Introduction to Algorithms and Data Structures

The only prerequisites are high school algebra, comfort with mathematical arguments, and working knowledge of at least one programming language. Participants will use Python for the coding assignments, so some familiarity with Python and Jupyter Notebook is helpful, but not required.

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Course Dates

STARTS ON

TBD

Course Duration

DURATION

10 weeks, online
5-10 hours/week

Course Fee

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or get US$250 off with a referral

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Learn the Core Principles and Real-World Applications of Algorithm Design

There is a rapidly growing demand for technology professionals who understand the ways in which algorithms drive today’s world. The number of technical professionals who list “algorithms and data structures” among their skills is increasing by 25% year over year, according to LinkedIn Insights. Keep pace with this rapidly growing field by enrolling in Algorithms and Data Structures, an online program offered by Carnegie Mellon University's School of Computer Science Executive Education. Participants receive an in-depth understanding of the design principles behind real-world, problem-solving algorithms, as well as the data structures that support them.

"Carnegie Mellon is known for being at the forefront of technology. … The School of Computer Science is internationally recognized."

SOURCE: Forbes

Key Outcomes

Software engineers and developers, as well as early-career technology graduates and other IT professionals, will gain an in-depth understanding of algorithms and data structures. Over the course of 10 weeks, you will:

  • Explain the key concepts related to algorithms and data structures
  • Model computational problems and design algorithms
  • Apply standardized algorithmic building blocks
  • Analyze algorithms to verify correctness and efficiency
  • Explore real-world applications of algorithms and data structures
  • Practice implementing algorithms using Python

Program Modules

Each of the 10 modules focuses on a specific type of algorithm or data structure, encompassing an array of computational theories and applications.

Module 1:

Introduction to Algorithms

Learn fundamental concepts of algorithm design, including:

  • Illustrating the key components of an algorithm and the notations used for time complexity
  • Performing recurrence analysis and analyzing the time complexity of merge-sort and quick-select algorithms

Module 2:

Concrete Models and Tight Upper and Lower Bounds

Delve deeper into time complexity and learn to prove upper and lower bounds (worst and best possible runtime) of comparison-based algorithms, including:

  • Explaining the concept of concrete models as well as tight upper and lower bounds
  • Applying the information-theoretic and adversary techniques to prove upper and lower bounds of computational problems

Module 3:

Greedy Algorithms

Learn when to apply greedy algorithms, which are designed for rapid optimization, including:

  • Explaining what a greedy algorithm is and how to design such algorithms
  • Proving the optimality of greedy algorithms

Module 4:

Dynamic Programming

Learn dynamic programming algorithms and explore their real-world applications, including:

  • Developing and implementing dynamic programming
  • Comparing the bottom-up and the top-down approaches to dynamic programming.

Module 5:

Hashing and Streaming

Explore data structures, such as hash tables and data streams, and apply them to solve computational problems, including:

  • Examining the properties of hashing and applying it to the dynamic dictionary problems
  • Using hashing to solve problems on data streams

Module 6:

Network Flows

Learn the fundamental concepts of network flows and how to use max-flow algorithms to solve optimization problems, including:

  • Finding the maximum flow and minimum cut of a given network
  • Designing and implementing network flow algorithms to solve problems

Module 7:

Linear Programming

Formulate and solve linear programming (LP) problems, including:

  • Exploring LP solutions for the min-cut max-flow and the operations research problems
  • Applying LP algorithms, such as the Simplex algorithm

Module 8:

NP-Completeness

Understand the concepts of P (polynomial time problems) and NP, a wider class of problems that are potentially intractable, including:

  • Proving a problem is NP-complete
  • Developing approximation algorithms to solve NP-complete problems

Module 9:

Multiplicative Weights Algorithms

Learn to formulate multiplicative weights algorithms (frequently used in prediction, learning, and optimization systems) and prove their correctness, including:

  • Using the multiplicative weights framework to solve problems
  • Proving the correctness of multiplicative weight algorithms

Module 10:

Gradient Descent

Learn the definition, core concepts, and applications of gradient descent (GD)—a method commonly used to solve optimization and machine learning problems, including:

  • Examining the fundamentals of the GD framework and convexity
  • Implementing GD algorithms to solve convex optimization problems

Module 1:

Introduction to Algorithms

Learn fundamental concepts of algorithm design, including:

  • Illustrating the key components of an algorithm and the notations used for time complexity
  • Performing recurrence analysis and analyzing the time complexity of merge-sort and quick-select algorithms

Module 6:

Network Flows

Learn the fundamental concepts of network flows and how to use max-flow algorithms to solve optimization problems, including:

  • Finding the maximum flow and minimum cut of a given network
  • Designing and implementing network flow algorithms to solve problems

Module 2:

Concrete Models and Tight Upper and Lower Bounds

Delve deeper into time complexity and learn to prove upper and lower bounds (worst and best possible runtime) of comparison-based algorithms, including:

  • Explaining the concept of concrete models as well as tight upper and lower bounds
  • Applying the information-theoretic and adversary techniques to prove upper and lower bounds of computational problems

Module 7:

Linear Programming

Formulate and solve linear programming (LP) problems, including:

  • Exploring LP solutions for the min-cut max-flow and the operations research problems
  • Applying LP algorithms, such as the Simplex algorithm

Module 3:

Greedy Algorithms

Learn when to apply greedy algorithms, which are designed for rapid optimization, including:

  • Explaining what a greedy algorithm is and how to design such algorithms
  • Proving the optimality of greedy algorithms

Module 8:

NP-Completeness

Understand the concepts of P (polynomial time problems) and NP, a wider class of problems that are potentially intractable, including:

  • Proving a problem is NP-complete
  • Developing approximation algorithms to solve NP-complete problems

Module 4:

Dynamic Programming

Learn dynamic programming algorithms and explore their real-world applications, including:

  • Developing and implementing dynamic programming
  • Comparing the bottom-up and the top-down approaches to dynamic programming.

Module 9:

Multiplicative Weights Algorithms

Learn to formulate multiplicative weights algorithms (frequently used in prediction, learning, and optimization systems) and prove their correctness, including:

  • Using the multiplicative weights framework to solve problems
  • Proving the correctness of multiplicative weight algorithms

Module 5:

Hashing and Streaming

Explore data structures, such as hash tables and data streams, and apply them to solve computational problems, including:

  • Examining the properties of hashing and applying it to the dynamic dictionary problems
  • Using hashing to solve problems on data streams

Module 10:

Gradient Descent

Learn the definition, core concepts, and applications of gradient descent (GD)—a method commonly used to solve optimization and machine learning problems, including:

  • Examining the fundamentals of the GD framework and convexity
  • Implementing GD algorithms to solve convex optimization problems
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Program Experience

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Try-it Activities

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Live Office Hours

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Knowledge Checks

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Discussion Board Activities

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Programming Assignments

Who Should Attend

This program is designed for anyone with a STEM or computer science background who would like an in-depth understanding of general computational problems and their algorithms, as well as the data structures that support them. It focuses on the core principles used to design algorithms, prove their correctness, and analyze their complexity. By completing the program, you will add a valuable credential attesting to your understanding of real-world applications of algorithms and data structures. The program is particularly suitable for:

  • Early-career technology grads who would like to build their skills in a way that has a practical application in the jobs marketplace.
  • Software engineers and other technology professionals who want a hands-on understanding of advanced algorithms and data structures.
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Program Faculty

Faculty Member David P. Woodruff

David P. Woodruff

Associate Professor, Department of Computer Science, Carnegie Mellon University

David Woodruff is an associate professor in CMU's Computer Science Department. A recipient of the 2021 Herbert Simon Award for Teaching Excellence in Computer Science, he has taught algorithm programs since joining CMU in 2017... More info

Faculty Member Anupam Gupta

Anupam Gupta

Professor, Department of Computer Science, Carnegie Mellon University

Anupam Gupta is a professor in CMU's Computer Science Department. He taught Graduate Algorithms at CMU in the Spring 2021 semester. He is recipient of the 2019 Herbert Simon Award for Teaching Excellence in Computer Science... More info

Faculty Member David P. Woodruff

David P. Woodruff

Associate Professor, Department of Computer Science, Carnegie Mellon University

David Woodruff is an associate professor in CMU's Computer Science Department. A recipient of the 2021 Herbert Simon Award for Teaching Excellence in Computer Science, he has taught algorithm programs since joining CMU in 2017.

Over his professional career, David has been a member of the Visiting Faculty Program at Google's Mountain View Research Division; research scientist at the IBM Almaden Research Principles and Methodologies Group; and visiting scholar at the Tsinghua University Institute for Theoretical Computer Science. He is author of the book Sketching as a Tool for Numerical Linear Algebra and more than 200 articles for refereed publications. He holds a Ph.D. in computer science from the Massachusetts Institute of Technology.
Faculty Member Anupam Gupta

Anupam Gupta

Professor, Department of Computer Science, Carnegie Mellon University

Anupam Gupta is a professor in CMU's Computer Science Department. He taught Graduate Algorithms at CMU in the Spring 2021 semester. He is recipient of the 2019 Herbert Simon Award for Teaching Excellence in Computer Science

Anupam's research interests include theoretical computer science, with an emphasis on algorithms for uncertain environments and metric embeddings. He has received a CAREER Award and an Alfred P. Sloan Research Fellowship. In addition, he is a member of the Joint Indo-US Networked Center for Algorithms Under Uncertainty and has published more than 160 papers. He holds a Ph.D. in computer science from the University of California, Berkeley, and a B.Tech from the Indian Institute of Technology, Kanpur.

Certificate

Certificate

Upon successful completion of the program, participants will receive a verified digital certificate of completion from Carnegie Mellon University's School of Computer Science Executive Education. Participants must complete 60 percent of the required activities including a capstone project (if any) to obtain the certificate of completion. This is a training program and it is not eligible for academic credit.

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Your digital certificate will be issued in your legal name and emailed to you at no additional cost, upon completion of the program, per the stipulated requirements. All certificate images are for illustrative purposes only and may be subject to change at the discretion of Carnegie Mellon University's School of Computer Science Executive Education.

The Carnegie Mellon School of Computer Science Executive Education learning experience

At Carnegie Mellon’s Executive Education Program in the School of Computer Science, we provide organizations and people access to the skills and tools necessary to solve real world technical problems by equipping the next generation of technology leaders with the experience, insights and novel solutions developed by our community of computer science experts. From custom training programs to online individualized learning, our cutting-edge programming — backed by faculty who pioneered the field — takes your skillset to the next level, giving you the tools to tackle your company’s next great technological challenge.
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