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Data Science for Smart Cities

Learn various scientific techniques that will allow the analysis, inference and prediction of large-scale data (e.g. GPS vehicular data, social media data, mobile phone data, individual social network data etc.) that are present in city networks.

Este curso está archivado
16 semanas estimadas
6–9 horas por semana
Al ritmo del instructor
Dictado por un instructor según un cronograma
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Sobre este curso

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The availability of low cost and ubiquitous sensors in city infrastructure provides high granular data at unprecedented spatiotemporal scales. “Smart Cities” envision to utilize this data to provide a healthy, happy and sustainable urban ecosystem by integrating the information and communication technology (ICT), Internet of things (IoT) and citizen participation to effectively manage and utilize city infrastructure and services. “Data Science” is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge from data in various forms and provides fast and efficient understanding of the current dynamics of cities and ways to improve different services. This course will introduce scientific techniques that will allow the analysis, inference and prediction of large-scale data (e.g. GPS vehicular data, social media data, mobile phone data, individual social network data, etc.) that are present in city networks. Basics of the data science methods to analyze these datasets will be presented. The course will focus both on the methods and their application to smart-city problems. Python will be used to demonstrate the application of each method on datasets available to the instructor. Examples of problems that will be discussed include ridesharing platforms, smart and energy-efficient buildings, evacuation modeling, decision making during extreme events & urban resilience.

De un vistazo

  • Institución: PurdueX
  • Tema:Ingeniería
  • Nivel:Intermediate
  • Prerrequisitos:

    Undergraduate calculus and basic knowledge of statistical analysis.

  • Idioma:English
  • Transcripción de video:English

Lo que aprenderás

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  • Classify the different types of data generated by smart cities.
  • Apply the basics of various data mining techniques.
  • Map the data mining tool that is appropriate for various smart city applications.
  • Code, apply and solve the data mining algorithms using Python.
  • Interpret the results from the data mining tools and make connections to policy making as they relate to smart cities applications.

Plan de estudios

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Unit 1. Introduction to Data Mining

Week 1: Introduction to the Course & Syllabus, Review of Statistical Methods

Instructor introduction, introduction to data mining, course overview, student introduction, introduction of statistical methods, modeling uncertainty, random variables, population and samples, and statistical inference

Week 2: Optimization, Data Pre-Processing

Introduction to optimization, optimization-basic concepts, optimization problem formulation, optimization algorithms, data and measurement, types of datasets, data quality, data pre-processing, and task identification

Week 3: Project Discussion/Introduction to Python

Introduction to Python, Python for data mining, optimization using Python, and data pre-processing using python

Unit 2. Data Mining Tasks

Week 4: Regression Analysis, Association Rule Mining

Introduction to regression analysis, Linear regression, Logistic regression models, Poisson regression models, applications of regression analysis to smart cities, introduction to associate rule mining, association rule mining applications to urban systems, and association rule mining approaches

Week 5: Association Rule Mining, Statistical Classification

A-priori algorithm, F-P growth algorithm, ECLAT, evaluation methods, introduction to the classification problem, Logistic regression, Naïve Bayes classifier, and Bayesian network classifier

Weeks 6 and 7: Decision Tree, Support Vector Machines

Introduction to decision trees, decision tree training, decision tree algorithms, practical issues with decision trees, introduction to support vector machines, support vector machines, ensemble classifiers, and classifier performance evaluation

Weeks 8 and 10: Introduction to Data Clustering, Clustering Algorithms: Partitional and Hierarchical

Introduction to data clustering, (dis)similarity measures, distribution (model)-based clustering algorithms, types of clustering algorithms, partitional clustering (k-means and its variants), and hierarchical clustering

Week 11: Other Clustering Approaches

Density-based clustering algorithms, cluster validity, characteristics of “data, clusters, and clustering algorithms”

Unit 3. Advanced Data Mining Techniques

Week 12: Neural Networks

Introduction to neural networks, a neuron model, learning an ANN model, multi-layer-feed-forward ANNs, ANN application to land use prediction

Week 13: Deep Learning

Introduction to deep learning, deep learning, and deep learning for smart cities

Week 14: Case studies of Data Science Applications for Smart Cities

Week 15: Virtual Exam and Project Submission

Acerca de los instructores

¿Quién puede hacer este curso?

Lamentablemente, las personas residentes en uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.

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