Graduate Courses

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Bioinformatics

I think taking 6308 + 6200 would be the best for new students, and if you had to pick one, 6308 is probably easier and will give students more bioinfo exposure. 6200 is much harder, has much more comp sci theory/programming in it, and for someone who has never seen a fastq or bam file.. it might be a steeper learning curve.

BINF6200: Bioinformatics Programming

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Focuses on the fundamental programming skills required in the bioinformatics industry. Focuses on Python and R as the main programming language used. Topics include string operations, file manipulation, regular expressions, object-oriented programming, data structures, testing, program design, and implementation. Includes substantial out-of-classroom assignments.

BINF6308: Bioinformatics Computational Methods I

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Offers the first semester of a two-semester sequence on the use of computers in bioinformatics research. Offers students an opportunity to work with current methods and computational algorithms used in contemporary sequence analysis. Teaches practical skills necessary to manage and mine the vast biological information being generated and housed in public databases. Emphasizes the use of Python as the primary computer language and requires students to learn and understand basic computer logic and syntax, including an introduction to scalars, arrays, hashes, decision statements, loops, subroutines, references, and regular expressions. A focus on fundamental skills, including the command line interface found in the Linux operating system, is designed to prepare students for second-semester applications.

BINF6309: Bioinformatics Computational Methods II

  • Syllabus:

Designed to build upon the core topics covered in BINF 6308, i.e., use of the computer as a tool for bioinformatics research. Builds upon the Python language fundamentals covered during the first semester but requires students to apply these fundamentals to a semester-long project. The project includes protein family analysis, multiple sequence analysis, phylogeny, and protein structure analysis. Additionally, students have an opportunity to learn to build, load, connect, and query custom MySQL databases, and parse command line flags.

BINF6400: Genomics in Bioinformatics

  • Syllabus:

Introduces the field of genomics. With the completion of the Human Genome Project several years ago, there has been an explosion of genetic data collected. Focuses on the bioinformatics tools necessary to analyze large-scale genomic data. Covers topics such as phylogenetic trees, molecular evolution, gene expression profiling, heterogeneous genomic data, as well as next-generation sequencing (NGS) data.

Programming

CS5001: Intensive Foundations of Computer Science

  • Syllabus:

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This course is an accelerated introduction to the principles of systematic problem solving through “computational thinking” and programming. Topics include analysis of problems, modeling a solution, data types, and control structures. Additionally, it will introduce various ways to organize data including a discussion of their advantages and disadvantages.

This course is part of the ALIGN MS in the Computer Science Program, but assumes no prior experience with programming. Students learn the basics of programming using the Python programming language.

Machine Learning

DS4400: Machine Learning and Data Mining I

  • Syllabus: https://www.ccs.neu.edu/home/alina/classes/Spring2021/

Machine learning is a fast-pacing and exciting field achieving human-level performance in tasks such as image classification, speech recognition. machine translation, precision medicine, and self-driving cars. Machine learning has already impacted greatly our daily lives and has the potential to transform the world even more in the near future. This course will provide a broad introduction to machine learning and cover the fundamental algorithms for supervised and unsupervised learning. We will cover topics related to regression, classification, deep learning, dimensionality reduction, and clustering. The class will also provide an introduction into adversarial machine learning, an emerging area that studies the fundamental security issues of machine learning

Biostatistics

ENVR6500: Biostatistics

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Offers an overview of traditional and modern statistical methods used to analyze biological data using the free and open-source R programming environment. Lectures describe core statistical approaches and discuss their suitability for understanding patterns that arise at different levels of biological organization, from cellular processes to whole ecosystems. Supervised lab sessions offer students an opportunity to develop the R programming skills required to analyze the complex datasets that often emerge when addressing cutting-edge questions in biology. Topics include basic probability and sampling theory, experimental design, null hypothesis significance testing, t-tests and ANOVA, correlation and regression, likelihood, model selection, and information theory.

Evolution

BIOL5585: Advanced Evolution

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Discusses history of evolutionary theory and lines of evidence. Emphasis is on mechanisms of speciation. Introduces and discusses current evolutionary topics.

GIS

ENVR5260: Geographical Information Systems

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This course explores the practical application of GIS to support geographic inquiry and decision making. It focuses on the technical knowledge of the common tasks that a GIS analyst faces in applying GIS to a variety of spatial problems. Students will gain hands-on experience with a leading commercial GIS software package.Students will have the opportunity to gain a good understanding of the concepts, principles, approaches, and techniques of spatial analysis using Geographic Information Systems.

  • Students will have the opportunity to become familiar with the everyday challenges facing GIS professionals as they apply GIS principles to a variety of spatial problems.

  • Students will have the opportunity to gain an understanding of real-world GIS applications and how to approach them.

  • Students will have the opportunity to explore geographic data, to learn how it is stored and managed, and to see how it differs from other types of data.

  • Students will have the opportunity to become familiar with the basic functionality and operation of the ESRI ArcGIS software.