This course is part of the Data Science Foundations: Data Structures and Algorithms Specialization

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Sriram Sankaranarayanan

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Part of the Master of Science in Data Science degree
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Data Science Foundations: Data Structures and Algorithms SpecializationUniversity of Colorado Boulder

About this Course

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This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures.
Dynamic Programming, Greedy Algorithms can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

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Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Course 3 of 3 in the

Data Science Foundations: Data Structures and Algorithms Specialization

Advanced Level

Completion of previous courses. Calculus, probability theory: distributions, expectations and moments. Some programming experience with Python.

Approx. 37 hours to complete

English

What you will learn

Describe basic algorithm design techniques
Create divide and conquer, dynamic programming, and greedy algorithms
Understand intractable problems, P vs NP and the use of integer programming solvers to tackle some of these problems

Skills you will gain

Analysis of Algorithms
Algorithm Design
Python Programming
Data Structure Design
Intractability

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 3 of 3 in the

Data Science Foundations: Data Structures and Algorithms Specialization

Advanced Level

Completion of previous courses. Calculus, probability theory: distributions, expectations and moments. Some programming experience with Python.

Approx. 37 hours to complete

English

Instructor

Instructor rating

4.57/5 (20 Ratings)

Sriram Sankaranarayanan

Professor

Department of Computer Science

27,246 Learners

3 Courses

Offered by

University of Colorado Boulder

Start working towards your Master's degree

This course is part of the 100% online Master of Science in Data Science from University of Colorado Boulder. If you are admitted to the full program, your courses count towards your degree learning.

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Syllabus - What you will learn from this course

Week1

Week 1

11 hours to complete

Divide and Conquer Algorithms

11 hours to complete

9 videos (Total 231 min), 11 readings, 6 quizzes

9 videos

What Are Divide and Conquer Algorithms?7mMax Subarray Problem Using Divide and Conquer23mKaratsuba’s Multiplication Algorithm39m Master Method Revisited28mFFT Part 1: Introduction and Complex Numbers48mFFT Part 2: Definition and Interpretation of Discrete Fourier Transforms34mFFT Part 3: Divide and Conquer Algorithm for FFT22mApplication # 1 : Fast Polynomial Multiplication using FFT 8mApplication # 2: Data Analysis using FFT17m

11 readings

Welcome and Where to Find Help 10mReading: Important Prerequisites10mLogistics: Textbook and Readings10mOverview of Module 110mCLRS Chapter 410mCLRS Chapter 4.110mJupyter Notebook on Karatsuba's Algorithm10mCLRS Chapters 4.3 - 4.510mBasics of Complex Numbers10mFourier Transforms10mFast Fourier Transform10m

5 practice exercises

Max Subarray Problem30mKaratsuba's Multiplication Algorithm30mMaster Method30mComplex Numbers and Roots of Unity30mFFT Algorithm and Applications30m

Week2

Week 2

9 hours to complete

Dynamic Programming Algorithms

9 hours to complete

6 videos (Total 131 min), 6 readings, 6 quizzes

6 videos

Introduction to Dynamic Programming + Rod Cutting Problem30mRod Cutting Problem: Memoization23mCoin Changing Problem12mKnapsack Problem29mWhen Optimal Substructure Fails10mDynamic Programming: Longest Common Subsequence25m

6 readings

Overview of Module 210mRod Cutting Problem10mMemoization 10mCoin Changing Problem10mJupyter Notebook10mCLRS Chapter 15.410m

5 practice exercises

Rod Cutting Problem and Recurrence30mMemoization 30mCoinchanging Problem30mKnapsack Problem30mLongest Common Subsequence30m

Week3

Week 3

7 hours to complete

Greedy Algorithms

7 hours to complete

5 videos (Total 102 min), 4 readings, 4 quizzes

Week4

Week 4

11 hours to complete

Intractability and Supplement on Quantum Computing

11 hours to complete

9 videos (Total 288 min), 5 readings, 5 quizzes

9 videos

Decision Problems and Languages36mPolynomial Time Problems28mNP Definition29mNP Completeness and Reductions37mNP Complete Problems: Examples24mComputation and Physics30mQubits and Operations37mBell's Inequality21mGrover's Search Algorithm42m

5 readings

Overview of Module 410mCLRS Chapter 34 10mCLRS chapter 34.110mCLRS Chapter 34.2 and 34.310mCLRS Chapter 34.510m

4 practice exercises

Decision Problems and Languages30mPolynomial Time and Certificates30mNP Completeness Reductions30mNP Completeness Problems30m

Reviews

4.4

14 reviews

5 stars
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3 stars
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2 stars
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1 star
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TOP REVIEWS FROM DYNAMIC PROGRAMMING, GREEDY ALGORITHMS

by DMSep 20, 2021

Excellent. This course covers some difficult topics, but the lectures and homework assignments were superb and made them quite approachable.

by CSDec 6, 2022

This course save me time on learning the dynamic programming. I really love the 4-steps to construct the dynamic programming. It gives me the guideline when designing DP solution.

by AMSep 18, 2022

Great work from professor Sriram Sankaranarayanan explaining such complex material. I wish we could review more examples during the class (specially Dynamic Programming ones).

by YSJul 22, 2022

Excellent course! I really learned alot and enjoyed all the challenges and topics in your course. Thank you so much!

View all reviews

About the Data Science Foundations: Data Structures and Algorithms Specialization

Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, searching, and indexing. This course will teach the fundamentals of data structures and algorithms with a focus on data science applications. This specialization is targeted towards learners who are broadly interested in programming applications that process large amounts of data (expertise in data science is not required), and are familiar with the basics of programming in python. We will learn about various data structures including arrays, hash-tables, heaps, trees and graphs along with algorithms including sorting, searching, traversal and shortest path algorithms.

The courses in this specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Data Science Foundations: Data Structures and Algorithms

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Dynamic Programming, Greedy Algorithms

About this Course

What you will learn

Skills you will gain