This course is an introduction to data cleaning, analysis and visualization. We will teach the basics of data analysis through concrete examples. You will learn how to take raw data, extract meaningful information, use statistical tools, and make visualizations. This was offered as a non-credit course during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

The book "Introductory Business Statistics" by Thomas K. Tiemann explores the basic ideas behind statistics, such as populations, samples, the difference between data and information, and most importantly sampling distributions. The author covers topics including descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics. Using real-world examples throughout the text, the author hopes to help students understand how statistics works, not just how to "get the right number."

"Introductory Business Statistics with Interactive Spreadsheets - 1st Canadian Edition" is an adaptation of Thomas K. Tiemann's book, "Introductory Business Statistics". In addition to covering basics such as populations, samples, the difference between data and information, and sampling distributions, descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics, the following information has been added: the chi-square test and categorical variables, null and alternative hypotheses for the test of independence, simple linear regression model, least squares method, coefficient of determination, confidence interval for the average of the dependent variable, and prediction interval for a specific value of the dependent variable. This new edition also allows readers to learn the basic and most commonly applied statistical techniques in business in an interactive way -- when using the web version -- through interactive Excel spreadsheets. All information has been revised to reflect Canadian content.

In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required.

The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theories for how the brain may learn from experience, focusing on the neurobiology of object recognition. We plan to emphasize hands-on applications and exercises, paralleling the rapidly increasing practical uses of the techniques described in the subject.

This course examines how numerical methods are used by engineers to translate the language of mathematics and physics into information that may be used to make engineering decisions. Often, this translation is implemented so that calculations may be done by machines (computers). Upon successful completion of this course, the student will be able to: Quantify absolute and relative errors; Distinguish between round-off and truncation errors; Interconvert binary and base-10 number representations; Define and use floating-point representations; Quantify how errors propagate through arithmetic operations; Derive difference equations for first and second order derivatives; Evaluate first and second order derivatives from numerical evaluations of continuous functions or table lookup of discrete data; Describe situations in which numerical solutions to nonlinear equations are needed; Implement the bisection method for solving equations; List advantages and disadvantages of the bisection method; Implement both Newton-Raphson and secant methods; Describe the difference between Newton-Raphson and secant methods; Demonstrate the relative performance of bisection, Newton-Raphson, and secant methods; Define and identify special types of matrices; Perform basic matrix operations; Define and perform Gaussian elimination to solve a linear system; Identify pitfalls of Gaussian elimination; Define and perform Gauss-Seidel method for solving a linear system; Use LU decomposition to find the inverse of a matrix; Define and perform singular value decomposition; explain the significance of singular value decomposition; Define interpolation; Define and use direct interpolation to approximate data and find derivatives; Define and use NewtonĺÎĺĺÎĺs divided difference method of interpolation; Define and use Lagrange and spline interpolation; Define regression; Perform linear least-squares regression and nonlinear regression; Derive and apply the trapezoidal rule and Simpson's rule of integration; Distinguish Simpson's method from the trapezoidal rule; Estimate errors in trapezoidal and Simpson integration; Derive and apply Romberg and Gaussian quadrature for integration; Define and distinguish between ordinary and partial differential equations; Implement Euler's methods for solving ordinary differential equations; Investigate how step size affects accuracy in Euler's method; Implement and use the Runge-Kutta 2nd order method for solving ordinary differential equations; Apply the shooting method to solve boundary-value problems; Define Fourier series and the Fourier transform; Find Fourier coefficients for a given data set or function and domain; Describe the finite element method for one-dimensional problems. (Mechanical Engineering 205)

Psychology is designed to meet scope and sequence requirements for the single-semester introduction to psychology course. The book offers a comprehensive treatment of core concepts, grounded in both classic studies and current and emerging research. The text also includes coverage of the DSM-5 in examinations of psychological disorders. Psychology incorporates discussions that reflect the diversity within the discipline, as well as the diversity of cultures and communities across the globe.Senior Contributing AuthorsRose M. Spielman, Formerly of Quinnipiac UniversityContributing AuthorsKathryn Dumper, Bainbridge State CollegeWilliam Jenkins, Mercer UniversityArlene Lacombe, Saint Joseph's UniversityMarilyn Lovett, Livingstone CollegeMarion Perlmutter, University of Michigan

By the end of this section, you will be able to:

Describe the assumptions of the psychodynamic perspective on personality development
Define and describe the nature and function of the id, ego, and superego
Define and describe the defense mechanisms
Define and describe the psychosexual stages of personality development

" This course develops logical, empirically based arguments using statistical techniques and analytic methods. Elementary statistics, probability, and other types of quantitative reasoning useful for description, estimation, comparison, and explanation are covered. Emphasis is on the use and limitations of analytical techniques in planning practice."

This subject is on regional energy-environmental modeling rather than on general energy-environmental policies, but the models should have some policy relevance. We will start with some discussion of green accounting issues; then, we will cover a variety of theoretical and empirical topics related to spatial energy demand and supply, energy forecasts, national and regional energy prices, and environmental implications of regional energy consumption and production. Where feasible, the topics will have a spatial dimension. This is a new seminar, so we expect students to contribute material to the set of readings and topics covered during the semester.

This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification and Bioinformatics. The final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.

This course introduces statistical tools and techniques that are routinely used by modern statisticians for a wide variety of applications. Upon successful completion of this course, the student will be able to: apply statistical hypothesis testing for one population; conduct statistical hypothesis testing and estimation for two populations; apply multiple regression analysis to analyze a multivariate problem; analyze the outputs for a multiple regression model and interpret the regression results; conduct test hypotheses about the significance of a multiple regression model and test the significance of the independent variables in the model; select appropriate multiple regression models using automatic model selection, forward selection, backward elimination, and stepwise selection; recognize and address issues when using multiple regression analysis; identify situations when nonparametric tests are appropriate; conduct nonparametric tests; explain the principles underlying General Linear Model, Multilevel Modeling, Data Mining, Machine Learning, Bayesian Belief Networks, Neural Network, and Support Vector Machine. This free course may be completed online at any time. (Mathematics 251)

This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics.