Coursera

Algorithmic Thinking (Part 2) (Coursera)

Offered by Rice University,
Algorithmic Thinking (Part 2) (Coursera)

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.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

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.
Course 6 of 7 in the Fundamentals of Computing Specialization

Syllabus

WEEK 1
Module 3 -Core Materials
Sorting, searching, big-O notation, the Master Theorem

WEEK 2
Module 3 -Project and Application
Closest pairs of points, clustering of points, comparison of clustering algorithms

WEEK 3
Module 4 -Core Materials
Dynamic programming, running time of DP algorithms, local and global sequence alignment

WEEK 4
Module 4 -Project and Application
Computation of sequence alignments, applications to genomics and text comparison

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

System Validation (2): Model process behaviour (Coursera) Coursera
EIT Digital

System Validation (2): Model process behaviour (Coursera)

CS: Software Engineering

System Validation is the field that studies the fundamentals of system communication and information processing. It is the next logical step in computer science and improving software development in general. It allows automated analysis based on behavioural models of a system to see if a system works correctly. We want to guarantee that the systems does exactly what it is supposed to do.

Aug 17th 2026
3 Weeks
Computer Science Software Development System Validation
Details
Accounting Data Analytics with Python (Coursera) Coursera
University of Illinois at Urbana-Champaign

Accounting Data Analytics with Python (Coursera)

Statistics & Data Analysis Data Science

This course focuses on developing Python skills for assembling business data. It will cover some of the same material from Introduction to Accounting Data Analytics and Visualization, but in a more general purpose programming environment (Jupyter Notebook for Python), rather than in Excel and the Visual Basic Editor. These concepts are taught within the context of one or more accounting data domains (e.g., financial statement data from EDGAR, stock data, loan data, point-of-sale data).

Aug 17th 2026
5-12 Weeks
Python SQL Linear Regression
Details
Applied Text Mining in Python (Coursera) Coursera
University of Michigan

Applied Text Mining in Python (Coursera)

Statistics & Data Analysis Data Science

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling).

Aug 17th 2026
4 Weeks
Programming Python Machine Learning
Details
Llama for Python Programmers (Coursera) Coursera
University of Michigan

Llama for Python Programmers (Coursera)

CS: Software Engineering CS: Programming

Llama for Python Programmers is designed for programmers who want to leverage the Llama 2 large language model (LLM) and take advantage of the generative artificial intelligence (AI) revolution. In this course, you’ll learn how open-source LLMs can run on self-hosted hardware, made possible through techniques such as quantization by using the llama.cpp package.

Aug 17th 2026
3 Weeks
Python Python Programming Coursera Plus
Details
Problem Solving Using Computational Thinking (Coursera) Coursera
University of Michigan

Problem Solving Using Computational Thinking (Coursera)

CS: Theory Computer Science

Have you ever heard that computers "think"? Believe it or not, computers really do not think. Instead, they do exactly what we tell them to do. Programming is, "telling the computer what to do and how to do it." Before you can think about programming a computer, you need to work out exactly what it is you want to tell the computer to do. Thinking through problems this way is Computational Thinking. Computational Thinking allows us to take complex problems, understand what the problem is, and develop solutions. We can present these solutions in a way that both computers and people can understand.

Aug 17th 2026
5-12 Weeks
Programming Algorithms Computational Thinking
Details
Algorithms, Part II (Coursera) Coursera
Princeton University

Algorithms, Part II (Coursera)

CS: Software Engineering CS: Theory

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms.

Aug 17th 2026
5-12 Weeks
Java Algorithms Data Structures
Details
An Introduction to Interactive Programming in Python (Part 1) (Coursera) Coursera
Rice University

An Introduction to Interactive Programming in Python (Part 1) (Coursera)

CS: Programming

This two-part course is designed to help students with very little or no computing background learn the basics of building simple interactive applications. Our language of choice, Python, is an easy-to learn, high-level computer language that is used in many of the computational courses offered on Coursera. To make learning Python easy, we have developed a new browser-based programming environment that makes developing interactive applications in Python simple.

Aug 10th 2026
5-12 Weeks
Programming Python Interactive Applications
Details
Practical Steps for Building Fair AI Algorithms (Coursera) Coursera
Fred Hutchinson Cancer Center

Practical Steps for Building Fair AI Algorithms (Coursera)

Computer Science

Algorithms increasingly help make high-stakes decisions in healthcare, criminal justice, hiring, and other important areas. This makes it essential that these algorithms be fair, but recent years have shown the many ways algorithms can have biases by age, gender, nationality, race, and other attributes. This course will teach you ten practical principles for designing fair algorithms. It will emphasize real-world relevance via concrete takeaways from case studies of modern algorithms, including those in criminal justice, healthcare, and large language models like ChatGPT. You will come away with an understanding of the basic rules to follow when trying to design fair algorithms, and assess algorithms for fairness.

Aug 17th 2026
4 Weeks
Algorithms Algorithm Design Bias
Details