Learn how to apply statistical modelling techniques to real-world business scenarios using Python. In this course, you will learn three predictive modelling techniques - linear and logistic regression, and naive Bayes - and their applications in real-world scenarios. The first half of the course focuses...

Mathematical modeling of fluid dynamics, convection, conduction, and phase transformations. Due to challenges arising from the COVID-19 pandemic, the start date for this course will be delayed to February 2, 2020. We apologize for the delay and hope to make this content available on the new start date...

Learn about effective supply chain strategies for companies that operate globally, with emphasis on how to plan and integrate supply chain components into a coordinated system. This course was formerly known as Supply Chains for Manufacturing I. A supply chain entails two or more parties that are linked...

Learn how to build predictive models using machine learning. This course will give you an overview of machine learning-based approaches for predictive modelling, including tree-based techniques, support vector machines, and neural networks using Python. These models form the basis of cutting-edge analytics...

How do investors, creditors, and other users analyze financial statements to assess corporate performance. Learn financial accounting, how to read financial statements, and input valuation models for better corporate finance decision-making. Accounting is the language of business. It is difficult to...

Apply your predictive modelling acumen in a business case setting. The final project brings together the skills and knowledge acquired throughout the MicroMasters programme. You will draw on your knowledge of data analysis techniques to demonstrate your capacity to deal effectively with current job market...

Develop a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing and prediction. -- Part of the MITx MicroMasters program in Statistics and Data Science. Statistics is the science of turning data into insights and ultimately decisions. Behind recent advances...

Become a UX data scientist! From qualitative data analysis to big data Web analytics, you will be able to leverage insights from data to make empirically-based recommendations. Do big data and UX speak to you? This MOOC will give you the methods and tools to analyze the whole spectrum of data we handle...

Learn the mathematical foundations essential for financial engineering and quantitative finance: linear algebra, optimization, probability, stochastic processes, statistics, and applied computational techniques in R. Modern finance is the science of decision making in an uncertain world, and its language...

Financial derivatives are ubiquitous in global capital markets. Students will obtain a sophisticated understanding of valuation methods; tools for quantifying, hedging, and speculating on risk; and a basic familiarity with major markets and instruments. Financial derivatives are ubiquitous in global...

This free 5-module online introductory course gives you the essential concepts, techniques and skills to effectively work with data and produce compelling data stories under tight deadlines. Comprising of video lectures, tutorials, assignments, readings and discussion forums, this course is open to...

The exercises we cover today will have you working directly with the Spark specific components of the AMPLab’s open-source software stack, called the Berkeley Data Analytics Stack (BDAS).

This three-week course provides a foundation for marketers and analysts seeking to understand the core principles of digital analytics and to improve business performance through better digital measurement. Course highlights include: * An overview of today’s digital measurement landscape * Guidance...

A series of sixteen lectures covering the core of the book "Information Theory, Inference, and Learning Algorithms (Cambridge University Press, 2003)" which can be bought at Amazon, and is available free online. A subset of these lectures used to constitute a Part III Physics course at the University...

Introductory course in machine learning by world leading expert Geoffrey Hinton. Topics include: linear regression and classification, neural networks, clustering, decision trees, gaussian processes, deep belief nets and more

In this course, we study neural networks of various types. Topics include: neural network architectures, perceptrons, the backpropagation algorithm, neuro-probabilistic language models, convolutional nets for digit recognition, mini-batch gradient descent, the momentum method, recursive neural networks...

Advanced course in machine learning by world leading expert Geoffrey Hinton. Topics include: graphical models, Restricted Boltzmann machines, Object Recognition in Deep Neural Nets, Recurrent neural networks, Non-linear Dimensionality Reduction and more.

The course is an interdisciplinary course, focused on the emerging science of complex networks and their applications. The material includes the mathematics of networks, their applications to biology, sociology, technology and other fields, and their use in the research of real complex systems in nature...

Learn to create a novel mobile application in this MIT course - from research to design, usability, implementation and field evaluation. How do you design a mobile app that truly changes people's lives? How can you understand how a new service is being used, both quantitatively and qualitatively? How...

Learn the methods and strategies for using large-scale educational data to improve education and make discoveries about learning. Online and software-based learning tools have been used increasingly in education. This movement has resulted in an explosion of data, which can now be used to improve educational...