Our Courses

Data Science at Scale

Learn scalable data management, evaluate big data technologies, and design effective visualizations. This Specialization covers intermediate topics in data science. You will gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you’ll explore legal and ethical issues that arise in working with big data.

• Course by
• Self Paced
• English

Architecture, Algorithms, and Protocols of a Quantum Computer and Quantum Internet

Learn how a quantum computer can be operated: you will go through the basics of quantum algorithms, quantum error-correction, micro-architectures, compilers, and programming languages for quantum computing, and protocols for the quantum internet.

• Course by
• Self Paced
• English

Autonomous Mobile Robots

Basic concepts and algorithms for locomotion, perception, and intelligent navigation.

• Course by
• Self Paced
• English

Computing in Python IV: Objects & Algorithms

Learn about recursion, search and sort algorithms, and object-oriented programming in Python.

• Course by
• Self Paced
• English

Data Structures & Algorithms I: ArrayLists, LinkedLists, Stacks and Queues

Work with the principles of data storage in Arrays, ArrayLists & LinkedList nodes. Understand their operations and performance with visualizations. Implement low-level linear, linked data structures with recursive methods, and explore their edge cases. Extend these structures to the Abstract Data Types, Stacks, Queues and Deques.

• Course by
• English

Data Structures & Algorithms II: Binary Trees, Heaps, SkipLists and HashMaps

Become familiar with nonlinear and hierarchical data structures. Study various tree structures: Binary Trees, BSTs and Heaps. Understand tree operations and algorithms. Learn and implement HashMaps that utilize key-value pairs to store data. Explore probabilistic data structures like SkipLists. Course tools help visualize the structures and performance.

• Course by
• Self Paced
• English

Data Structures & Algorithms III: AVL and 2-4 Trees, Divide and Conquer Algorithms

Learn more complex tree data structures, AVL and (2-4) trees. Investigate the balancing techniques found in both tree types. Implement these techniques in AVL operations. Explore sorting algorithms with simple iterative sorts, followed by Divide and Conquer algorithms. Use the course visualizations to understand the performance.

• Course by
• Self Paced
• English

Essentials of Genomics and Biomedical Informatics

This course presents clinicians and digital health enthusiasts with an overview of the data revolution in medicine and how to exploit it for research and in the clinic. The course will not make you a bioinformatician but will introduce the main concepts, tools, algorithms, and databases in this field.

• Course by
• English

Introduction to Computer Science and Programming

The term “Computation” refers to the action performed by a computer. A computation can be a basic operation and it can also be a sophisticated computer simulation requiring a large amount of data and substantial resources. This course aims at introducing learners with no prior knowledge to the basic key concepts of computer science. By following the lectures and exercises of this course, you will gain an understanding of algorithms by programming using the language Ruby.

• Course by
• English

Algorithmic Design and Techniques

Learn how to design algorithms, solve computational problems and implement solutions efficiently.

• Course by
• Self Paced
• English

Graph Algorithms

Learn how to use algorithms to explore graphs, compute shortest distance, min spanning tree, and connected components.

• Course by
• Self Paced
• English

String Processing and Pattern Matching Algorithms

Learn about pattern matching and string processing algorithms and how they apply to interesting applications.

• Course by
• Self Paced
• English

Graph Algorithms in Genome Sequencing

Learn how graphs are used to assemble millions of pieces of DNA into a contiguous genome and use these genomes to construct a Tree of Life.

• Course by
• Self Paced
• English

Algorithms and Data Structures Capstone

Synthesize your knowledge of algorithms and biology to build your own software for solving a biological challenge.

• Course by
• Self Paced
• English

Big Data Fundamentals

Learn how big data is driving organisational change and essential analytical tools and techniques, including data mining and PageRank algorithms.

• Course by
• English

Optimization: principles and algorithms - Linear optimization

Introduction to linear optimization, duality and the simplex algorithm.

• Course by
• Self Paced
• 40
• English

Optimization: principles and algorithms - Unconstrained nonlinear optimization

Introduction to unconstrained nonlinear optimization, Newton’s algorithms and descent methods.

• Course by
• Self Paced
• 27
• English

Data Structures & Algorithms Using C++

• Course by
• Self Paced
• 6
• English

Optimization: principles and algorithms - Network and discrete optimization

Introduction to network optimization and discrete optimization

• Course by
• Self Paced
• 20
• English

Mathematical Methods for Data Analysis

Learn mathematical methods for data analysis including mathematical formulations and computational methods. Some well-known machine learning algorithms such as k-means are introduced in the examples.

• Course by
• Self Paced
• 21
• English

PyTorch Basics for Machine Learning

This course is the first part in a two part course and will teach you the fundamentals of PyTorch. In this course you will implement classic machine learning algorithms, focusing on how PyTorch creates and optimizes models. You will quickly iterate through different aspects of PyTorch giving you strong foundations and all the prerequisites you need before you build deep learning models.

• Course by
• Self Paced
• English

Principles of Data Science Ethics

Concern about the harmful effects of machine learning algorithms and AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics.

This data science ethics course for both practitioners and managers provides guidance and practical tools to build better models and avoid these problems. The course offers a framework data scientists can use to develop their projects and an audit process to follow in reviewing them. Case studies with Python code are provided.

• Course by
• Self Paced
• English

Digital Marketing and Data Driven Analytics

Digital Marketing is the backbone element which uses platforms and digital technologies, such as any kind of device to maintain connected to the users. Once the users are part of this ecosystem, the companies convert this random data in accurate algorithms to improve the effectiveness of any marketing campaign.

• Course by
• Self Paced
• English

Accelerated Computer Science Fundamentals

Topics covered by this Specialization include basic object-oriented programming, the analysis of asymptotic algorithmic run times, and the implementation of basic data structures including arrays, hash tables, linked lists, trees, heaps and graphs, as well as algorithms for traversals, rebalancing and shortest paths. This Specialization sequence is designed to help prospective applicants prepare for the flexible and affordable Online Master of Computer Science (MCS) and MCS in Data Science.

• Course by
• Self Paced
• English

ESG Investing: Financial Decisions in Flux

As ESG investing continues to evolve towards a global standard, certain initiatives such as the UN’s sustainable development goals, and the Paris Agreement on climate change, have already spurred significant changes across the financial markets. As the title of this specialization suggests, financial decisions by investors, as well as capital deployment by companies, organizations, and governments, have been shifting amid increasing attention to environmental, social, and governance-related concerns. By the end of this specialization, students with basic knowledge of traditional financial pr

• Course by
• Self Paced
• English