This course provides the fundamental computational toolbox for solving science and engineering problems. Topics include review of linear algebra, applications to networks, structures, estimation, finite difference and finite element solutions of differential equations, Laplace’s equation and potential flow, boundary-value problems, Fourier series, the discrete Fourier transform, and convolution. We will also explore many topics in AI and machine learning throughout the course.

This course provides a review of linear algebra, including applications to networks, structures, and estimation, Lagrange multipliers. Also covered are: differential equations of equilibrium; Laplace’s equation and potential flow; boundary-value problems; minimum principles and calculus of variations; Fourier series; discrete Fourier transform; convolution; and applications.
Note: This course was previously called “Mathematical Methods for Engineers I.”

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.

This course explores statistical modeling and control in manufacturing processes. Topics include the use of experimental design and response surface modeling to understand manufacturing process physics, as well as defect and parametric yield modeling and optimization. Various forms of process control, including statistical process control, run by run and adaptive control, and real-time feedback control, are covered. Application contexts include semiconductor manufacturing, conventional metal and polymer processing, and emerging micro-nano manufacturing processes.

This course will focus on fundamental subjects in convexity, duality, and convex optimization algorithms. The aim is to develop the core analytical and algorithmic issues of continuous optimization, duality, and saddle point theory using a handful of unifying principles that can be easily visualized and readily understood.

Course Description: This course will assist learners to find the math that is used in the real-world. Learners will learn how to solve real-life problems using mathematical stills they will acquire as they work through these lessons. By the end of this course, learners will be able to design their own project and discuss the math in it with their classmates.

Are you ready to leave the sandbox and go for the real deal? Have you followed Data Analysis: Take It to the MAX() and Data Analysis: Visualization and Dashboard Design and are ready to carry out more robust data analysis?

In this project-based course you will engage in a real data analysis project that simulates the complexity and challenges of data analysts at work. Testing, data wrangling, Pivot Tables, sparklines? Now that you have mastered them you are ready to apply them all and carry out an independent data analysis.

For your project, you will pick one raw dataset out of several options, which you will turn into a dashboard. You will begin with a business question that is related to the dataset that you choose. The datasets will touch upon different business domains, such as revenue management, call-center management, investment, etc.

Syllabus and Jupyter computer exercises created for the course Statistics and Data Analysis taught at the VU Amsterdam. The course is an introduction to data analysis at the BSc level and covers correlation, regression, factor analysis, cluster analysis, time series analysis and spatial analysis.

This course introduces methods for harnessing data to answer questions of cultural, social, economic, and policy interest. We will start with essential notions of probability and statistics. We will proceed to cover techniques in modern data analysis: regression and econometrics, design of experiments, randomized control trials (and A/B testing), machine learning, and data visualization.
We will illustrate these concepts with applications drawn from real-world examples and frontier research. Finally, we will provide instruction on the use of the statistical package R, and opportunities for students to perform self-directed empirical analyses.
MITx Online
This course draws materials from 14.310x Data Analysis for Social Scientists, which is part of the MicroMasters Program in Data, Economics, and Design of Policy offered by MITx Online. The MITx Online course is entirely free to audit, though learners have the option to pay a fee, which is based on the learner’s ability to pay, to take the proctored exam and earn a course certificate. To access that course, create an MITx Online account and enroll in the course 14.310x Data Analysis for Social Scientists.

This course is designed to introduce first-year Sloan MBA students to the fundamental techniques of using data. In particular, the course focuses on various ways of modeling, or thinking structurally about decision problems in order to make informed management decisions.

This page shares five units of youcubed lessons for grades 6-10 that introduce students (and teachers) to data science. The units start with an introduction to the concept of data and move to lessons that invite students to explore their own data sets. These lessons teach important content through a pattern-seeking, exploratory approach, and are designed to engage students actively.







Data Science LessonsThis page shares five units of youcubed lessons for grades 6-10 that introduce students (and teachers) to data science. The units start with an introduction to the concept of data and move to lessons that invite students to explore their own data sets.  These lessons teach important content through a pattern-seeking, exploratory approach, and are designed to engage students actively. The culminating unit is a citizen science project that gives students an opportunity to conduct a data inquiry. The lessons accompany a new online course for teachers, where some of the lessons are featured, along with other lesson ideas. These lessons are offered with ideas for in-person or online teaching, and can be taught at any time of year.









LessonsTeacher Online Course: 21st Century Teaching and LearningUnit 1: Data Is EverywhereUnit 2: Working With Data Analysis ToolsUnit 3: Measures of Center & SpreadUnit 4: Understanding VariabilityUnit 5: A Community Data Collection Project

ResourcesHigh School Data Science CourseCODAPWhat's Going On In This Graph?Data Science Initiative VideoThe Data Science K-12 MovementData Talks



Cool Extras




What are Data Talks?






A Picture Book Introduction to Data Science






Measures of Center and Spread Animated Movies






Stanford News Press Release

In an increasingly data-driven world, data and its use aren’t always all it’s cracked up to be. This course aims to address the critical lack of any or appropriate data in many areas where complex decisions need to be made.

For instance, how can you predict volcano activity when no eruptions have been recorded over a long period of time? Or how can you predict how many people will be resistant to antibiotics in a country where there is no available data at national level? Or how about estimating the time needed to evacuate people in flood risk areas?

In situations like these, expert opinions are needed to address complex decision-making problems. This course, aimed at researchers and professionals from any academic background, will show you how expert opinion can be used for uncertainty quantification in a rigorous manner.

Various techniques are used in practice. They vary from the informal and undocumented opinion of one expert to a fully documented and formal elicitation of a panel of experts, whose uncertainty assessments can be aggregated to provide support for complex decision making.

In this course you will be introduced to state-of-the-art expert judgment methods, particularly the Classical Model (CM) or Cooke’s method, which is arguably the most rigorous method for performing Structured Expert Judgment.

CM, developed at TU Delft by Roger Cooke, has been successfully applied for over 30 years in areas as diverse as climate change, disaster management, epidemiology, public and global health, ecology, aeronautics/aerospace, nuclear safety, environment and ecology, engineering and many others.

This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application. Topics include divide-and-conquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography.

This course covers the design, construction, and testing of field robotic systems, through team projects with each student responsible for a specific subsystem. Projects focus on electronics, instrumentation, and machine elements. Design for operation in uncertain conditions is a focus point, with ocean waves and marine structures as a central theme. Topics include basic statistics, linear systems, Fourier transforms, random processes, spectra, ethics in engineering practice, and extreme events with applications in design.

The main goal of this course is to give the students a solid foundation in the theory of elliptic and parabolic linear partial differential equations. It is the second semester of a two-semester, graduate-level sequence on Differential Analysis.

This is the first semester of a two-semester sequence on Differential Analysis. Topics include fundamental solutions for elliptic; hyperbolic and parabolic differential operators; method of characteristics; review of Lebesgue integration; distributions; fourier transform; homogeneous distributions; asymptotic methods.

In this course, we study elliptic Partial Differential Equations (PDEs) with variable coefficients building up to the minimal surface equation. Then we study Fourier and harmonic analysis, emphasizing applications of Fourier analysis. We will see some applications in combinatorics / number theory, like the Gauss circle problem, but mostly focus on applications in PDE, like the Calderon-Zygmund inequality for the Laplacian, and the Strichartz inequality for the Schrodinger equation. In the last part of the course, we study solutions to the linear and the non-linear Schrodinger equation. All through the course, we work on the craft of proving estimates.

Differential Equations are the language in which the laws of nature are expressed. Understanding properties of solutions of differential equations is fundamental to much of contemporary science and engineering. Ordinary differential equations (ODE’s) deal with functions of one variable, which can often be thought of as time.

The laws of nature are expressed as differential equations. Scientists and engineers must know how to model the world in terms of differential equations, and how to solve those equations and interpret the solutions. This course focuses on the equations and techniques most useful in science and engineering.
Course Format
This course has been designed for independent study. It provides everything you will need to understand the concepts covered in the course. The materials include:

Lecture Videos by Professor Arthur Mattuck.
Course Notes on every topic.
Practice Problems with Solutions.
Problem Solving Videos taught by experienced MIT Recitation Instructors.
Problem Sets to do on your own with Solutions to check your answers against when you’re done.
A selection of Interactive Java® Demonstrations called Mathlets to illustrate key concepts.
A full set of Exams with Solutions, including practice exams to help you prepare.

Content Development
Haynes Miller 
Jeremy Orloff 
Dr. John Lewis 
Arthur Mattuck