Statistics

This course reviews the core methods of statistical and machine learning used to analyze data and support decision-making. Students acquire skills in unsupervised learning to build clusters and decrease dimensionality in big datasets, as well as classification and regression. Topics include: evaluation of learning methods; unsupervised learning-- clustering and dimension reduction; statistical classification; case studies.

Pre-requisites: Linear algebra; Probability and Data Analysis; Introduction to Statistical Modeling

Topics include Fourier analysis: Fourier series, Fourier transform, Dirac delta function, sifting property, Fourier representation, convolution, correlations, Parseval's theorem power spectrum, sampling; Nyquist theorem, data compression, solving ordinary differential equations with Fourier methods, driven damped oscillators, Green's functions for 2nd order ODEs, partial differential equations, PDEs and curvilinear coordinates, Bessel functions, and Sturm-Liouville theory. Topics for probability and statistics include concept and origin of randomness, randomness as frequency and as degree of belief, discrete and continuous probabilities, combining probabilities, Bayes theorem, probability distributions and how they are characterized, moments and expectations, error analysis, permutations, combinations, and partitions, Binomial distribution, Poisson distribution, the Normal or Gaussian distribution, shot noise and waiting time distributions, resonance and the Lorentzian, growth and competition and power-law distributions, hypothesis testing, parameter estimation, Bayesian inference, correlation and covariance, and model fitting.

The International Internship course develops vital business skills employers are actively seeking in job candidates. This course is comprised of two parts: an internship, and a hybrid academic seminar. Students are placed in an internship within a sector related to their professional ambitions. The hybrid academic seminar, conducted both online and in-person, analyzes and evaluates the workplace culture and the daily working environment students experience. The course is divided into eight career readiness competency modules as set out by the National Association of Colleges and Employers (NACE), which guide the course’s learning objectives. During the academic seminar, students reflect weekly on their internship experience within the context of their host culture by comparing and contrasting their experiences with their global internship placement with that of their home culture. Students reflect on their experiences in their internship, the role they have played in the evolution of their experience in their internship placement, and the experiences of their peers in their internship placements. Students develop a greater awareness of their strengths relative to the career readiness competencies, the subtleties and complexities of integrating into a cross-cultural work environment, and how to build and maintain a career search portfolio.

This course offers an introduction to the basic concepts of programming and to solving mathematical and statistical problems.

This course covers the basic theories and methods of sampling technology, and application of sampling surveys in the fields of society, economy, and scientific research. It introduces some basic probability sampling methods, including simple random sampling, stratified sampling, cluster sampling, multi-stage sampling, equidistant sampling and unequal probability sampling, etc., focusing on the theory of statistical inference and sampling design. It provides a brief introduction to non-sampling errors and survey practices (such as questionnaire design, survey report writing, etc.).