The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events Specialist
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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March 20, 2025
10 weeks, online
5-10 hours/week
Participants report that enrolling in a program with colleagues fosters collaborative learning and amplifies their impact.
Please provide your details to get more information about the group-enrollment pricing.
The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events SpecialistThere 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.
SOURCE: Forbes
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:
Each of the 10 modules focuses on a specific type of algorithm or data structure, encompassing an array of computational theories and applications.
Learn fundamental concepts of algorithm design, including:
Delve deeper into time complexity and learn to prove upper and lower bounds (worst and best possible runtime) of comparison-based algorithms, including:
Learn when to apply greedy algorithms, which are designed for rapid optimization, including:
Learn dynamic programming algorithms and explore their real-world applications, including:
Explore data structures, such as hash tables and data streams, and apply them to solve computational problems, including:
Learn the fundamental concepts of network flows and how to use max-flow algorithms to solve optimization problems, including:
Formulate and solve linear programming (LP) problems, including:
Understand the concepts of P (polynomial time problems) and NP, a wider class of problems that are potentially intractable, including:
Learn to formulate multiplicative weights algorithms (frequently used in prediction, learning, and optimization systems) and prove their correctness, including:
Learn the definition, core concepts, and applications of gradient descent (GD)—a method commonly used to solve optimization and machine learning problems, including:
Learn fundamental concepts of algorithm design, including:
Learn the fundamental concepts of network flows and how to use max-flow algorithms to solve optimization problems, including:
Delve deeper into time complexity and learn to prove upper and lower bounds (worst and best possible runtime) of comparison-based algorithms, including:
Formulate and solve linear programming (LP) problems, including:
Learn when to apply greedy algorithms, which are designed for rapid optimization, including:
Understand the concepts of P (polynomial time problems) and NP, a wider class of problems that are potentially intractable, including:
Learn dynamic programming algorithms and explore their real-world applications, including:
Learn to formulate multiplicative weights algorithms (frequently used in prediction, learning, and optimization systems) and prove their correctness, including:
Explore data structures, such as hash tables and data streams, and apply them to solve computational problems, including:
Learn the definition, core concepts, and applications of gradient descent (GD)—a method commonly used to solve optimization and machine learning problems, including:
Try-it Activities
Live Office Hours
Knowledge Checks
Discussion Board Activities
Programming Assignments
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.
Download BrochureYour 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.
David P. Woodruff
Anupam Gupta
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.
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.
After reviewing the information on the program landing page, we recommend you submit the short form above to gain access to the program brochure, which includes more in-depth information. If you still have questions on whether this program is a good fit for you, please email learner.success@emeritus.org, and a dedicated program advisor will follow-up with you very shortly.
Some programs do have prerequisites, particularly the more technical ones. This information will be noted on the program landing page, as well as in the program brochure. If you are uncertain about program prerequisites and your capabilities, please email us at the ID mentioned above.
Note that, unless otherwise stated on the program web page, all programs are taught in English and proficiency in English is required.
More than 50 percent of our participants are from outside the United States. Class profiles vary from one cohort to the next, but, generally, our online certificates draw a highly diverse audience in terms of professional experience, industry, and geography — leading to a very rich peer learning and networking experience.
Check back to this program web page or email us to inquire if future program dates or the timeline for future offerings have been confirmed yet.
Each program includes an estimated learner effort per week. This is referenced at the top of the program landing page under the Duration section, as well as in the program brochure, which you can obtain by submitting the short form at the top of this web page.
We have designed this program to fit into your current working life as efficiently as possible. Time will be spent among a variety of activities including:
The program is designed to be highly interactive while also allowing time for self-reflection and to demonstrate an understanding of the core topics through various active learning exercises. Please email us if you need further clarification on program activities.
More than 300,000 learners across 200 countries have chosen to advance their skills with Emeritus and its educational learning partners. In fact, 90 percent of the respondents of a recent survey across all our programs said that their learning outcomes were met or exceeded.
All the contents of the course would be made available to students at the commencement of the course. However, to ensure the program delivers the desired learning outcomes the students may appoint Emeritus to manage the delivery of the program in a cohort-based manner the cost of which is already included in the overall course fee of the course.
A dedicated program support team is available 24/5 (Monday to Friday) to answer questions about the learning platform, technical issues, or anything else that may affect your learning experience.
Peer learning adds substantially to the overall learning experience and is an important part of the program. You can connect and communicate with other participants through our learning platform.
Each program includes an estimated learner effort per week, so you can gauge what will be required before you enroll. This is referenced at the top of the program landing page under the Duration section, as well as in the program brochure, which you can obtain by submitting the short form at the top of this web page. All programs are designed to fit into your working life.
This program is scored as a pass or no-pass; participants must complete the required activities to pass and obtain the certificate of completion. Some programs include a final project submission or other assignments to obtain passing status. This information will be noted in the program brochure. Please email us if you need further clarification on any specific program requirements.
Upon successful completion of the program, you will receive a smart digital certificate. The smart digital certificate can be shared with friends, family, schools, or potential employers. You can use it on your cover letter, resume, and/or display it on your LinkedIn profile.
The digital certificate will be sent approximately two weeks after the program, once grading is complete.
No, only verified digital certificates will be issued upon successful completion. This allows you to share your credentials on social platforms such as LinkedIn, Facebook, and Twitter.
No, there is no alumni status granted for this program. In some cases, there are credits that count toward a higher level of certification. This information will be clearly noted in the program brochure.
You will have access to the online learning platform and all the videos and program materials for 12 months following the program start date. Access to the learning platform is restricted to registered participants per the terms of agreement.
Participants will need the latest version of their preferred browser to access the learning platform. In addition, Microsoft Office and a PDF viewer are required to access documents, spreadsheets, presentations, PDF files, and transcripts.
Yes, the learning platform is accessed via the internet, and video content is not available for download. However, you can download files of video transcripts, assignment templates, readings, etc. For maximum flexibility, you can access program content from a desktop, laptop, tablet, or mobile device.
Video lectures must be streamed via the internet, and any livestream webinars and office hours will require an internet connection. However, these sessions are always recorded, so you may view them later.
Yes, you can register up until seven days past the published start date of the program without missing any of the core program material or learnings.
The program fee is noted at the top of this program web page and usually referenced in the program brochure as well.
Yes, you can do the bank remittance in the program currency via wire transfer or debit card. Please contact your program advisor, or email us for details.
Yes! Please email us with the details of the program you are interested in, and we will assist you.
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Yes, the flexible payment option allows a participant to pay the program fee in installments. This option is made available on the payment page and should be selected before submitting the payment.
Please connect with us via email for assistance.
Emeritus collects all program payments, provides learner enrollment and program support, and manages learning platform services.
Program fees for Emeritus programs with Carnegie Mellon University’s School of Computer Science Executive Education may not be paid for with (a) funds from the GI Bill, the Post-9/11 Educational Assistance Act of 2008, or similar types of military education funding benefits or (b) Title IV financial aid funds.
For the program refund and deferral policy, please click the link here.
Flexible payment options available.