Statistics

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 Markov processes in discrete and continuous time. Topics include Markov chains, Poisson process, Markov processes, and an introduction to renewal theory and regenerative processes.

This course examines the fundamentals of Bayesian inference, including the specification of prior and posterior distributions, Bayesian decision theoretic concepts, the ideas behind Bayesian hypothesis tests, model choice and model averaging, the capabilities of several common model types, such as hierarchical and mixture models. It also looks at the ideas behind Monte Carlo integration, importance sampling, rejection sampling, Markov chain Monte Carlo samplers such as the Gibbs sampler and the Metropolis-Hastings algorithm, and use of the WinBuGS posterior simulation software.

This course provides an overview of data management architectures and analytics procedures aimed at organizing, describing, and modeling big data, both structured and unstructured. The course discusses both technical aspects of data management/analytics and topics related to analysis managerial evaluation including how to translate the outputs into meaningful business insights. The course examines topics including relational databases such as OLTP, Data warehouse, and SQL language; big data and NoSQL databases, distributed file system, Hadoop, Spark, and Data Lake concept; data understanding and data preparation; models and statistical techniques applied to Big Data; regression and classification trees; ensemble methods (random forest and boosted trees); logistic regression; supervised artificial neural networks; models' performance evaluation; big data ingestion and management; data preparation and cleaning; machine learning algorithms application; and machine learning model evaluation. The course requires students have a basic understanding of descriptive and inferential statistics and basic computer skills as a prerequisite.

In this course, students learn the skills to write Python code to implement statistical and machine learning algorithms that can be applied in a range of contexts. Each week the course covers an aspect of computer coding using examples and exercises that drawn on bioscience contexts. Topics will include: probability, maximum likelihood, Bayes theorem, supervised learning: regression and classification, unsupervised learning: dimensionality reduction and clustering, model evaluation and improvement, reinforcement learning, and neural networks and deep learning.