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Sunday, February 28

NECSI Summer School


June 6-17, 2016

NECSI Summer School

Early Registration Open

Register by April 1st to take advantage of early registration discounts! 

June 6-10: Complex Physical, Biological & Social Systems
June 12: Computer Programming and Complex Systems 
June 13-17: Complex Systems Modeling, Networks, and Data Analytics
Location: MIT, Cambridge, MA

Course Information
These courses welcome registration by faculty, graduate students, post-doctoral fellows, professionals and others who would like to gain an understanding of the fundamentals of complex systems for application to research in their respective fields, or as a basis for pursuing complex systems research.
The NECSI Summer School offers two intensive week-long courses which can be taken together or separately, one is not a prerequisite for the other. The 2nd week has been updated this year to include an introduction to data analytics, in addition to complex systems modeling and networks. The courses consist of lectures, discussions, and supervised group projects. If desired, arrangements for credit at a home institution may be made in advance. Information regarding room accommodations with local university housing will be provided to course registrants.

See course descriptions below or online at:

WEEK ONE CX201: Complex Physical, Biological and Social Systems
Dates: June 6-10, 2016
This course offers an introduction to the essential concepts of complex systems and related mathematical methods and simulation strategies with application to physical, biological and social systems. The course will particularly focus on the use of multiscale representations as a unifying approach to complex systems concepts, methods and applications.
Concepts to be discussed include: emergence, complexity, networks, self-organization, pattern formation, evolution, adaptation, fractals, chaos, cooperation, competition, attractors, interdependence, scaling, dynamic response, information, and function.
Methods to be discussed include: statistical methods, cellular automata, agent-based modeling, pattern recognition, system representation and informatics.

LAB CX102: Computer Programming and Complex
Systems Date: June 12, 2016
This course introduces computer programming in the Python language for those with little or no computer programming experience. It is designed as a precursor to CX202.
The course will present programming concepts and hands-on exercises. Topics to be covered include: data structures, algorithms, variables and assignments, numerical and logical operations, lists and dictionaries, user-defined functions, flow control, loops, and visualization.

WEEK TWO CX202: Complex Systems Modeling, Networks & Data Analytics
Dates: June 13-17, 2016
This course provides (a) an introduction to building models of complex systems (physical, biological, social and engineered), and (b) the study of networks, including topologies and dynamics of real world networks, and (c) an introduction to data analytics.
The course will cover the basic construction and analysis of models including identifying what is to be modeled, constructing a mathematical representation, analysis tools and implementing and simulating the model in a computer program. Particular attention will be paid to choosing the right level of detail for the model, testing its robustness, and discussing which questions a given model can or cannot answer.
The study of networks will introduce the use of network topologies and the characterization of networks describing complex systems, including such concepts as small worlds, degree distribution, diameter, clustering coefficient, modules, and motifs. Different types of network topologies and network behaviors that model aspects of real complex systems will be described including: modular, sparse, random, scale-free, influence, transport, transformation, and structure.
The introduction to data analytics will cover skills needed to transform raw data into visualizations and insight. A variety of visualization techniques will be covered, including interactive representations. Analytic methods to be covered include: time series analysis, network analysis, data mining, machine learning, distribution fitting, and more. Students will learn to obtain and prepare data for analysis. An overview of academy- and industry-standard toolboxes for handling data will be given, including the construction of databases, visualization, and analysis.
NOTE: Students without a background in programming are strongly recommended to attend CX102: Computer Programming and Complex Systems in conjunction with CX202.
Comments from previous students:
Excellent course...useful thematic overview... applications in diverse contexts were exciting. Particularly appreciated the group project - excellent experiential pedagogy.
The course was an eye-opening framework to analyze my work through a different lens.
Presentations were extremely useful for me in understanding how to begin modeling complex systems and assessing them. Helped me understand a lot of things I have been doing so far without clearly understanding the principles.
This class very much stretched my mind to apply the ideas of complexity to the world... I believe I learned more on a grander scale... will help enrich my vocabulary and the way of thinking in the world with respect to complexity.
Excellent class. I hope to take a more active role in the community.
This course contained more insight than any other 'complexity' themed course that I have taken.

For more information and registration, visit: http://necsi.edu/education/school.html
New England Complex Systems Institute