CIV_ENV 495-0-32: Data Analytics for Transportation and Urban Infrastructure Applications

Quarter Offered

Spring : Th 9-12 ; Chen


Data Analytics is a graduate‐level class, which introduces most state‐of‐the‐art data
analytical concepts, techniques, and right algorithms to solve problems.

We live in a world occupied by various information. Big data is everywhere. With the
rapidly evolving of the web technology and mobile use, people are becoming more
and more enthusiastic about interacting, communicating and sharing with each other
through different social platforms and media. In recent years, this collective
intelligence has spread to many different domains, with a particular focus on ecommerce,
healthcare, and social network, causing the volume of user‐generated
data to expand exponentially. The extraction of knowledge from such a large amount
of unstructured dynamically changed is a challenging task. Those typical data includes
social comments from Facebook, online customer reviews, Twitter and other popular
social platforms, shopping transaction records, mobile messages, financial news and
climate data, etc. In the transportation field, mobile devices like GPS or apps in the
smartphone make it possible to track vehicle traces, and some traffic surveillance data
including speed, link counts, etc. also generate big data in large volumes.

However, the methods, models and algorithms that are used in the transportation
field to mine and explore data from estimation, prediction, validation of traffic to
transportation theories and models may not perform well under the new situation.
The same issue also exists in other fields.

Course Objectives

  1. To provide students a starting point for Data Analytics in their work and research;
  2. To introduce students to the popular algorithms and methods in Data Analytics;
  3. To expose students to recent study in Data Analytics;
  4. At the end of this course, each student should successfully generate a Data


Leskovec, Jure, Rajarman, Anand, Ullman, Jeffrey D.  Mining of Massive Datasets. 2nd ed.: Cambridge University Press, 2014

Liu, Yuxi Python Machine Learning By Example: The easiest way to get into machine learning. 2nd ed. Pakt Publishing, 2017

CEE 495-0-32 Course Syllabi