Main Page - Wiki Course Notes. Learning From Data. Yaser S. Abu-Mostafa. Free Online Courses From Top Universities. About the Course Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive.
It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). The course explains the key analysis techniques in process mining. Course Syllabus This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. The course covers the three main types of process mining. The first type of process mining is discovery. Introduction to Computational Thinking and Data Science. *Note - This is an Archived course* 6.00.2x is aimed at students with some prior programming experience in Python and a rudimentary knowledge of computational complexity.
We have chosen to focus on breadth rather than depth. The goal is to provide students with a brief introduction to many topics, so that they will have an idea of what’s possible when the time comes later in their career to think about how to use computation to accomplish some goal. That said, it is not a “computation appreciation” course. Students will spend a considerable amount of time writing programs to implement the concepts covered in the course.
This is a past/archived course. Specialization. Specialization. Specialization. Upcoming session: Nov 7 — Dec 12.
About the Course Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively.
The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales. In this course, you will learn the landscape of relevant systems, the principles on which they rely, their tradeoffs, and how to evaluate their utility against your requirements. 20 cursos gratis de Estadísticas y Análisis de datos.
Las mejores universidades del mundo nos ofrecen una veintena de cursos online gratis de Estadísticas y Análisis de datos.
¡Aquí toda la información que necesitas saber! Hoy vamos a echar un vistazo a la agenda de cursos universitarios que las universidades más rankeadas del extranjero han programado para este mes. Hablamos de una importante selección de contenidos educativos que abordan distintos temas como estadísticas, análisis de datos, recolección de datos, técnicas estadísticas, herramientas algorítmicas, y mucho más. Por otro lado, también debemos destacar la participación activa de diferentes casas de estudio superior como la Universidad Nacional Autónoma de México, el Tecnológico de Monterrey, la Universidad de California, la Universidad Wesleyana, la Universidad Duke, la Universidad de Michigan, la Universidad de Amsterdam, entre otros. Class Material. Lectures: You can obtain all the lecture slides at any point by cloning 2015, and using git pull as the weeks go on.
Videos: You can see the entire list of videos here. Below we list them by class/section along with a link to the slides. We’ve also started a YouTube channel for cs109. This channel has smaller videos dealing with nitty gritty stuff on the course. Labs/Sections: Note that the Lab video and notebook is actually recorded and produced on the Thursday and Friday of the previous week, but is listed under the week that sections pertaining to the material on the Lab are given. Week 1 (Mon Aug 31 - Fri Sep 4) Lecture 1: Course Overview Week 2 (Mon Sep 7 - Fri Sep 11) Lab 1: Pandas, Python, and Github Repository: 2015Lab1 Video: HD (recommended), SD, Captions, Mobile Lecture 2: Repository: 2015. Free Data Science Courses. Free Data Science Courses The Little List of Free #DataScience Courses Free Online Data Science Courses & Data Science Training Click on the free data science courses links below: The Open Source Data Science Masters.