Internet of things (IoT) has become a significant component of urban life, giving rise to “smart cities.” These smart cities aim to transform present-day urban conglomerates into citizen-friendly and environmentally sustainable living spaces. The digital infrastructure of smart cities generates a huge amount of data that could help us better understand operations and other significant aspects of city life.
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Ce que vous apprendrez
Describe types of smart city-generated datasets, data mining techniques, and how to implement them using Python 3.
Explain how to read and preprocess data for data mining.
Apply data mining techniques to smart city-generated data and visualize and interpret the physical implications of the results.
Compétences que vous acquerrez
- Catégorie : Mathematics
- Catégorie : Python Programming
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Il y a 8 modules dans ce cours
This module provides an overview of the course content and structure. In this module, you will learn about the different course elements. In this module, you will get acquainted with your instructor and get an opportunity to introduce yourself and interact with your peers.
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2 vidéos1 lecture1 sujet de discussion
In this module, you will learn about data mining, why we need it, and the approach. The module also presents the basics of probability and statistics, which form the foundation for data mining. You will also gain insight into data preprocessing and data mining task identification.
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12 vidéos4 lectures2 devoirs1 sujet de discussion
In this module, you will learn about Python programming for data mining. The module also discusses important Python modules: NumPy , SciPy, and Matplotlib. You will learn to install Python using Anaconda and use the Jupyter notebook to write your code. The module also presents some examples demonstrating data preprocessing using Python.
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6 vidéos4 lectures2 devoirs3 laboratoires non notés
In this module, you will learn about supervised learning (learning from examples). The module discusses two supervised learning tasks: regression and classification. You will also gain insights into several classification algorithms such as Bayesian classifiers, decision trees, support vector machines (SVM), and ensemble classifiers.
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12 vidéos5 lectures2 devoirs1 sujet de discussion9 laboratoires non notés
In this module, you will learn about unsupervised learning (learning from unlabelled data without any ground truth labels). The module also discusses frequent itemset mining. You will also gain an insight into several data clustering algorithms such as distribution-based, partitional, and hierarchical clustering.
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11 vidéos5 lectures2 devoirs1 sujet de discussion7 laboratoires non notés
In this module, you will learn about anomaly detection problems and algorithms. You will gain insight into anomaly detection techniques. You will learn to validate your results. When applying data mining to smart city data, you will also learn to avoid false discoveries using statistical significance testing and hypothesis testing.
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5 vidéos2 lectures2 devoirs4 laboratoires non notés
In this module, you will learn about some advanced data mining algorithms such as artificial neural networks (ANN) and deep learning. You will develop an understanding of the applications of these algorithms. The module also analyzes hidden Markov models (HMMs) for modeling time series (sequential) data.
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10 vidéos3 lectures1 devoir1 sujet de discussion4 laboratoires non notés
In this module, you are provided with your term-end project, instructions to complete the project, and the criteria for how your instructor will grade your submission.
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1 vidéo2 lectures1 devoir1 laboratoire non noté1 plugin
Instructeur
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Recommandé si vous êtes intéressé(e) par Data Analysis
University of Illinois Urbana-Champaign
École Polytechnique Fédérale de Lausanne
University of Colorado Boulder
University of Colorado Boulder
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