This Fall, C2SMART launched the “C2SMART Student Learning Hub”, free for all consortium member students. Lessons will be from a variety of course domains including data science, computer science, and traffic simulation. The Learning Hub will:

Check out our tentative schedules below and stay tuned for more details!

Fall 2020

7 sessions, every Friday 3 PM EST starting from Oct. 9th, 2020

Instructor: Dr. Yueshuai (Brian) He, New York University

Experience with traffic modeling is useful, but not required.

This workshop provides a detailed introduction to the MATSim-NYC model developed by C2SMART Center and teaches how to extend the base model to incorporate new scenarios as well as how to duplicate the development of the model to other cities. The MATSim-NYC model is a city-scale simulation test-bed to evaluate emerging technologies and policies with a common platform. Participants will gain hands-on example data and scripts from the model and practice input preparation and output analysis.

Although registration is closed, if you still want to attend upcoming sessions, please contact Yueshuai (Brian) He at yh1995@nyu.edu.

Oct. 21 1:00 PM, Session 1 (1 hour); Oct. 30 1:00 PM,  Session 2 (2 hours)

Instructor: Suzana Duran Bernardes, New York University

No prior experience required.

The session will introduce learners to data science through the Python programming language. This skills-based specialization is intended for newcomers without any programming background. This session will introduce fundamental programming concepts including data structures, basic operations in python, Pandas library for data analysis and Matplotlib for data visualization. You will also learn how to use Jupyter Notebook to write your programs. In the hands-on exercise, you’ll use the technologies learned and create your own program for data retrieval, processing, and visualization.

Watch session 1 here

Nov. 11 2:00PM (tentative date)

Instructor: Zilin Bian, New York University

No prior experience required. Coding knowledge preferred.

This session will provide you a foundational understanding of machine learning models (isolation forest, decision tree, neural network etc.) as well as demonstrate how these models can solve complex problems for smart cities. This session is intended for newcomers who are interested in knowing what machine learning and what applications can be built. More importantly, you’ll learn about not only the theoretical knowledge, but also gain the practical know-how needed from a simple hands-on exercise.

Instructor: Jingxing Wang, University of Washington

Nov. 18 1:00-2:00 PM

No prior experience required. Coding knowledge preferred.

This session will introduce approaches to collect open-sourced transportation data for related research. We will use Google API travel time data collection as an example to demonstrate how such real time travel time data was collected and used for traffic performance analysis in the greater Seattle area during the COVID-19 pandemic. This session will also discuss several widely used ways to collect other types of data. Hands-on exercise will be provided.

Spring 2021

Instructor: Di Yang, NYU

No prior experience required.

Knowledge of Geographic Information Systems (GIS) is an increasingly sought after skill in industries from agriculture to public health. In this class you will learn the basics of the industry’s leading software tool, ArcGIS, including its common data types (such as raster and vector data), structures, projections and geographic coordinate system. You will learn how to visualize and analyze your data and apply location-based analytics to your research. In the hands-on exercise, you will create a GIS project using a combination of data identification and collection, analytical map development, and spatial analysis techniques.

Instructor: Fan Zuo & Ding Wang, NYU

No prior experience required.

Traffic simulation is the mathematical modeling of transportation systems through the application of computer software to better help plan, design, and operate transportation systems. In this course, you will get to know the extensive functions of an open source, highly portable, microscopic and continuous road traffic simulation package, Simulation of Urban MObility” (SUMO), which is designed to handle large road networks. You will use SUMO to create your network with traffic signal heads and bus lanes, define vehicle types and trips behaviors, run simulations and generate reports.

Instructor: Suzana Duran Bernardes, NYU

No prior experience required.

As you begin your own research, it is important you keep your materials understandable and usable. This session is intended for newcomers to data visualization. In this session, we will demonstrate best practices for data visualization and data storytelling. You will view examples from real world cases. With the hands-on exercise session, you will be able to generate powerful visualizations and dashboards of common data analyses that will help people understand and make decisions based on their data.

Instructor: 

No prior experience required.

Before starting your own research, it is critical to understand the current state of your discipline.  In this workshop, we will explore how to conduct a thorough literature review, how to navigate the databases available to you, and how to organize your work.

Other potential topics:

  • Database
  • Geocoding
  • Synchro
  • Object detection

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