Statistics 2 (DS110)
Credits:3
The course covers nonlinear regression models including logistic regression, regression models with categorical independent variables, interaction terms and non-linear transformations of the predictors, factorial experiments, introduction to nonparametric statistics and nonparametric hypothesis testing, Bayesian statistics: Bayesian Priors, Posteriors, and Estimators, Bayesian hypothesis testing. Instructor-led discussions and problem sets with assessment including exams, projects, and problem sets.
Data Structures/Algorithms in Data Science (DS115)
Credits:4
The data structures part of the course will give students the knowledge to implement their algorithms using procedural and functional programming techniques and their associated data structures, including lists, vectors, data frames, dictionaries, trees, and graphs. The part of the courses dedicated to algorithms will help students to develop the skill set to understand the problem, break it into manageable pieces, assess alternative problem-solving strategies and arrive at an algorithm that efficiently solves the given problem. Class examples and homework will help students to apply the knowledge in data science domain.
Data Visualization (DS116)
Credits:3
The course is about the art and science of turning raw data into readable and useful visuals. The students will learn how to choose appropriate visualizations for numeric and categorical variables, the principles of visualization for univariate and multivariate data, visualization of spatial data, text data, etc. The course also provides the foundation for grammar of graphics. The second part of the course will focus on developing visual dashboards. Assessment by problem sets, projects, and exams. Instructor led discussions.
Programming for Data Science (DS120)
Credits:3
The course covers fundamentals of programming for data science such as classes, methods, procedures, control structures, functions, arrays, strings, scoping. The course will emphasize the use of programming essentials for data science-related tasks, such as working with dataframes, numeric calculations with vectors and lists, etc. The course will make use of programming languages widely used in data science such as Python and R. Three hours of instructor led class time per week including discussions and problem sets.
Physics and Chemistry in Life Sciences (DS150)
Credits:3
Introductory course to the foundations of chemistry and physics. Topics in chemistry include: atoms and molecules, chemical reactions, chemical solutions, chemical bonding, etc. The course provides an overview of topics in physics that are of particular importance to the life sciences and bioinformatics, including mechanics, electricity and magnetism, heat, nuclear physics, fluids, and waves, etc. Instructor led discussions. Assessment by problems sets, projects and exams.
Cell and Molecular Biology (DS151)
Credits:3
This course is aimed to provide understanding of the fundamental processes of cellular functions going on in prokaryotic and eukaryotic cells. The first part of the course focuses on the macro level with an exploration of basic cell characteristics, cellular membranes, cellular respiration and cell interaction with the environment. The second part of the course focuses on genetics with a look at chromosomes, genes, gene expression, how cells accomplish DNA replication, repair errors that can result in DNA, how cells reproduce, how cells communicate._x000D_ The last part of the course explores the relationship between cancer and the immune system at the cellular level. In each topic the appropriate techniques in cell and molecular biology will be discussed
Databases & Distributed Systems (DS205)
Credits:3
This course focuses on the design and system issues related to distributed database systems. Students will learn the usage of different design strategies for distributed databases, and they will study query processing techniques and algorithms as well as transaction management and concurrency control concepts used in such systems. Design and implementation issues related to multidatabase systems are discussed as well. The course will cover graph databases, relational and non-relational database structures. Instructor led discussion. Assessment by problem sets, exams and projects.
Business Intelligence (DS206)
Credits:3
This course provides an introduction to the concepts of business intelligence. It explores the essential components of BI project lifecycle: project planning, BI tool selection, data modelling, ETL (extract, transform, load) design, BI application/dashboard design and deployment. The course approaches BI from both managerial and technical viewpoints: the managerial perspective helps to understand how BI can support the organization’s decision-making processes, while the technical perspective explores the tools and techniques for developing for BI solutions. Learning is supported by individual and group projects, as well as assignments and case studies. Instructor-led discussions, with assessment by problem sets exams and projects.
Time Series Forecasting (DS207)
Credits:3
This course introduces the fundamental techniques for time series forecasting and analysis. The topics will include regression analysis, ARMA/ARIMA modelling, (G)ARCH modeling, VAR models, along with diagnostics and forecasting, and more. Mathematical formulation and assumptions underlying these statistical models, the consequence and the potential solutions when one or more of these assumptions are violated, are emphasized throughout. Students who successfully complete this course will be able to choose from available techniques to handle the real-world data, understand the trade-offs between models, and capture key patterns contained in the data. Instructor-led discussions, with assessment by problem sets exams and projects.
Spatial Data Science (DS209)
Credits:3
The course is an intensive introduction to spatial data science, covering topics from basic spatial data types, GIS, and coordinate systems to more advanced geostatistical, spatial machine learning, and spatial optimization methods. Having a strong applied focus, students will explore spatial data and use the methods and techniques to formulate and tackle complex real-world spatial data science problems. Students are expected to complete regular reading and coding assignments. Home tasks and assessment will include problem sets, discussion of case studies, and implementation of state-of-the-art spatial data analysis methods and algorithms from journal articles.
Introduction to Bioinformatics (DS211)
Credits:3
In this course, students learn fundamental concepts, methods, databases and algorithms in bioinformatics. The course covers specific tasks that can be completed using databases and algorithms. Three hours of instructor-led class time per week.
Computational Biology (DS213)
Credits:3
This course focuses on the analysis of NGS and -omics datasets to study complex biological problems. The course will expand on processing the data produced by next generation sequencing (NGS) technologies, e.g. read mapping, variant calling for DNA-seq datasets, gene expression estimation from RNA-seq datasets and peak calling from ChIP-seq datasets. Case studies are explored to identify disease-linked genetic features via genome wide association studies; to identify differentially expressed genes and differentially regulated biological processes in a disease; to perform genome assembly and species annotation in metagenomic datasets; to identify evolutionary relationship between species via phylogenetic studies; to apply machine learning for single cell analysis and annotation; and to integrate multiple sources of -omics data into a single framework.
Networks and System Biology (DS215)
Credits:3
This course explores the nature and properties of protein-protein interaction (PPI) networks and the functions of specialized sub-networks or biological pathways. The course will introduce a short overview of small world properties of biological networks, network patterns underlying regulatory feedback loops with simple graph theory algorithms. The students will also apply simplified rule-based modeling to investigate activation or repression of biological pathways. Examples drawn from case studies will be used to elucidate differential regulation of biological pathways in a disease. Tools and solutions for PPI and pathway annotation as well as machine learning based text mining will also be discussed.
Cheminformatics (DS216)
Credits:3
Cheminformatics is a field of information technology that links together chemistry and computer science. One of the major applications of cheminformatics is in drug discovery, where cheminformatics is used to store and analyze chemical data, as well as apply machine learning techniques to predict chemical properties or design new chemical motifs. This course includes topics such as molecular representation, chemical data manipulation, molecular property prediction. It also includes high level discussions on state-of-the-art methods of applying machine learning techniques for drug discovery.
Biostatistics (DS217)
Credits:3
The course covers development and application of statistical methods to a wide range of topics in biology. Examples will be drawn from subfields such as pharmacology, drug design, genetics, and molecular biology. The course is comprised of instructor-led lecture notes and practical exercises.
Causal Inference (DS219)
Credits:3
This course provides students with an introductory theory of causal inference for observational studies where randomized experiments are not possible. Topics include potential outcomes framework (matching and stratification, propensity weighting), causal graphical models, etc. Students will develop skills in implementing models using python and R to solve problems from various disciplines.
Urban Data Science (DS221)
Credits:3
This course provides a comprehensive introduction and overview of data science methods for informing urban planning, city management, and smart city solutions. It will cover the fundamentals of urban economic theory – mathematically exploring how cities form, grow, and function; urban mobility and transport planning and their relationship to land use; and will focus on applying spatial statistics, machine learning, spatial optimization and network theory to tackle real-world problems facing urban planners and policy makers. Such problems include to determining where in a city a new transit line should be introduced, how to plan a city to achieve equitable access to urban facilities for all citizens, how to optimally route snow cleaning and waste collection vehicles, how to predict urban mobility, and how to make use of data visualisation to build urban monitoring tools for decision makers. Home tasks and assessment include regular reading and coding assignments, as well as discuss case studies and the latest methods and algorithms.
Marketing Analytics (DS223)
Credits:3
The course concentrates on data science tools and methods that help to transform customer data into actionable findings. Topics include estimation of customer lifetime value, measuring marketing campaign ROI, new product development, advertising response models, etc. The students will learn how to define a business problem and chose appropriate data science method for it. Students are expected to have basic programming skills. Assessment by problem sets, projects, and examinations.
Applications of Machine Learning in Natural and Life Sciences (DS225)
Credits:3
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Students will learn about applications of machine learning (ML) and deep learning algorithms for solving open problems in natural and life sciences. Students will study recently published research papers and, amongst others, learn about the data-driven discovery of partial differential equations through sparse regression and protein structure prediction from its sequence through deep learning. Students will also aim to reproduce and use results from these papers. Knowledge of ML algorithms is required. |
Bayesian Statistics (DS226)
Credits:3
The course is an introduction to the theory of Bayesian Statistical Inference and Data Analysis. Both refer to practical inferential methods that use probability models for both observable and unobservable quantities. The flexibility and generality of these methods allow them to address complex real-life problems that are not amenable to other techniques. This course will also provide a pragmatic introduction to powerful applications of those methods. Topics include: the basics of Bayesian inference for single and multiparameter models, regression, hierarchical models, model checking, approximation of a posterior distribution by iterative and non-iterative sampling methods.
Business Analytics for Data Science (DS227)
Credits:3
This course will focus on business understanding and problem framing. This includes analysis of previous findings and identifying stakeholders’ challenges, understanding the components of analytics framework to compete on analytics; developing a data strategy for defining key performance metrics; introducing big data concepts and technological infrastructure for processing information; discussing innovative business models, appropriate analytical tools and necessary leadership role to implement analytics initiatives and prioritize them for budgeting, efficient resource allocation, effective creation of shared values and sustainable performance growth in a business domain.
Product Management (DS228)
Credits:3
The purpose of this course is to teach the students what product management is, who are product managers and what kind of skill set one needs to have to be successful at this role. We will cover the whole product management and product development lifecycle starting from ideation, idea validation, MVP experiments and implementation, success metrics and KPI setup and follow up._x000D_ Students will learn to create products or features from A to Z. _x000D_ Students are expected to have programming/coding skills to succeed in the course. There will be no coding sessions in this course.
Machine Learning Operations (MLOps) (DS229)
Credits:3
Machine learning operations (MLOps) is a core function of Machine Learning engineering, focused on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them. This course will familiarize students with the key aspects of end-to-end machine learning (ML) projects and ML-specific technical depth. The main goal of the course is to teach students the core concepts, methodologies and challenges in deployment and monitoring of Machine Learning systems with a primary focus on testing and monitoring of such systems.
Computer Vision (DS231)
Credits:3
This course provides a comprehensive introduction to the field of computer vision, focusing on the fundamental techniques and concepts used to extract meaningful information from digital images and videos. Topics include: image formation and representation, color models, image processing techniques, edge detection, feature extraction, image segmentation, object recognition, motion analysis, 3D scene reconstruction, and deep learning for computer vision. Students will learn to apply various computer vision algorithms and techniques to real-world problems using popular programming languages and libraries. Through hands-on projects, students will develop skills in image analysis, pattern recognition, and machine learning techniques relevant to computer vision applications.
Natural Language Processing (DS233)
Credits:3
The course goal is to provide a solid background in theory and techniques of Natural Language Processing for tasks such as understanding and generating natural language, machine translation, text classification, named entity recognition, topic modeling, etc. The course will focus on solving applied problems together with providing solid theoretical background.
Biomedical Imaging and Cell Staining (DS244)
Credits:3
Managerial Accounting and Analysis (DS250)
Credits:3
The course covers business concepts and methods used to report managerial performance information to internal users and managers to assist in making sound business decisions in managing the firm. Students will also learn how to analyse the efficiency of the firm’s management using financial data and how these data can be transformed into data science problems/projects._x000D_ Assessment by projects, problem sets, and exams. Instructor led discussions.
Capstone (DS299)
Credits:3
Students will use their accumulative knowledge to solve real-world problems with real-world data. During the project, students will follow the entire process of solving a real-world data science project: collecting and processing actual data, applying suitable and appropriate analytic methods to the problem, presenting results and findings. Students will be provided with a list of projects to choose from; however they are also encouraged to introduce their own projects. To conclude, students will do presentation of their findings and will submit reproducible report (codes and used datasets).