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The course will be coding-oriented and cover the concepts underpinning and the applications of statistical modeling/inference. Students will build models with real-world data and modern data science toolkits. Concepts covered in this course will include: foundations in probability including basic rules, Bayes theorem, basic distributions; sampling and the central limit theorem; bootstrapping, confidence intervals, hypothesis testing, multiple testing; linear models, basic and multiple regression, inference for regression, regularization; classification, logistic regression and tree-based methods; prediction, model interpretation, model evaluation.

Data is central to data science. The data explosion experienced in every aspect of our lives, from social media to advanced instrumentation, requires a deeper understanding of the full spectrum of data life cycle management. Starting with the concept of ‘what is data?’, the first part of this course introduces various stages of the data life cycle, from defining data requirements, to data creation and gathering, to data fusion and data preparation, to data cleaning and quality control, to exploratory analytics, data interpretation, and visualization. We will explore concepts in FAIR data principles of data curation, metadata, and digital preservation policies with the aim of data reuse and reproducible science. The second part of this course will introduce the concept of relational databases that provide storage and management for structured data. We will explore concepts and implementations of relational database management systems suited for data science applications.

This course will provide students with advanced concepts on the construction and use of data structures and their associated algorithms. Abstract data types, lists, stacks, queues, trees and graphs. Sorting, searching, hashing and an introduction to numerical error control. Techniques of algorithm analysis and problem-solving paradigms, using relevant programming languages and tools. 

This course will be an introductory course to machine learning. The goal is to equip students with knowledge of existing tools for data analysis and to get students prepared for more advanced courses in machine learning. The course will cover core principles of artificial intelligence for statistical inference and pattern analysis. Topics will include probability distributions; graphical models; optimization, maximum likelihood estimation, regression; classification; cross validation; generalization and overfitting; neural networks; nonparametric estimators; clustering; autoencoders; generative models; and kernel methods. Applications in tabular, image, and textual data for supervised and unsupervised learning tasks will be covered.

Advanced Databases for Data Science

This course will explore intermediate-level design and implementation of database systems with an emphasis on scalable, distributed systems. The course will deepen students’ knowledge of advanced relational database management, followed by discussing current and emerging practices for dealing with big data and large-scale database systems used by many social networking and e-commerce services. Concepts include design and implementation of relational databases, exploration of distributed data structures including graph, document, and key-value storage models and scalable and resilient query processing. Students will gain practice working with real data and multiple modern database technologies.

Mathematical Tools for Data Science

This course will present the mathematical intuition, theory, and techniques driving the numerical computation methods used for processing and analyzing data in various real-life problems. Topics include dimensionality reduction, linear and non-linear approximation, frequency and wavelet analysis, and a glimpse into the mathematics of deep neural networks, classification, large-scale and high-performance numerical computing, and visualization. Each topic will be motivated by a data analysis challenge, introduce the mathematical intuition, theory and techniques used to address it, and conclude with a coding component with real data.

Visualization and Communication

This course will provide students with a foundational understanding of visual perceptional and data visualization design practices, provide instruction on using visualization for tasks such as exploratory analysis and storytelling to support both data-driven discovery and communication. The class will focus hands-on experiences with commonly used data science tools and technologies.

Governance, Bias, Ethics, and Fairness in Data Science and AI

Big data originates in a variety of venues; access to that data and ownership of it raise social and ethical issues. Data analytics produces results from which conclusions about society are drawn and acted upon; these uses also have social and ethical implications for practice. This course will focus on these issues that arise at both the beginning and the end of the process of data analytics.

Deep Learning