, I hade so much fun in this course! The lectures and material is very good, and everything is wrapped up in much fun projects and applications where you will learn a lot. I especially enjoyed the more mathematical approach in AT compared to PoC and IIPP, and also the general class structure! Highly recommended!

von Julian O

•21. März 2018

Another excellent course in the specialization from Rice. Really interesting algorithms that were fun, and non-trivial, to implement. The plotting and comparison exercises are helpful for gaining insight.

von Artur P

•20. Sep. 2018

Some parts was hard and some not because of my own experience, in general very good course and only hard problems forces us to think.

von Daniel W

•8. Jan. 2020

Pros:

Lots of good material to learn. Challenging. Lectures are easy to understand.

Cons:

More dense, textbook-jargon "CS major" feel to this class than the others. Expect to spend more hours and have less fun vs. parts 1-4. Much easier to get discouraged. Major problems waiting for assignments to go through peer grading process, sometimes taking *weeks*.

Suggestions:

More basic handouts such as: 1) Set notation cheat sheet. 2) Pseudocode examples fully decoded into simple language. Also, watching a visualization of the base-pairing algorithm (Needleman-Wunsch) is highly recommended for understanding what you're trying to do. You can google it, but it would be nice if they added it to the course. Also, more smiling.

In summary, it's a challenging course and I'm a better programmer for having finished it. However, it's more daunting, took me longer, and lacks the easy going/encouraging/illustrative style of the earlier courses. Peer grading takes way too long, especially if you're paying for a subscription.

(My review applies to both Algorithmic Thinking Parts 1 and 2)

von Stephen H

•7. Mai 2016

The transition from the original Coursera platform to the new one has significantly reduced my overall satisfaction with the course experience (I have previously taken the 4 earlier Rice courses in this series and enjoyed them immensely).

Major contributing factors to my dissatisfaction:

-- The user interface changes are unwelcome and have detracted from my ability to engage with the material.

-- There have been arbitrary (and unannounced) changes to polices, e.g. dropping distinction grading.

-- The price increase per course and locking down of assignments to only those who have paid has unfortunate side effects, including.

-- In contrast to previous courses in this series which had an active community engagement the discussion forums are now relatively empty, running the courses more often is not going to help this situation.

Having previously been an unpaid evangelist for the Coursera approach and materials I now find myself extremely reluctant to recommend this revised experience to colleagues.

von Eelko d G

•17. Nov. 2016

Compared to the previous courses in this specialization I found this a tough course. Calculating the big-O for yourself wasn't easy. The descriptions of the applications #1 and #2 were not very clear to me at the start (probably because English is not my first language). Some sentences I had to read several times before I understand what was asked. But the things I learned are useful and I am definitely sure that what I have learned here I can use in my work as software engineer. Thank you Luay, Scott and Joe for another excellent course.

von Yu(Helena) H

•2. Dez. 2016

Great class, very well constructed. Professors are very knowledgeable and the course materials are well designed. I started from the very beginning of the class knowing very little about python and algorithm, by this class I'm pretty comfortable coding in python. And I got to apply what I've learned in my interview for jobs. Definitely a great class! Learned a lot, and still applying it in my job.

von 周玮晨

•21. Aug. 2018

Very great course.At first i under estimate this course, but through this course i became stuggling in project and assignment. The depth and breadth of the course is wonderful.Maybe sometimes got stucked, but finally always found It's worthwhile spending hours on this course. This course drove me thinking and thinking. It should take some time to review. Sometimes i don't know how i finished

von Justin M

•18. Feb. 2020

Very challenging course, but I did enjoy the content quite a lot. The programming assignments were well-structured and built upon one another to the point that the final graph resilience project took me an entire weekend to complete, but greatly expanded my understanding of both python data structures and how to represent graphs using them.

von Todd R

•28. Aug. 2016

It takes some serious dedication to understand and apply the material if math and computation are foreign to you, like they were for me. But if you apply that dedication, the material is accessible and the feeling of accomplishment is tremendous. Great class/teaching.

von Rudy H

•6. März 2019

Prof. Luay is an excellent instructor, his approach is very well thought of and his explanation on the subject is very constructive and clear which is vital to the understanding of such subject. I am learning a ton and very thankful to all that involved.

von Olga T

•28. Sep. 2018

very educational. I've learnt not only about graph theory but also how to use matplotlib and timeit libraries. The assignments were quite challengeable but rewarding.

von Maysam Q R

•17. Sep. 2019

The class is very useful, I already see the improvement in the codes that I write. And the assignments are very well-designed and truly helpful.

von Márton A N

•8. März 2018

This is where computer science truly starts, without the excessive preliminary math that usually scares most people away. Great course!

von Gerardo G

•8. März 2019

Great course, please offer an oline program to obtain an Rice university grade in science computer.

von Tairan Y

•13. Mai 2018

very thoughtful course!

not easy by any means, but for sure learned a lot from the hard experience.

von Aaron M

•22. März 2018

A step up in difficulty from the previous modules in this specialisation.

von Vern K

•26. Juli 2018

Course and assignments were very well thought out and informative.

von Andrew F

•5. März 2018

Another fantastic course from the team at Rice - thank you!

von Adam C

•9. Juli 2019

Great course!

von Rita I G

•7. Feb. 2019

Good course!!

- 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