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 A highly interactive course focused on analysis of ecological data and independent projects using the open-source software program R. Prerequisite: BIOL_SCI 215 or ENVR SCI 202, a course in statistics.

Professor Stuart Wagenius

BIO_SCI 354 will be a course where we cover some the landmark results in quantitative biology. Every module (of which there are 5-6) will end with analysis of a data set acquired from the authors of studies and reanalysis and re-plotting of a central result from the paper. En route to that I will teach you the biology mathematics physics and statistics required to make the plots. The landmark papers will span from studies in gene regulation, developmental biology, sequencing etc.
We will also have various crash courses in coding, image analysis, etc.Introduction to landmark insights into quantitative biology. Random genetic processes, gene expression, cell adaption, cell cycle, developmental morphogens, phylgenomics.

Course website: https://madhavmani.wixsite.com/qbiocourse

Professor: Madhav Mani

BIO_SCI 359 is taught in 4 modules, which will involve students repeating experiments from some of the landmark papers in quantitative biology. Groups of students will work together and learn how to do experiments in a wet lab, perform live-imaging, conduct sequencing based studies, analyze their own data, and presentation skills.

Professor: Richard Carthew

Patterns of gene expression and their causes. Prerequisites: BIOL_SCI 215-0, BIOL_SCI 219-0; a course in statistics.

Professor Eric Weiss

IBIS 404 uses mathematical-based experimental analysis and modeling to study biological problems. The class will introduce quantitative techniques, computational tools and biological systems that help investigators analyze heterogeneous complex data about molecular networks to uncover meaningful relationships about key components.

Professor: Richard Carthew

Quantitative approaches to molecular and cell biology, focused on developing an understanding of connections between biomolecule structure and dynamics, and behavior of cells. The course will also include review of topics from statistics of random variables and statistical data analysis relevant to biology and biophysics.

Professor: John Marko

Introduction to the development and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques to the study of biological systems.

Introduction to programming practice using Python. Analysis and formulation of problems for computer solution. Systematic design, construction, and testing of programs. Substantial programming assignments in Python. See professor’s website for an updated syllabus.

This introductory programming course is not part of the major. It provides an introduction to programming for those that can benefit from becoming better programmers, but without committing to the major student’s version of the course.

Prof. Sarah Van Wart (Fall), Prof. Connor Bain (Winter & Spring) & Prof. Kuzmanovic (Spring)

This is an introductory course on the fundamentals of computer programming. I see this class as an opportunity for you, the student, to see what computer programming is all about and (more importantly) to see whether you want to spend the next few years doing more of it. This course will include weekly programming projects, readings, a midterm, and final examinations. Class participation is not optional.

Professsor Ian Horswill (Fall), Professor Sara Owsley Sood (Winter, Spring)

BIO_SCI 354 will be a course where we cover some the landmark results in quantitative biology. Every module (of which there are 5-6) will end with analysis of a data set acquired from the authors of studies and reanalysis and re-plotting of a central result from the paper. En route to that I will teach you the biology mathematics physics and statistics required to make the plots. The landmark papers will span from studies in gene regulation, developmental biology, sequencing etc.
We will also have various crash courses in coding, image analysis, etc.Introduction to landmark insights into quantitative biology. Random genetic processes, gene expression, cell adaption, cell cycle, developmental morphogens, phylgenomics.

Course website: https://madhavmani.wixsite.com/qbiocourse

Professor: Madhav Mani

Nearly every discipline with a quantitative component including engineering, physical sciences, social sciences, finance, computer graphics, big data, and machine learning rely on linear algebra. Numerical computation greatly enables modeling the data analysis in these fields. In order to utilize linear algebra and computing for problem solving, it is essential to understand how to set up problems (in the linear framework and numerically), determine when well defined solutions exist, write programs and algorithms in MATLAB to solve these problems, evaluate whether the algorithm will find the solution efficiently, and evaluate the accuracy of the computation.

Professor: Niall Mangan

Modern data streams, whether from biomedical research labs or environmental and social-science research teams, are quantitative and noisy. The goal of this class is to help you begin to think quantitatively and statistically about data, and to help you answer the question What do your data (actually) say?.  

To help you answer this question, we present an approach centered on applying statistical and mathematical techniques the modern way: with a computer.  More specifically, this course is not a substitute for a Machine Learning, AI, or classical statistics course. Instead, we will cover topics like parameter estimation, hypothesis testing, and dimensionality reduction in a way that is centered on the specific information that you know about your data.  By the end of the course, you will have a set of tools that should allow you to attack any quantitative problem, make a conclusion, and assess your confidence in that conclusion.

• Students will learn to make and manipulate empirical data distributions in their computers.  Students will be able to use these distributions to test different hypotheses about their data without relying on specialized statistics software.  
• Students will be able to identify the advantages and disadvantages of different statistical, mathematical, or computational techniques and will be able to determine appropriate methods for their data.  

• Students will be able to use data to construct models appropriate to their research question.  
• Students will be able to evaluate their model’s accuracy and efficacy and to compare their models to others.


Professor: Madhav Mani

Analysis and implementation of numerical methods for random processes: random number generators, Monte Carlo methods, Markov chains, stochastic differential equations, and applications.

Professor: Hermann Riecke

This course will give an introduction to the theory and practice of analyzing high-throughput RNA sequencing through lectures and hands-on exercises.

Professor William Kath

Our digital, connected, sensor rich world is generating extraordinary amounts of data (“Big Data”) that are being used for purposes as diverse as teaching a computer to win at Jeopardy or offering taxi alternatives. The skills needed to go from data to knowledge and application, which go under the name of Data Science, are in big demand in industry, government, and academia. This course provides an introduction to the foundational skills needed by data scientists. Prior knowledge of programming is not needed.

Professors Luis Amaral and Adam Pah

https://www.nico.northwestern.edu/education/nico-101-0-introduction-to-programming-for-big-data.html

The goal of this course is to provide an introduction of statistical methodologies in important topics in bioinformatics and computational biology. The course covers statistical methodologies used in two major topics, including gene expression data analysis and high-throughput DNA sequence analysis. Statistical theory and methods in this course include Z-test, t-test, regression, ANOVA, multivariate data analysis, Bayesian statistics, bootstrap, Monte-Carlo simulation, clustering algorithms, Markov Chain, Hidden Markov Chain, mixture model, etc. Students will learn basic knowledges and programming skills to perform most common bioinformatic analyses of data generated from current molecular biology research. The lectures will cover both principles of genomics and basic R coding to perform the statistical analyses. Students who are interested in bioinformatic research, gene expression analysis and high throughput sequence data analysis are highly encouraged to take this class. 

Professor: Jiping Wang. 

This tutorial is intended as a brief introduction to computer programming for data analysis in the Python scripting language. By the end of this tutorial, a student should be able to write simple Python programs, load and visualize data sets, and read and understand other Python code.

BE/APh161: Physical Biology of the Cell Course
Instructor: Professor Rob Phillips, Caltech
http://www.rpgroup.caltech.edu/aph161/syllabus?s=03 

Modern data streams, whether from biomedical research labs or environmental and social-science research teams, are quantitative and noisy. The goal of this class is to help you begin to think quantitatively and statistically about data, and to help you answer the question What do your data (actually) say?.  

To help you answer this question, we present an approach centered on applying statistical and mathematical techniques the modern way: with a computer.  More specifically, this course is not a substitute for a Machine Learning, AI, or classical statistics course. Instead, we will cover topics like parameter estimation, hypothesis testing, and dimensionality reduction in a way that is centered on the specific information that you know about your data.  By the end of the course, you will have a set of tools that should allow you to attack any quantitative problem, make a conclusion, and assess your confidence in that conclusion.

Course Notes

Video Lectures 

Find on this website a simple repository of talks by Professor Madhav Mani, Northwestern University.