Courses

INTRODUCTION TO PROBABILITY AND STATISTICS

STAT4001S001 • 3 pts • Session A

Section/Call Number 001/10216

Prerequisites: A good working knowledge of calculus, including derivatives, single and double, limits, sums and series. Life is a gamble and with some knowledge of probability / statistics is easier evaluate the risks and rewards involved. Probability theory allows us take a known underlying model and estimate how likely will we be able to see future events. Statistical Inference allows us to take data we have seen and estimate the missing parts of an unknown model. The first part of the course focus on the former and the second part the latter.

Instructor

Gabriel Young

Modality

In-Person

Day/Time

Mo 16:30-18:05
Tu 16:30-18:05
We 16:30-18:05
Th 16:30-18:05

Enrollment

0 of 45

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Wall Street Prep: Economics, Finance, and Analytics

Visiting students can take this course as part of a Focus Area.

The Wall Street Prep: Economics, Finance, and Analytics Focus Area is designed for students who want to gain a better understanding of finance, business, and the complexities of economic systems. Students enhance their academic experience through specialized co-curricular activities exclusive to the city and earn a Certification of Participation.

Learn more about this Focus Area

PROBABILITY THEORY

STAT4203S001 • 3 pts • Session A

Section/Call Number 001/10215

Prerequisites: MATH V1101 Calculus I and MATH V1102 Calculus II, or the equivalent, and STAT W1111 or STAT W1211 (Introduction to Statistics). Corequisites: MATH V1201 Calculus III, or the equivalent, or the instructor's permission. This course can be taken as a single course for students requiring knowledge of probability or as a foundation for more advanced courses. It is open to both undergraduate and master students. This course satisfies the prerequisite for STAT W3107 and W4107. Topics covered include combinatorics, conditional probability, random variables and common distributions, expectation, independence, Bayes' rule, joint distributions, conditional expectations, moment generating functions, central limit theorem, laws of large numbers, characteristic functions.

Instructor

Young Kim

Modality

In-Person

Day/Time

Mo 16:30-18:05
Tu 16:30-18:05
We 16:30-18:05
Th 16:30-18:05

Enrollment

0 of 25

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Wall Street Prep: Economics, Finance, and Analytics

Visiting students can take this course as part of a Focus Area.

The Wall Street Prep: Economics, Finance, and Analytics Focus Area is designed for students who want to gain a better understanding of finance, business, and the complexities of economic systems. Students enhance their academic experience through specialized co-curricular activities exclusive to the city and earn a Certification of Participation.

Learn more about this Focus Area

Linear Regression Models

STAT4205S001 • 3 pts • Session A

Section/Call Number 001/10214

Prerequisites: STAT GU4204 or the equivalent, and a course in linear algebra. Theory and practice of regression analysis. Simple and multiple regression, testing, estimation, prediction, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.

Instructor

Modality

In-Person

Day/Time

Mo 18:15-19:50
Tu 18:15-19:50
We 18:15-19:50
Th 18:15-19:50

Enrollment

0 of 25

View this in the Directory Courses

Wall Street Prep: Economics, Finance, and Analytics

Visiting students can take this course as part of a Focus Area.

The Wall Street Prep: Economics, Finance, and Analytics Focus Area is designed for students who want to gain a better understanding of finance, business, and the complexities of economic systems. Students enhance their academic experience through specialized co-curricular activities exclusive to the city and earn a Certification of Participation.

Learn more about this Focus Area

STAT COMP & INTRO DATA SCIENCE

STAT4206S001 • 3 pts • Session A

Section/Call Number 001/10213

Prerequisites: STAT GU4204 and GU4205 or the equivalent. Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.

Instructor

Modality

In-Person

Day/Time

Mo 16:30-18:05
Tu 16:30-18:05
We 16:30-18:05
Th 16:30-18:05

Enrollment

0 of 25

View this in the Directory Courses

Wall Street Prep: Economics, Finance, and Analytics

Visiting students can take this course as part of a Focus Area.

The Wall Street Prep: Economics, Finance, and Analytics Focus Area is designed for students who want to gain a better understanding of finance, business, and the complexities of economic systems. Students enhance their academic experience through specialized co-curricular activities exclusive to the city and earn a Certification of Participation.

Learn more about this Focus Area

Time Series Analysis

STAT4221S001 • 3 pts • Session A

Section/Call Number 001/10212

Prerequisites: STAT GU4205 or the equivalent. Prerequisites: STAT GU4205 or the equivalent. Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.

Instructor

Modality

In-Person

Day/Time

Mo 18:15-19:50
Tu 18:15-19:50
We 18:15-19:50
Th 18:15-19:50

Enrollment

0 of 25

View this in the Directory Courses

Wall Street Prep: Economics, Finance, and Analytics

Visiting students can take this course as part of a Focus Area.

The Wall Street Prep: Economics, Finance, and Analytics Focus Area is designed for students who want to gain a better understanding of finance, business, and the complexities of economic systems. Students enhance their academic experience through specialized co-curricular activities exclusive to the city and earn a Certification of Participation.

Learn more about this Focus Area

STATISTICAL MACHINE LEARNING

STAT4241S001 • 3 pts • Session A

Section/Call Number 001/10211

Prerequisites: STAT GU4206 The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

Instructor

Modality

In-Person

Day/Time

Mo 18:15-19:50
Tu 18:15-19:50
We 18:15-19:50
Th 18:15-19:50

Enrollment

0 of 25

View this in the Directory Courses

Wall Street Prep: Economics, Finance, and Analytics

Visiting students can take this course as part of a Focus Area.

The Wall Street Prep: Economics, Finance, and Analytics Focus Area is designed for students who want to gain a better understanding of finance, business, and the complexities of economic systems. Students enhance their academic experience through specialized co-curricular activities exclusive to the city and earn a Certification of Participation.

Learn more about this Focus Area

STATISTICAL METHODS FOR FINANCE

STAT4261S001 • 3 pts • Session A

Section/Call Number 001/10210

Prerequisites: STAT GU4204 and STAT GU4205 A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.

Instructor

Hammou El Barmi

Modality

In-Person

Day/Time

Tu 09:00-12:10
Th 09:00-12:10

Enrollment

0 of 25

View this in the Directory Courses

Wall Street Prep: Economics, Finance, and Analytics

Visiting students can take this course as part of a Focus Area.

The Wall Street Prep: Economics, Finance, and Analytics Focus Area is designed for students who want to gain a better understanding of finance, business, and the complexities of economic systems. Students enhance their academic experience through specialized co-curricular activities exclusive to the city and earn a Certification of Participation.

Learn more about this Focus Area

PROBABILITY

STAT5203W001 • 3 pts • Session A

Section/Call Number 001/10209

Prerequisites: At least one semester of calculus. A calculus-based introduction to probability theory. Topics covered include random variables, conditional probability, expectation, independence, Bayes rule, important distributions, joint distributions, moment generating functions, central limit theorem, laws of large numbers and Markovs inequality.

Instructor

Young Kim

Modality

In-Person

Day/Time

Mo 16:30-18:05
Tu 16:30-18:05
We 16:30-18:05
Th 16:30-18:05

Enrollment

0 of 15

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LINEAR REGRESSION MODELS

STAT5205W001 • 3 pts • Session A

Section/Call Number 001/10207

Prerequisites: STAT GR5203 and GR5204 or the equivalent. Theory and practice of regression analysis, Simple and multiple regression, including testing, estimation, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.

Instructor

Modality

In-Person

Day/Time

Mo 18:15-19:50
Tu 18:15-19:50
We 18:15-19:50
Th 18:15-19:50

Enrollment

0 of 15

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STAT COMP & INTRO DATA SCIENCE

STAT5206S001 • 3 pts • Session A

Section/Call Number 001/10206

Prerequisites: STAT GU5204 and STAT GU5205 Open to MA students in Statistics only Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.

Instructor

Modality

In-Person

Day/Time

Mo 16:30-18:05
Tu 16:30-18:05
We 16:30-18:05
Th 16:30-18:05

Enrollment

0 of 15

View this in the Directory Courses

Time Series Analysis

STAT5221S001 • 3 pts • Session A

Section/Call Number 001/10205

Open to MA students in Statistics only Prerequisites: STAT GU4205 or the equivalent. Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.

Instructor

Modality

In-Person

Day/Time

Mo 18:15-19:50
Tu 18:15-19:50
We 18:15-19:50
Th 18:15-19:50

Enrollment

0 of 15

View this in the Directory Courses

STATISTICAL MACHINE LEARNING

STAT5241S001 • 3 pts • Session A

Section/Call Number 001/10204

Prerequisites: STAT GR5206 or the equivalent. Open to MA students in Statistics only The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

Instructor

Modality

In-Person

Day/Time

Mo 18:15-19:50
Tu 18:15-19:50
We 18:15-19:50
Th 18:15-19:50

Enrollment

0 of 15

View this in the Directory Courses

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