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Data Mining for Smart Cities
  • Level Foundation
  • Course by IIT Roorkee
  • Offered by

About

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. In this course, you will become aware of various data mining and machine learning techniques and the various dataset on which they can be applied. You will learn how to implement data mining in Python and interpret the results to extract actionable knowledge. The course includes hands-on experiments using various real-life datasets to enable you to experiment on your domain-related novel datasets. You will use Python 3 programming language to read and preprocess the data and then implement various data mining tasks on the cleaned data to obtain desired results. Subsequently, you will visualize the results for the most efficient description.

Modules

Introduction

    • 2 Videos
    • 1 Readings
    • 1 Discussion
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1 Discussions

  • Meet and Greet

2 Videos

  • Course Introduction
  • Meet Your Instructor

1 Readings

  • Course Overview

Lesson 1: Introduction to Data Mining and its Application to Urban Systems

    • 4 Videos
    • 1 Readings
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4 Videos

  • Necessity of Data Mining
  • Data and Mining Techniques 
  • Applications of Data Mining to Smart Cities 
  • Data Mining: Common Challenges

1 Readings

  • Essential Reading: Introduction to Data Mining and its Application to Urban Systems

Lesson 2: Review of Statistical Methods

    • 4 Videos
    • 2 Readings
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4 Videos

  • Uncertainty and How to Model It
  • Review of Random Variables
  • Population, Samples, and Statistical Inference
  • Parameter Estimation

2 Readings

  • Essential Reading: Review of Statistical Methods
  • Recommended Reading: Review of Statistical Methods

Lesson 3: Data Preprocessing

    • 4 Videos
    • 1 Readings
    • 1 Discussion
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1 Discussions

  • Data Mining for Smart Cities: Dos and Don’ts

4 Videos

  • Types of Collected Data
  • Data Quality
  • Data Preprocessing Tasks
  • Task Identification

1 Readings

  • Essential Reading: Data Pre-Processing – I

Practice Quiz: Week 1

    • 1 Assignment
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1 Assignment

  • Introduction to Data Mining for Smart Cities

Graded Quiz: Week 1

    • 1 Assignment
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1 Assignment

  • Graded Quiz: Introduction to Data Mining for Smart Cities

Lesson 1: Python Programming for Data Mining

    • 3 Videos
    • 1 Readings
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3 Videos

  • Installing Python Using Anaconda Distribution
  • Python Data Types and Data Structures
  • Python Libraries for Data Mining: NumPy, SciPy, and Matplotlib

1 Readings

  • Essential Reading: Python Programming for Data Mining

Lesson 2: Data Preprocessing

    • 3 Videos
    • 1 Readings
    • 3 Lab
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3 Labs

  • Practice Assignment - Feature Scaling and Standardization
  • Practice Assignment: Label Encoding for Categorical Variables
  • Practice Assignment: Handling Missing Values in the Data

3 Videos

  • Feature Scaling and Standardization
  • Label Encoding for Categorical Variables
  • Handling Missing Values in the Data 

1 Readings

  • Essential Reading: Data Pre-Processing – II

Introduction to Term-End Project

    • 1 Readings
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1 Readings

  • Introduction to Project - Multi-layer Perceptron and Markov Process

Practice Quiz: Week 2

    • 1 Assignment
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1 Assignment

  • Introduction to Python Programming for Data Mining

Graded Quiz : Week 2

    • 1 Assignment
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1 Assignment

  • Graded Quiz: Introduction to Python Programming for Data Mining

Live Session

    • 1 Readings
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1 Readings

  • Live Session 1

Lesson 1: Regression Analysis

    • 3 Videos
    • 2 Readings
    • 2 Lab
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2 Labs

  • Practice Assignment: Linear Regression Problem and Its Solution
  • Practice Assignment: Advanced Regression Models 

3 Videos

  • Regression Analysis and Its Applications in Smart Cities
  • Linear Regression Problem and Its Solution
  • Advanced Regression Models 

2 Readings

  • Essential Reading: Regression Analysis
  • Recommended Reading: Regression Analysis

Lesson 2: Introduction to Statistical Classifier

    • 3 Videos
    • 1 Readings
    • 3 Lab
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3 Labs

  • Practice Assignment: Classification Problem and Logistic Regression
  • Practice Assignment: Naïve Bayes Classifiers
  • Practice Assignment: Bayesian Network Classifiers

3 Videos

  • Classification Problem and Logistic Regression
  • Naïve Bayes Classifiers
  • Bayesian Network Classifiers

1 Readings

  • Essential Reading: Introduction to Statistical Classifier

Lesson 3: Decision Trees

    • 2 Videos
    • 1 Readings
    • 1 Lab
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1 Labs

  • Practice Assignment: Popular Decision Tree Algorithms and Their Shortcomings

2 Videos

  • Decision Tree Classifiers and How to Train Them
  • Popular Decision Tree Algorithms and Their Shortcomings

1 Readings

  • Essential Reading: Decision Trees

Lesson 4: Support Vector Machines (SVMs)

    • 4 Videos
    • 1 Readings
    • 1 Discussion
    • 3 Lab
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1 Discussions

  • Supervised Learning Tasks and Algorithms

3 Labs

  • Practice Assignment: Linear SVMs and How to Train Them
  • Practice Assignment - Nonlinear SVMs: The Kernel Trick
  • Practice Assignment: Ensemble Classifiers

4 Videos

  • Linear SVMs and How to Train Them
  • Nonlinear SVMs: The Kernel Trick
  • Ensemble Classifiers 
  • Classifier Performance Evaluation

1 Readings

  • Essential Reading: Support Vector Machines (SVMs)

Practice Quiz : Week 3

    • 1 Assignment
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1 Assignment

  • Supervised Learning

Graded Quiz: Week 3

    • 1 Assignment
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1 Assignment

  • Graded Quiz: Supervised Learning

Lesson 1: Association Rule Mining

    • 3 Videos
    • 1 Readings
    • 1 Lab
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1 Labs

  • Practice Assignment: Generating Association Rules from Frequent Itemsets

3 Videos

  • Introduction and Applications to Urban Systems
  • Association Rule Mining: Brute Force vs. Apriori
  • Generating Association Rules from Frequent Itemsets

1 Readings

  • Essential Reading: Association Rule Mining

Lesson 2: Data Clustering

    • 6 Videos
    • 2 Readings
    • 5 Lab
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5 Labs

  • Distribution Model-Based Clustering Algorithms
  • Practice Assignment: Partitional Clustering Algorithms
  • Practice Assignment: Limitations of k-Means and Importance of Choosing Initial Centroids
  • Practice Assignment: Hierarchical Clustering
  • Practice Assignment: Density-Based Clustering Algorithms

6 Videos

  • Data Clustering and Similarity/Distance Measures
  • Distribution Model-Based Clustering Algorithms
  • Partitional Clustering Algorithms
  • Limitations of k-Means and Importance of Choosing Initial Centroids
  • Hierarchical Clustering
  • Density-Based Clustering Algorithms

2 Readings

  • Essential Reading: Data Clustering
  • Recommended Reading: Data Clustering

Lesson 3: Evaluating the Results of Data Clustering

    • 2 Videos
    • 1 Readings
    • 1 Discussion
    • 1 Lab
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1 Discussions

  • Supervised vs. Unsupervised Learning for Smart City Data

1 Labs

  • Practice Assignment: Characteristics of Data, Clusters, and Clustering Algorithms

2 Videos

  • Cluster Validity
  • Characteristics of Data, Clusters, and Clustering Algorithms

1 Readings

  • Essential Reading: Evaluating the Results of Data Clustering

Practice Quiz : Week 4

    • 1 Assignment
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1 Assignment

  • Practice Quiz: Unsupervised Learning

Graded Quiz : Week 4

    • 1 Assignment
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1 Assignment

  • Graded Quiz: Unsupervised Learning

Live Session

    • 1 Readings
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1 Readings

  • Live Session 2

Lesson 1: Introduction to Anomaly Detection

    • 3 Videos
    • 1 Readings
    • 2 Lab
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2 Labs

  • Practice Assignment: The Anomaly Detection Problem
  • Practice Assignment: Anomaly Detection Techniques: Part 2 

3 Videos

  • The Anomaly Detection Problem 
  • Anomaly Detection Techniques: Part 1 
  • Anomaly Detection Techniques: Part 2 

1 Readings

  • Essential Reading: Introduction to Anomaly Detection

Lesson 2: Avoiding False Discoveries

    • 2 Videos
    • 1 Readings
    • 2 Lab
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2 Labs

  • Practice Assignment: Statistical Significance Testing 
  • Practice Assignment: Hypothesis Testing

2 Videos

  • Statistical Significance Testing 
  • Hypothesis Testing

1 Readings

  • Essential Reading: Avoiding False Discoveries

Practice Quiz : Week 5

    • 1 Assignment
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1 Assignment

  • Practice Quiz: Anomaly Detection and Result Validation

Graded Quiz : Week 5

    • 1 Assignment
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1 Assignment

  • Graded Quiz: Anomaly Detection and Result Validation

Lesson 1: Neural Networks

    • 3 Videos
    • 1 Readings
    • 1 Lab
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1 Labs

  • Practice Assignment: ANN Applications

3 Videos

  • Neuron and ANN Models
  • Multilayer Feed-Forward ANNs
  • ANN Applications

1 Readings

  • Essential Reading: Neural Networks

Lesson 2: Deep Learning

    • 3 Videos
    • 1 Readings
    • 1 Lab
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1 Labs

  • Practice Assignment: Training a Deep Neural Network

3 Videos

  • Introduction to Deep Learning
  • Training a Deep Neural Network
  • Deep Learning for Smart Cities

1 Readings

  • Essential Reading: Deep Learning 

Lesson 3: Hidden Markov Models (HMMs)

    • 4 Videos
    • 1 Readings
    • 1 Discussion
    • 2 Lab
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1 Discussions

  • Learning Smart City Data: Conventional vs. Deep learning

2 Labs

  • Practice Assignment: Graphical Models 
  • Practice Assignment: Discrete-State HMMs

4 Videos

  • Markov Process
  • Graphical Models 
  • Discrete-State HMMs
  • Applications of HMMs to Smart Cities

1 Readings

  • Essential Reading: Hidden Markov Models (HMMs)

Practice Quiz : Week 6

    • 1 Assignment
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1 Assignment

  • Practice Quiz: Advanced Data Mining Techniques

Staff-Graded Assignment: Multi-layer Perceptron and Markov Process

    • 1 Readings
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Staff-Graded Assignment: Multi-layer Perceptron and Markov Process

1 Labs

  • Jupyter Lab for Project (Part 1)

1 Readings

  • How to Attempt and Submit the Project

Live Session

    • 1 Readings
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1 Readings

  • Live Session 3

Course Wrap-Up

    • 1 Videos
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1 Videos

  • Course Wrap-up

Course End Survey

    • 1 Plugin
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Auto Summary

Unlock the potential of IoT in urban development with "Data Mining for Smart Cities." Dive into data science and AI, guided by expert instructors. Master Python-based data mining techniques, analyze urban data, and extract actionable insights to enhance city life. Perfect for beginners, this foundational course offers hands-on experiments with real-life datasets. Available through Coursera with Starter and Professional subscription options. Transform urban data into smart solutions!

Dr. Dheeraj Kumar
Instructor

Dr. Dheeraj Kumar