New England Complex Systems Institute
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Summer Session 2019

Gain new insights that reframe your thinking, specific tools to advance current projects, and perspectives to set new directions.

Dates: June 2 - 14

Location: MIT, Cambridge, MA

 

The NECSI Summer School offers two intensive week-long courses on complexity science: modeling and networks, and data analytics. You may register for any of the weeks. If desired, arrangements for credit at a home institution may be made in advance.

Week 1: June 2-7 CX201B: Concepts and Modeling

Week 2: June 9-14 CX202B: Networks and Data Analytics

Group Projects

Group projects are one of the most rewarding parts of the winter and summer courses. Participants split into project teams and put together a publication quality research project using complex systems tools learned during the week. On the final day of each week, groups present their projects. We consistently receive positive feedback about the projects.

Credit

Arrangements to receive credit for NECSI courses at a home institution should be made in advance. To do so, contact us at programs@necsi.edu.

Schedule

Sunday, Jun. 2: 
Lab 9 AM – 5 PM

Monday – Thursday, Jun. 3-6: 
Lecture 9 AM – 5 PM 
Group Projects 6 – 8 PM

Friday, Jun. 7: 
Group Presentation 9 AM – 12 PM 
Evaluations and Class Photo 12 – 12:15 PM 
Exam (required if taking course for credit, optional otherwise) 12:30 – 1:30 PM

Sunday, Jun. 9: 
Lab 9 AM – 5 PM

Monday – Thursday, Jun. 10-13: 
Lecture 9 AM – 5 PM 
Group Projects 6 – 8 PM

Friday, Jun. 14: 
Group Presentation 9 AM – 12 PM 
Evaluations and Class Photo 12 – 12:15 PM 
Exam (required if taking course for credit, optional otherwise) 12:30 – 1:30 PM


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Lab June 2, Course June 3-7

CX102 (Lab): Computer Programming for Complex Systems

This one day lab introduces computer programming in the Python language for those with little or no computer programming experience. It is designed as a precursor to CX201B.

The lab 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.

CX201B: Concepts and Modeling

This course offers an introduction to the essential concepts and models of complex systems and related mathematical methods and simulation strategies with application to physical, biological and social systems.

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 data analytics. The course will use of multiscale representations as a unifying approach to complex systems concepts, methods and applications.

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.

There will be supervised group projects as an integral part of the course.


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Lab June 9, Course June 10-14

CX103 (Lab): Setting up for Data Analytics

This one day lab introduces computer programming in the Python language focused on the management of data for analysis. It is designed as a precursor to CX202B.

This lab will cover essential data handling methodologies using industry-standard tools in the Python language. The lab will cover obtaining, loading, cleaning, initial exploration, saving, and preparing data for in-depth analysis. Time permitting, other topics to be covered in the lab include database construction and management, basic plotting and visualization, and fundamental concepts for developing web-based interactive visualizations.

CX202B: Networks and Data Analytics

This course provides an introduction to (a) the study of networks, including topologies and dynamics of real world networks and (b) the fundamentals of data analytics, machine learning, and artificial intelligence.

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 data analytics lessons will cover skills needed to transform raw data into visualizations and insight. The course will cover fundamental 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.

Students will learn to obtain and prepare data for analysis. An overview of academy- and industry-standard toolboxes for handling large datasets will be given, including the collection of data using APIs, construction of databases, visualization, and analysis. A variety of visualization techniques will be covered, including interactive representations.

Analytic methods to be covered include: distribution fitting, data mining, machine learning (regression, classification and clustering), network analysis and time series analysis. 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.


Overview | Week 1 | Week 2 | Student Reviews | Register

Reviews from Previous NECSI Course Students

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"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."