Our Courses

Comparative Research Designs and Methods

Emile Durkheim, one of the founders of modern empirical social science, once stated that the comparative method is the only one that suits the social sciences. But Descartes already had reminded us that “comparaison n’est pas raison”, which means that comparison is not reason (or theory) by itself. This course provides an introduction and overview of systematic comparative analyses in the social sciences and shows how to employ this method for constructive explanation and theory building.

• Course by
• 10 hours
• English

Doğrusal Cebir I: Uzaylar ve İşlemciler / Linear Algebra I: Spaces and Operators

Bu ders doğrusal cebir ikili dizinin birincisidir. Doğrusal uzaylar kavramı, doğrusal işlemciler, matris gösterimleri ve denklem sistemlerinin hesaplanabilmesi için temel araçlar vb. konuları içermektedir.

• Course by
• Self Paced
• Turkish

Mathematics 1 Part 2: Integral calculus, algebra, and applications

The second in a series of two courses which help you to master mathematics fundamentals and build your quantitative skillset for progression in high-growth careers, or to use as step towards further study at undergraduate level.

• Course by
• Self Paced
• English

Linear Algebra - Foundations to Frontiers

Learn the mathematics behind linear algebra and link it to matrix software development.

• Course by
• Self Paced
• English

Advanced Linear Algebra: Foundations to Frontiers

Learn advanced linear algebra for computing.

• Course by
• Self Paced
• English

Linear Algebra I: Vectors and Linear Equations

This course provides an overview of bachelor-level linear algebra. You will review all the concepts and practice and refresh the skills related to vectors and linear equations.

• Course by
• Self Paced
• 27
• English

Linear Algebra II: Matrices and Linear Transformations

This course provides an overview of bachelor-level linear algebra. You will review all the concepts and practice and refresh the skills related to matrices and linear transformations.

• Course by
• Self Paced
• 21
• English

College Algebra

This College Algebra course will cover fundamental concepts of algebra required to interpret a variety of functions and equations.

This course is eligible for Audit and Verified Certificate only.

• Course by
• Self Paced
• 100
• English

A-level Mathematics for Year 12 - Course 1: Algebraic Methods, Graphs and Applied Mathematics Methods

Develop your thinking skills, fluency and confidence to aim for an A* in A-levelmaths and prepare for undergraduate STEM degrees.

• Course by
• Self Paced
• 12
• English

Introduction to Embedded Machine Learning

Machine learning (ML) allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware.

• Course by
• Self Paced
• 17 hours
• English

Algebra: Elementary to Advanced - Equations & Inequalities

This course is intended for students looking to create a solid algebraic foundation of fundamental mathematical concepts from which to take more advanced courses that use concepts from precalculus, calculus, probability, and statistics. This course will help solidify your computational methods, review algebraic formulas and properties, and apply these concepts model real world situations. This course is for any student who will use algebraic skills in future mathematics courses.

• Course by
• Self Paced
• 10 hours
• English

Probability & Statistics for Machine Learning & Data Science

Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises.

• Course by
• Self Paced
• 29 hours
• English

Algebra and Differential Calculus for Data Science

Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will teach you the most fundamental Calculus concepts that you will need for a career in Data Science without a ton of unnecessary proofs and techniques that you may never use. Consider this an expressway to Data Science with approachable methods and friendly concepts that will guide you to truly understanding the most important ideas in Differential Calculus.

• Course by
• Self Paced
• 8 hours
• English

Algebra: Elementary to Advanced - Polynomials and Roots

This course is the final course in a three part algebra sequence, In this course, students extend their knowledge of more advanced functions, and apply and model them using both algebraic and geometric techniques. This course enables students to make logical deductions and arrive at reasonable conclusions. Such skills are crucial in today's world. Knowing how to analyze quantitative information for the purpose of making decisions, judgments, and predictions is essential for understanding many important social and political issues.

• Course by
• Self Paced
• 9 hours
• English

Introduction to Linear Models and Matrix Algebra

Learn to use R programming to apply linear models to analyze data in life sciences.

• Course by
• Self Paced
• 12
• English

Classical Cryptosystems and Core Concepts

Welcome to Introduction to Applied Cryptography. Cryptography is an essential component of cybersecurity. The need to protect sensitive information and ensure the integrity of industrial control processes has placed a premium on cybersecurity skills in today’s information technology market. Demand for cybersecurity jobs is expected to rise 6 million globally by 2019, with a projected shortfall of 1.5 million, according to Symantec, the world’s largest security software vendor. According to Forbes, the cybersecurity market is expected to grow from $75 billion in 2015 to $170 billion by 2020.

• Course by
• Self Paced
• 12 hours
• English

Linear Algebra for Machine Learning and Data Science

Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises.

• Course by
• Self Paced
• 22 hours
• English

Fundamental Linear Algebra Concepts with Python

In this course, you'll be introduced to finding inverses and matrix algebra using Python. You will also practice using row reduction to solve linear equations as well as practice how to define linear transformations. Let's get started!

• Course by
• Self Paced
• 11 hours
• English

Analytical Mechanics for Spacecraft Dynamics

This course is part 2 of the specialization Advanced Spacecraft Dynamics and Control. It assumes you have a strong foundation in spacecraft dynamics and control, including particle dynamics, rotating frame, rigid body kinematics and kinetics. The focus of the course is to understand key analytical mechanics methodologies to develop equations of motion in an algebraically efficient manner. The course starts by first developing D’Alembert’s principle and how the associated virtual work and virtual displacement concepts allows us to ignore non-working force terms.

• Course by
• Self Paced
• 32 hours
• English

Physics 101 - Rotational Motion and Gravitation

This third course serves as an introduction to the physics of rotational motion and gravitation. Upon completion, learners will understand how mathematical laws and conservation principles describe the motions and interactions of objects around us. Learners will gain experience in solving physics problems with tools such as graphical analysis, algebra, vector analysis, and calculus. This third course covers Rotational motion, Angular Momentum, and Gravitation.

• Course by
• Self Paced
• 28 hours
• English

Introduction to Deep Learning

Deep Learning is the go-to technique for many applications, from natural language processing to biomedical. Deep learning can handle many different types of data such as images, texts, voice/sound, graphs and so on. This course will cover the basics of DL including how to build and train multilayer perceptron, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders (AE) and generative adversarial networks (GANs).

• Course by
• Self Paced
• 60 hours
• English

Remote Sensing Image Acquisition, Analysis and Applications

Welcome to Remote Sensing Image Acquisition, Analysis and Applications, in which we explore the nature of imaging the earth's surface from space or from airborne vehicles. This course covers the fundamental nature of remote sensing and the platforms and sensor types used. It also provides an in-depth treatment of the computational algorithms employed in image understanding, ranging from the earliest historically important techniques to more recent approaches based on deep learning.

• Course by
• Self Paced
• 23 hours
• English

Physics 102 - Electric Potential and DC Circuits

This second course serves as an introduction to the physics of electricity and magnetism. Upon completion, learners will understand how mathematical laws and conservation principles describe fields and how these fields are related to electrical circuits. Learners will gain experience in solving physics problems with tools such as graphical analysis, algebra, vector analysis, and calculus. This second course covers Electric Potential, Capacitance, Current, Resistors, and DC Circuits.

• Course by
• Self Paced
• 22 hours
• English

Digital Signal Processing 1: Basic Concepts and Algorithms

Digital Signal Processing is the branch of engineering that, in the space of just a few decades, has enabled unprecedented levels of interpersonal communication and of on-demand entertainment. By reworking the principles of electronics, telecommunication and computer science into a unifying paradigm, DSP is a the heart of the digital revolution that brought us CDs, DVDs, MP3 players, mobile phones and countless other devices. In this series of four courses, you will learn the fundamentals of Digital Signal Processing from the ground up.

• Course by
• Self Paced
• 29 hours
• English

Introduction to Linear Algebra and Python

This course is the first of a series that is designed for beginners who want to learn how to apply basic data science concepts to real-world problems. You might be a student who is considering pursuing a career in data science and wanting to learn more, or you might be a business professional who wants to apply some data science principles to your work. Or, you might simply be a curious, lifelong learner intrigued by the powerful tools that data science and math provides. Regardless of your motivation, we’ll provide you with the support and information you need to get started.

• Course by
• Self Paced
• 12 hours
• English