This course introduces the Dynamic Distributed Dimensional Data Model (D4M), a breakthrough in computer programming that combines graph theory, linear algebra, and databases to address problems associated with Big Data. Search, social media, ad placement, mapping, tracking, spam filtering, fraud detection, wireless communication, drug discovery, and bioinformatics all attempt to find items of interest in vast quantities of data. This course teaches a signal processing approach to these problems by combining linear algebraic graph algorithms, group theory, and database design. This approach has been implemented in software. The class will begin with a number of practical problems, introduce the appropriate theory, and then apply the theory to these problems. Students will apply these ideas in the final project of their choosing. The course will contain a number of smaller assignments which will prepare the students with appropriate software infrastructure for completing their final projects.

IDS.410J Modeling and Assessment for Policy explores how scientific information and quantitative models can be used to inform policy decision-making. Students will develop an understanding of quantitative modeling techniques and their role in the policy process through case studies and interactive activities. The course addresses issues such as analysis of scientific assessment processes, uses of integrated assessment models, public perception of quantitative information, methods for dealing with uncertainties, and design choices in building policy-relevant models. Examples used in this class focus on models and information used in earth system governance.

Access and explore large datasets from the National Health and Nutrition Examination Survey (NHANES, 2003). Working with large datasets that emphasize exploration, finding patterns, and modeling is an essential first step in becoming fluent with data. This activity is a great place for students to start, since the dataset is straightforward and students can decide on the data they want to explore, including height, age, weight, and many other health-related attributes. Students begin by selecting and then investigating subsets of the dataset, for example, to find the cholesterol level of U.S. citizens. Then, working with their classmates or individually, students can try their own data science challenges, such as finding health trends in a subset of Americans by their household income, age, or marital status, etc.

Networks are ubiquitous in our modern society. The World Wide Web that links us to the rest of the world is the most visible example. But it is only one of many networks in which we are situated. Our social life is organized around networks of friends and colleagues. These networks determine our information, influence our opinions, and shape our political attitudes. They also link us, often through weak but important ties, to everybody else in the United States and in the world. 
This course will introduce the tools for the study of networks. It will show how certain common principles permeate the functioning of these diverse networks and how the same issues related to robustness, fragility, and interlinkages arise in many different types of networks.

Les vidéos 1080 ont pour objectif de vulgariser le savoir scientifique à destination des étudiants, des journalistes, des chercheurs de tous domaines et du grand public.

This is a path for those of you who want to complete the Data Science undergraduate curriculum on your own time, for free, with courses from the best universities in the World. In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind. OSSU Data Science uses the report Curriculum Guidelines for Undergraduate Programs in Data Science (https://www.amstat.org/asa/files/pdfs/EDU-DataScienceGuidelines.pdf) as our guide for course recommendation.

It is possible to finish within about 2 years if you plan carefully and devote roughly 20 hours/week to your studies. Learners can use this spreadsheet (linked in resource) to estimate their end date. Make a copy and input your start date and expected hours per week in the Timeline sheet. As you work through courses you can enter your actual course completion dates in the Curriculum Data sheet and get updated completion estimates.

Python and R are heavily used in Data Science community and our courses teach you both. Remember, the important thing for each course is to internalize the core concepts and to be able to use them with whatever tool (programming language) that you wish.

The Data Science curriculum assumes the student has taken high school math and statistics.

OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League

This is a course on the design and implementation of operating systems and their use as a foundation for systems programming. Topics covered include virtual memory; file systems; threads; context switches; kernels; interrupts; system calls; and interprocess communication, coordination, and interaction between software and hardware. A multi-processor operating system for RISC-V, xv6, is used to illustrate these topics. Individual laboratory assignments involve extending the xv6 operating system, for example to support sophisticated virtual memory features and networking.

Principles of Computer System Design: An Introduction is published in two parts. Part I, containing chapters 1-6, is a traditional printed textbook published by Morgan Kaufman, an imprint of Elsevier. Part II, containing chapters 7-11, is available here as an open educational resource.
This textbook, an introduction to the principles and abstractions used in the design of computer systems, is an outgrowth of notes written for 6.033 Computer System Engineering over a period of 40-plus years. Individual chapters are also used in other EECS subjects. There is also a web site for the current 6.033 class with a lecture schedule that includes daily assignments, lecture notes, and lecture slides. The 6.033 class Web site also contains a thirteen-year archive of class assignments, design projects, and quizzes.

Project Assignment for the course "CSC 217: Probability and statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

The ability to quantify the uncertainty in our models of nature is fundamental to many inference problems in Science and Engineering. In this course, we study advanced methods to represent, sample, update and propagate uncertainty. This is a “hands on” course: Methodology will be coupled with applications. The course will include lectures, invited talks, discussions, reviews and projects and will meet once a week to discuss a method and its applications.

RAISE (Responsible AI for Social Empowerment and Education) is a new MIT-wide initiative headquartered in the MIT Media Lab and in collaboration with the MIT Schwarzman College of Computing and MIT Open Learning. MIT researchers continually develop curriculum modules and associated teaching materials that are available to all K-12 educators for free under a Creative Commons license.

This course was developed and taught by Ben Marwick, Professor of Archaeology at University of Washington. It is a requirement for the UW Master of Science in Data Science, introduces students to the principles and tools for computational reproducibility in data science using R. Topics covered include acquiring, cleaning and manipulating data in a reproducible workflow using the tidyverse. Students will use literate programming tools, and explore best practices for organizing data analyses. Students will learn to write documents using R markdown, compile R markdown documents using knitr and related tools, and publish reproducible documents to various common formats. Students will learn strategies and tools for packaging research compendia, dependency management, and containerising projects to provide computational isolation.

This is the website for “R for Data Science”. This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.

Introduces the fundamental algorithmic approaches for creating robot systems that can autonomously manipulate physical objects in unstructured environments such as homes and restaurants. Topics include perception (including approaches based on deep learning and approaches based on 3D geometry), planning (robot kinematics and trajectory generation, collision-free motion planning, task-and-motion planning, and planning under uncertainty), as well as dynamics and control (both model-based and learning-based).
Homework assignments will guide students through building a software stack that will enable a robotic arm to autonomously manipulation objects in cluttered scenes (like a kitchen). A final project will allow students to dig deeper into a specific aspect of their choosing. The class has hardware available for ambitious final projects, but will also make heavy use of simulation using cloud resources.

The Software Tools for Academics and Researchers (STAR) program at MIT seeks to bridge the divide between scientific research and the classroom. Understanding and applying research methods in the classroom setting can be challenging due to time constraints and the need for advanced equipment and facilities. The multidisciplinary STAR team collaborates with faculty from MIT and other educational institutions to design software exploring core scientific research concepts. The goal of STAR is to develop innovative and intuitive teaching tools for classroom use.
All of the STAR educational tools are freely available. To complement the educational software, the STAR website contains curriculum components/modules which can facilitate the use of STAR educational tools in a variety of educational settings. Students, teachers, and professors should feel welcome to download software and curriculum modules for their own use.
Online Publication

This subject exposes students to a variety of visualization techniques so that they learn to understand the work involved in producing them and to critically assess the power and limits of each. Students concentrate on areas where visualizations are crucial for meaning making and data production. Drawing on scholarship in science and technology studies on visualization, critical art theory, and core discussions in science and engineering, students work through a series of case studies in order to become better readers and producers of visualizations.

Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative of the MIT Schwarzman College of Computing, works to train students and facilitate research to assess the broad challenges and opportunities associated with computing, and improve design, policy, implementation, and impacts.
This site is a resource for SERC pedagogical materials developed for use in MIT courses. SERC brings together cross-disciplinary teams of faculty, researchers, and students to develop original pedagogical materials that meet our goal of training students to practice responsible technology development through incorporation of insights and methods from the humanities and social sciences, including an emphasis on social responsibility.
Materials include the MIT Case Studies Series in Social and Ethical Responsibilities of Computing, original Active Learning Projects, and lecture materials that provide students hands-on practice and training in SERC, together with other resources and tools found useful in education at MIT. Original homework assignments and in-class demonstrations are specially created by multidisciplinary teams, to enable instructors to embed SERC-related material into a wide variety of existing courses.
The aim of SERC is to facilitate the development of responsible “habits of mind and action” for those who create and deploy computing technologies, and fostering the creation of technologies in the public interest.

Stebbins is a game about evolution. Students collect data as predators “eating” colored circles on a colored background, being careful to avoid the poisonous ones. Data analysis reveals how the population changes color over time, and can be used to illuminate a common misconception that individuals change in response to predation. Stebbins is modeled on a non-digital game-like simulation of natural selection created by evolutionary biologist G. Ledyard Stebbins.

In Stella, students act as astronomers, studying stars in a “patch” of sky in our own galaxy. Using simulated data from spectroscopy and other real-world instrumentation, students learn to determine star positions, radial velocity, proper motion, and ultimately, degree of parallax. As students establish their expertise in each area, they earn “badges” that allow them greater and easier access to the data. The Teacher Guide includes background on stellar spectroscopy (the brightness of a star), photometry (the breakdown of light from a star), and astrometry (measuring the positions of stars).