Introduction to Data Science, Columbia University | Blog to document and reflect on Columbia Data Science Class.
Introduction to Infographics and Data Visualization, January 2013. This course is an introduction to the basics of the visual representation of data. In this class you will learn how to design successful charts and maps, and how to arrange them to compose cohesive storytelling pieces. We will also discuss ethical issues when designing graphics, and how the principles of Graphic Design and of Interaction Design apply to the visualization of information. The course will have a theoretical component, as we will cover the main rules of the discipline, and also a practical one, as you will learn how to use Adobe Illustrator to design basic infographics and mock ups for interactive visualizations. You do not need any previous experience in infographics and visualization to take this course. With the readings, video lectures and tutorials available through the course, you will acquire enough skills to start producing compelling simple infographics almost right away.
Columbia Data Science course, Fall 2012. Introduction to Data Science. Schedule | CS 194-16: Introduction to Data Science.
Data Science Courses | Data Science London. Learning From Data - Online Course. A real Caltech course, not a watered-down version on YouTube & iTunes Free, introductory Machine Learning online course (MOOC) Taught by Caltech Professor Yaser Abu-Mostafa [article]Lectures recorded from a live broadcast, including Q&APrerequisites: Basic probability, matrices, and calculus8 homework sets and a final examDiscussion forum for participantsTopic-by-topic video library for easy review Outline This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications.
ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. What is learning? Live Lectures This course was broadcast live from the lecture hall at Caltech in April and May 2012. The Learning Problem - Introduction; supervised, unsupervised, and reinforcement learning. Is Learning Feasible? The Linear Model I - Linear classification and linear regression. Error and Noise - The principled choice of error measures. ISchool 296A Spring 2012. Course Description The seminar explores - Leading-edge trends in Data Science and Analytics at Silicon Valley and tech firms The speakers will include executives, entrepreneurs, and researchers from leading firms.
The topics covered will include (a subset of): - Data Analytics and Big Data - Machine Learning and scalability - Business Analytics including Online Marketing and Advertising, Financial Services and Risk Analytics, Operational and Service Analytics - Information Retrieval (Search) - Information Extraction - Social Networks and Social Media - Healthcare Analytics - Energy Analytics The seminar will cover the following aspects: - The types of problems being addressed in data science and analytics, the component methods and technologies being developed, and fruitful areas for research and entrepreneurial efforts - This requires attendance and participation in the seminar series and is open to the broader student and faculty community Prerequisites: None Units: 2 Course Objectives 1. 2. 1. 2. 1.
Data Mining | Sloan School of Management. School of Data - Evidence is Power. Coursera.org. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. 36-350, Statistical Computing, Fall 2012.