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.
Data Mining for Smart Cities
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
62 hours to complete
3 weeks at 20 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
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.
Skills you'll gain
Details to know
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Assessments
12 assignments
Taught in English
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There are 8 modules in this course
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.
What's included
2 videos1 reading1 discussion prompt
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.
What's included
12 videos4 readings2 assignments1 discussion prompt
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.
What's included
6 videos4 readings2 assignments3 ungraded labs
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.
What's included
12 videos5 readings2 assignments1 discussion prompt9 ungraded labs
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.
What's included
11 videos5 readings2 assignments1 discussion prompt7 ungraded labs
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.
What's included
5 videos2 readings2 assignments4 ungraded labs
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.
What's included
10 videos3 readings1 assignment1 discussion prompt4 ungraded labs
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.
What's included
1 video2 readings1 assignment1 ungraded lab1 plugin
Instructor
Offered by
Recommended if you're interested in Data Analysis
Google Cloud
Google Cloud
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