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Luay Nakhleh,
Scott Rixner,
and
Joe Warren
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 class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.
Read more
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 class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.
Read more
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 class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.
In part 2 of this course, we will study advanced algorithmic techniques such as divide-and-conquer and dynamic programming. As the central part of the course, students will implement several algorithms in Python that incorporate these techniques 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.
Once students have completed this class, they will have both the mathematical and programming skills to analyze, design, and program solutions to a wide range of computational problems. While this class will use Python as its vehicle of choice to practice Algorithmic Thinking, the concepts that you will learn in this class transcend any particular programming language.
Register for this course and see more details by visiting:
OpenCourser.com/course/35h2pg/algorithmic
What's inside
Syllabus
Module 3 - Core Materials
Sorting, searching, big-O notation, the Master Theorem
Module 3 - Project and Application
Closest pairs of points, clustering of points, comparison of clustering algorithms
Read more
Syllabus
Module 3 - Core Materials
Sorting, searching, big-O notation, the Master Theorem
Module 3 - Project and Application
Closest pairs of points, clustering of points, comparison of clustering algorithms
Read more
Module 4 - Core Materials
Dynamic programming, running time of DP algorithms, local and global sequence alignment
Module 4 - Project and Application
Computation of sequence alignments, applications to genomics and text comparison
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches more advanced algorithmic concepts, including divide-and-conquer and dynamic programming, for solving larger-scale problems
Involves hands-on implementation in Python to reinforce algorithmic thinking and problem-solving skills
Emphasizes the interaction between algorithms and real-world data structures through practical examples
Builds on the foundation of basic algorithmic concepts covered in Part 1 of the course, enabling learners to progress and refine their understanding
Suitable for students with a solid grasp of Python programming and basic algorithmic concepts
Requires proficiency in Python programming, which may pose a barrier for learners without prior coding experience
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Save Algorithmic Thinking (Part 2) to your list so you can find it easily later:
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Reviews summary
Algorithmic thinking part 2
Learners say Algorithmic Thinking Part 2 is a theoretically challenging, conceptually focused course that is well structured and engaging. Code writing is a key component of the course, helping learners comprehend concepts more deeply. Peer review is a significant part of assessment. Mathematical proofs are also a key feature, making this a suitable course for learners seeking high-level understanding of algorithms.
Lots of code writing
"At times, I felt the need for slightly more instruction."
"Then again it's good to be thrown in at the deep end and, writing code eventually helps comprehension."
Mathematical proofs used in course
"It is mathematical in nature."
"If you want a more practice-approached model, please search elsewhere."
Course is challenging
"The course is theoretically more difficult than most other Algorithms course you'd find here on Coursera."
"really having a hard time understanding this professor"
Peer review is required
"This is really good course but bad thing about the course is masrks is given by the students or other person and some people give it bad marks even that's it correct"
"They asked 5 peer revview to pass the course for each project"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Algorithmic Thinking (Part 2) with these
activities:
Brush Up on Python Programming
Show steps
Refreshing your Python programming skills will help you feel more confident in implementing the algorithms you learn in the course.
Browse courses on
Python
Show steps
-
Review your notes or online resources on Python syntax and data structures.
-
Solve a few simple coding challenges or practice exercises in Python.
Organize Your Course Notes and Materials
Show steps
Keeping your notes and materials organized will help you stay on top of the course material and improve your retention.
Show steps
-
Gather all your notes, handouts, quizzes, and assignments.
-
Create a filing system or use a note-taking app to organize the materials.
Review Sorting and Searching Algorithms
Show steps
Reviewing sorting and searching algorithms will prepare you for the core concepts covered in the course.
Browse courses on
Sorting Algorithms
Show steps
-
Go over your notes from a previous algorithms course or study materials.
-
Work through practice problems on sorting and searching algorithms.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow Tutorials on Divide-and-Conquer and Dynamic Programming
Show steps
Following tutorials on these advanced algorithmic techniques will provide a solid foundation for the second part of the course.
Show steps
-
Find reputable online tutorials or video lectures on divide-and-conquer and dynamic programming.
-
Go through the tutorials, taking notes and working through any examples.
Solve Coding Challenges on Dynamic Programming
Show steps
Solving coding challenges will allow you to apply the dynamic programming techniques you learn in the course and improve your problem-solving skills.
Browse courses on
Dynamic programming
Show steps
-
Find online coding platforms or resources that offer dynamic programming challenges.
-
Select challenges of varying difficulty levels and work through them, implementing dynamic programming solutions.
-
Review your solutions and identify areas for improvement.
Implement a Dynamic Programming Algorithm in Python
Show steps
Implementing a dynamic programming algorithm in Python will reinforce your understanding of the technique and provide practical experience.
Browse courses on
Dynamic programming
Show steps
-
Choose a problem that can be solved using dynamic programming.
-
Design and implement the algorithm in Python, ensuring efficiency and correctness.
-
Test your implementation thoroughly with various inputs.
Analyze a Real-World Dataset Using Dynamic Programming
Show steps
Analyzing a real-world dataset using dynamic programming will provide you with valuable hands-on experience and demonstrate the practical applications of the technique.
Browse courses on
Dynamic programming
Show steps
-
Identify a suitable dataset and problem that can be solved using dynamic programming.
-
Load and preprocess the dataset into a suitable format for analysis.
-
Implement and apply the dynamic programming algorithm to analyze the dataset.
-
Interpret the results and draw meaningful conclusions.
Connect with Experienced Programmers
Show steps
Connecting with experienced programmers can provide you with valuable insights and guidance as you progress through the course.
Browse courses on
Networking
Show steps
-
Attend online or offline meetups and conferences.
-
Reach out to professionals in your field through LinkedIn or other social media platforms.
Brush Up on Python Programming
Show steps
Refreshing your Python programming skills will help you feel more confident in implementing the algorithms you learn in the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review your notes or online resources on Python syntax and data structures.
- Solve a few simple coding challenges or practice exercises in Python.
Organize Your Course Notes and Materials
Show steps
Keeping your notes and materials organized will help you stay on top of the course material and improve your retention.
Show steps
Show steps
Show steps
- Gather all your notes, handouts, quizzes, and assignments.
- Create a filing system or use a note-taking app to organize the materials.
Review Sorting and Searching Algorithms
Show steps
Reviewing sorting and searching algorithms will prepare you for the core concepts covered in the course.
Browse courses on
Sorting Algorithms
Show steps
Show steps
Show steps
- Go over your notes from a previous algorithms course or study materials.
- Work through practice problems on sorting and searching algorithms.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow Tutorials on Divide-and-Conquer and Dynamic Programming
Show steps
Following tutorials on these advanced algorithmic techniques will provide a solid foundation for the second part of the course.
Show steps
Show steps
Show steps
- Find reputable online tutorials or video lectures on divide-and-conquer and dynamic programming.
- Go through the tutorials, taking notes and working through any examples.
Solve Coding Challenges on Dynamic Programming
Show steps
Solving coding challenges will allow you to apply the dynamic programming techniques you learn in the course and improve your problem-solving skills.
Browse courses on
Dynamic programming
Show steps
Show steps
Show steps
- Find online coding platforms or resources that offer dynamic programming challenges.
- Select challenges of varying difficulty levels and work through them, implementing dynamic programming solutions.
- Review your solutions and identify areas for improvement.
Implement a Dynamic Programming Algorithm in Python
Show steps
Implementing a dynamic programming algorithm in Python will reinforce your understanding of the technique and provide practical experience.
Browse courses on
Dynamic programming
Show steps
Show steps
Show steps
- Choose a problem that can be solved using dynamic programming.
- Design and implement the algorithm in Python, ensuring efficiency and correctness.
- Test your implementation thoroughly with various inputs.
Analyze a Real-World Dataset Using Dynamic Programming
Show steps
Analyzing a real-world dataset using dynamic programming will provide you with valuable hands-on experience and demonstrate the practical applications of the technique.
Browse courses on
Dynamic programming
Show steps
Show steps
Show steps
- Identify a suitable dataset and problem that can be solved using dynamic programming.
- Load and preprocess the dataset into a suitable format for analysis.
- Implement and apply the dynamic programming algorithm to analyze the dataset.
- Interpret the results and draw meaningful conclusions.
Connect with Experienced Programmers
Show steps
Connecting with experienced programmers can provide you with valuable insights and guidance as you progress through the course.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Attend online or offline meetups and conferences.
- Reach out to professionals in your field through LinkedIn or other social media platforms.
Career center
Learners who complete Algorithmic Thinking (Part 2) will develop knowledge and skills
that may be useful to these careers:
Software Engineer
Software Engineer
Software Engineers design, develop, and test software applications. They may work on a variety of projects, from small personal apps to large enterprise systems. Algorithmic Thinking (Part 2) can be a valuable course for Software Engineers because it teaches advanced algorithmic techniques that can help them develop more efficient and effective software solutions. The course also covers topics such as sorting, searching, and big-O notation, which are essential for understanding the performance of software algorithms.
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and computer science to extract insights from data. They may work on a variety of projects, such as developing predictive models, analyzing customer data, or identifying fraud. Algorithmic Thinking (Part 2) can be a helpful course for Data Scientists because it teaches advanced algorithmic techniques that can help them develop more efficient and effective data analysis solutions. The course also covers topics such as dynamic programming and sequence alignment, which are important for understanding the performance of data analysis algorithms.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models. They may work on a variety of projects, such as developing self-driving cars, improving healthcare outcomes, or detecting fraud. Algorithmic Thinking (Part 2) can be a helpful course for Machine Learning Engineers because it teaches advanced algorithmic techniques that can help them develop more efficient and effective machine learning algorithms. The course also covers topics such as dynamic programming and sequence alignment, which are important for understanding the performance of machine learning algorithms.
Computer Scientist
Computer Scientist
Computer Scientists research and develop new computing technologies. They may work on a variety of projects, such as developing new programming languages, designing new computer architectures, or creating new algorithms. Algorithmic Thinking (Part 2) can be a helpful course for Computer Scientists because it teaches advanced algorithmic techniques that can help them develop more efficient and effective computing solutions. The course also covers topics such as dynamic programming and sequence alignment, which are important for understanding the performance of computing algorithms.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex problems in a variety of industries. They may work on projects such as improving supply chain efficiency, reducing costs, or optimizing production schedules. Algorithmic Thinking (Part 2) can be a helpful course for Operations Research Analysts because it teaches advanced algorithmic techniques that can help them develop more efficient and effective solutions to complex problems. The course also covers topics such as dynamic programming and sequence alignment, which are important for understanding the performance of operations research algorithms.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data and make investment decisions. They may work on a variety of projects, such as developing trading strategies, managing risk, or evaluating investments. Algorithmic Thinking (Part 2) can be a helpful course for Quantitative Analysts because it teaches advanced algorithmic techniques that can help them develop more efficient and effective financial analysis solutions. The course also covers topics such as dynamic programming and sequence alignment, which are important for understanding the performance of financial analysis algorithms.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. They may work on a variety of projects, such as analyzing customer data, identifying trends, or developing predictive models. Algorithmic Thinking (Part 2) can be a helpful course for Data Analysts because it teaches advanced algorithmic techniques that can help them develop more efficient and effective data analysis solutions. The course also covers topics such as dynamic programming and sequence alignment, which are important for understanding the performance of data analysis algorithms.
Business Analyst
Business Analyst
Business Analysts work with businesses to improve their processes and systems. They may work on a variety of projects, such as analyzing business requirements, developing new business strategies, or implementing new technologies. Algorithmic Thinking (Part 2) can be a helpful course for Business Analysts because it teaches advanced algorithmic techniques that can help them develop more efficient and effective business solutions. The course also covers topics such as dynamic programming and sequence alignment, which are important for understanding the performance of business analysis algorithms.
Systems Analyst
Systems Analyst
Systems Analysts design, develop, and implement computer systems. They may work on a variety of projects, such as developing new software applications, upgrading existing systems, or integrating new technologies. Algorithmic Thinking (Part 2) can be a helpful course for Systems Analysts because it teaches advanced algorithmic techniques that can help them develop more efficient and effective systems solutions. The course also covers topics such as dynamic programming and sequence alignment, which are important for understanding the performance of systems analysis algorithms.
Software Developer
Software Developer
Software Developers design, develop, and test software applications. They may work on a variety of projects, from small personal apps to large enterprise systems. Algorithmic Thinking (Part 2) can be a helpful course for Software Developers because it teaches advanced algorithmic techniques that can help them develop more efficient and effective software solutions. The course also covers topics such as sorting, searching, and big-O notation, which are essential for understanding the performance of software algorithms.
Web Developer
Web Developer
Web Developers design, develop, and maintain websites. They may work on a variety of projects, from small personal websites to large e-commerce sites. Algorithmic Thinking (Part 2) can be a helpful course for Web Developers because it teaches advanced algorithmic techniques that can help them develop more efficient and effective web solutions. The course also covers topics such as sorting, searching, and big-O notation, which are essential for understanding the performance of web algorithms.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. They may work on a variety of projects, from small personal databases to large enterprise databases. Algorithmic Thinking (Part 2) can be a helpful course for Database Administrators because it teaches advanced algorithmic techniques that can help them develop more efficient and effective database solutions. The course also covers topics such as sorting, searching, and big-O notation, which are essential for understanding the performance of database algorithms.
Network Administrator
Network Administrator
Network Administrators design, implement, and maintain computer networks. They may work on a variety of projects, from small personal networks to large enterprise networks. Algorithmic Thinking (Part 2) can be a helpful course for Network Administrators because it teaches advanced algorithmic techniques that can help them develop more efficient and effective network solutions. The course also covers topics such as sorting, searching, and big-O notation, which are essential for understanding the performance of network algorithms.
Information Security Analyst
Information Security Analyst
Information Security Analysts design, implement, and maintain information security systems. They may work on a variety of projects, from small personal security systems to large enterprise security systems. Algorithmic Thinking (Part 2) can be a helpful course for Information Security Analysts because it teaches advanced algorithmic techniques that can help them develop more efficient and effective security solutions. The course also covers topics such as sorting, searching, and big-O notation, which are essential for understanding the performance of security algorithms.
Computer Support Specialist
Computer Support Specialist
Computer Support Specialists provide technical support to users of computers and other electronic devices. They may work on a variety of projects, from troubleshooting hardware and software problems to installing and configuring new systems. Algorithmic Thinking (Part 2) can be a helpful course for Computer Support Specialists because it teaches advanced algorithmic techniques that can help them develop more efficient and effective solutions to technical problems. The course also covers topics such as sorting, searching, and big-O notation, which are essential for understanding the performance of computer support algorithms.
For more career information including salaries, visit:
OpenCourser.com/course/35h2pg/algorithmic
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Algorithmic Thinking (Part 2).
Provides a comprehensive overview of sorting and searching algorithms. It covers a wide range of topics, including sorting, searching, and graph algorithms.
This classic textbook comprehensive guide to the analysis and design of algorithms. It covers a wide range of topics, including sorting, searching, graph algorithms, and dynamic programming.
Save
Provides a comprehensive overview of algorithm design techniques, including divide-and-conquer and dynamic programming. It valuable resource for students who want to learn more about the theoretical foundations of algorithm design.
Provides a clear and concise introduction to dynamic programming. It covers a wide range of topics, including the knapsack problem, the traveling salesman problem, and the sequence alignment problem.
Provides a comprehensive overview of divide-and-conquer algorithms. It covers a wide range of topics, including sorting, searching, graph algorithms, and dynamic programming.
This textbook provides a comprehensive overview of algorithms and data structures for massive datasets. It covers a wide range of topics, including sorting, searching, graph algorithms, and dynamic programming.
This textbook provides a clear and concise introduction to algorithms. It covers a wide range of topics, including sorting, searching, graph algorithms, and dynamic programming.
This textbook provides a comprehensive overview of data structures and algorithms in Python. It covers a wide range of topics, including sorting, searching, graph algorithms, and dynamic programming.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/35h2pg/algorithmic
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Effort
4 weeks of study, 7-10 hours/week
Level
Intermediate
Via
Coursera
Institution
Rice University
Instructors
Luay Nakhleh
Scott Rixner
Joe Warren
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
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آذربایجان دیلی
This course belongs to a
collection
called
Fundamentals of Computing. It's comprised of seven courses:
- Algorithmic Thinking (Part 2) (viewing)
- Algorithmic Thinking (Part 1)
- An Introduction to Interactive Programming in Python (Part 2)
- The Fundamentals of Computing Capstone Exam
- Principles of Computing (Part 2)
- An Introduction to Interactive Programming in Python (Part 1)
- Principles of Computing (Part 1)
Good to know
Teaches more advanced algorithmic concepts, including divide-and-conquer and dynamic programming, for solving larger-scale problems
Involves hands-on implementation in Python to reinforce algorithmic thinking and problem-solving skills
Emphasizes the interaction between algorithms and real-world data structures through practical examples
Builds on the foundation of basic algorithmic concepts covered in Part 1 of the course, enabling learners to progress and refine their understanding
Suitable for students with a solid grasp of Python programming and basic algorithmic concepts
Requires proficiency in Python programming, which may pose a barrier for learners without prior coding experience
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