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Algorithmic Thinking (Part 1)

Algorithmic Thinking (Part 1)

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8.9/10 (Our Score)
Product is rated as #28 in category Computer Science

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”.

Instructor Details

Luay Nakhleh received a BSc degree in Computer Science from the Technion (Israel) in 1996, a Master's degree in Computer Science from Texas A&M University in 1998, and a PhD degree in Computer Science from UT Austin in May 2004 (Advisor: Prof. Tandy Warnow). While at UT Austin, he received the Outstanding Doctoral Dissertation Award, the Bert Kay Dissertation Award, the Texas Excellence Teaching Award, and the Outstanding Teaching Assistant Award. Luay joined the Computer Science department at Rice University as an Assistant Professor in July 2004, and was promoted to Associate Professor, with tenure, effective July 2010. While at Rice, he received the DOE CAREER award in 2006, the NSF CAREER award in 2009, the Phi Beta Kappa Teaching award in 2009, an Alfred P. Sloan Research Fellowship in 2010 (in the Molecular Biology category), and a John P. Simon Guggenheim Foundation Fellowship in 2012 (in the Organismic Biology and Ecology category).

Specification: Algorithmic Thinking (Part 1)

Duration

20 hours

Year

2016

Level

Intermediate

Certificate

Yes

Quizzes

Yes

50 reviews for Algorithmic Thinking (Part 1)

4.1 out of 5
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  1. Todd R

    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.

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  2. Karun

    The applications were too time consuming. Please consider adding a tool that makes graphing easier. The course itself was very good and engaging and without us knowing it, would teach core fundamentals of computing through the coding exercises.

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  3. Isuru

    A course I enjoy very much!

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  4. Andrey S

    Too much bla, bla, bla. Very slowly, very boring.

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  5. Eelko d G

    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.

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  6. Yu(Helena) H

    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.

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  7. Qi D

    coursea does not allow me to quit the class. Also, I cannot do the homework or watch video at my own pace.

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  8. Martin W

    great course

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  9. Ze C

    Application assignment is a must do for students taking this course. The second computer network application is very a rewarding one for me to finish with gains on concepts of graph as well as programming stretch with my hands dirty.

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  10. Michal J

    Good for it lovers

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  11. Alexandrov D

    Thanks )

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  12. Ian B

    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.

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  13. Arthur–Lance

    thanks a lot

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  14. Arthur Lance

    thanks a lot

    Helpful(0) Unhelpful(0)You have already voted this
  15. Rachel K

    The project based course structure works really well for the material. This was a great course!

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  16. Marcello F

    Great course !

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  17. Arnob B

    Last assignment was a bit weird but great course otherwise!

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  18. Nathaniel B

    Excellent course!

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  19. Y A

    This is Wonderful and simpler explained course that is detailed with ‘learner’s requirement’.

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  20. Zou S

    Very impressive and interesting. Graph theory is really elegant representation of the computer network.

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  21. Ganapathi N K

    Superb

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  22. Edwin R

    The course content is well structured and the instructors’ explanation is clear and concise!

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  23. Alvin L

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

    Helpful(3) Unhelpful(0)You have already voted this
  24. Albert C G

    Great Class Truly makes you think

    Helpful(0) Unhelpful(0)You have already voted this
  25. Michael B R

    Another great course in this specialization!

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  26. Jaehwi C

    The best course to study computer science and algorithm for beginner!

    Helpful(0) Unhelpful(0)You have already voted this
  27. Siwei L

    Very helpful course!!

    Helpful(0) Unhelpful(0)You have already voted this
  28. Wynand

    Not quite the same level of energy presents in IIPP and Computing Principles. Also did not like the peer review projects, too messy.

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  29. Andrew F

    Another fantastic course from the team at Rice thank you!

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  30. Marton A N

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

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  31. Julian O

    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.

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  32. Aaron M

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

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  33. Tairan Y

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

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  34. Vern K

    Course and assignments were very well thought out and informative.

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  35. Jayadev H

    lectures are a bit on the slow side… not straight to the point and a bit repetative.. bfs we have already done in this spezialization. but homework/project/applications are excellent! makes up for the rest! Thank you!

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  36. Artur P

    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.

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  37. Olga T

    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.

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  38. Rita I G

    Good course!!

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  39. Rudy H

    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.

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  40. Gerardo G

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

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  41. Max B

    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!

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  42. Deepak V

    It was a good learning experience

    Helpful(0) Unhelpful(0)You have already voted this
  43. Adam C

    Great course!

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  44. Zoltan T

    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…

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  45. Eul S S

    E

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  46. Maysam Q R

    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.

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  47. Deepthi V J

    good one

    Helpful(0) Unhelpful(0)You have already voted this
  48. Jeffrey C

    Very challenging course

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  49. Daniel W

    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)

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  50. Justin M

    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.

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