IIT Roorkee

Data Mining for Smart Cities

Taught in English

Course

Gain insight into a topic and learn the fundamentals

Dr. Dheeraj Kumar

Instructor: Dr. Dheeraj Kumar

Beginner level

Recommended experience

58 hours to complete
3 weeks at 19 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.

Details to know

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Assessments

11 quizzes

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There are 7 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 quizzes1 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 quizzes3 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 quizzes1 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 quizzes1 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 quizzes4 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 quiz1 discussion prompt4 ungraded labs

Instructor

Dr. Dheeraj Kumar
IIT Roorkee
2 Courses104 learners

Offered by

IIT Roorkee

Recommended if you're interested in Data Analysis

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