Offered By

Data Mining for Smart Cities

Indian Institute of Technology Roorkee

About this Course

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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Beginner Level

Basic knowledge of python programming language, probability and statistics. Working with analyzing data to extract knowledge.

Approx. 62 hours to complete

English

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What you will 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 will gain

Mathematics
Python Programming

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Beginner Level

Basic knowledge of python programming language, probability and statistics. Working with analyzing data to extract knowledge.

Approx. 62 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Dr. Dheeraj Kumar

Assistant Professor

Electronics and Communications Engineering

59 Learners

2 Courses

Offered by

Indian Institute of Technology Roorkee

Indian Institute of Technology Roorkee is among the foremost of institutes of national importance in higher technological education and in engineering, basic and applied research. More than 170 years old, IIT Roorkee ranks amongst the best technological institutions in the world and has contributed to all sectors of technological development. It has also been considered a trend-setter in the area of education and research in the field of science, technology, and engineering.

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Syllabus - What you will learn from this course

Week1

Week 1

24 minutes to complete

Getting Started with the Course

24 minutes to complete

2 videos (Total 4 min), 1 reading

5 hours to complete

M1: Introduction to Data Mining for Smart Cities

5 hours to complete

12 videos (Total 79 min), 4 readings, 2 quizzes

12 videos

Necessity of Data Mining7mData and Mining Techniques 8mApplications of Data Mining to Smart Cities 7mData Mining: Common Challenges7mUncertainty and How to Model It7mReview of Random Variables7mPopulation, Samples, and Statistical Inference5mParameter Estimation5mTypes of Collected Data6mData Quality5mData Preprocessing Tasks6mTask Identification5m

4 readings

Essential Reading: Introduction to Data Mining and its Application to Urban Systems15mEssential Reading: Review of Statistical Methods45mRecommended Reading: Review of Statistical Methods10mEssential Reading: Data Pre-Processing – I30m

2 practice exercises

Introduction to Data Mining for Smart Cities1hGraded Quiz: Introduction to Data Mining for Smart Cities30m

Week2

Week 2

8 hours to complete

M2: Introduction to Python Programming for Data Mining

8 hours to complete

6 videos (Total 54 min), 4 readings, 2 quizzes

6 videos

Installing Python Using Anaconda Distribution6mPython Data Types and Data Structures8mPython Libraries for Data Mining: NumPy, SciPy, and Matplotlib8mFeature Scaling and Standardization9mLabel Encoding for Categorical Variables11mHandling Missing Values in the Data 9m

4 readings

Essential Reading: Python Programming for Data Mining 45mEssential Reading: Data Pre-Processing – II45mIntroduction to Project - Multi-layer Perceptron and Markov Process 10mLive Session 11h

2 practice exercises

Introduction to Python Programming for Data Mining1hGraded Quiz: Introduction to Python Programming for Data Mining30m

Week3

Week 3

16 hours to complete

M3: Supervised Learning

16 hours to complete

12 videos (Total 91 min), 5 readings, 2 quizzes

12 videos

Regression Analysis and Its Applications in Smart Cities7mLinear Regression Problem and Its Solution7mAdvanced Regression Models 8mClassification Problem and Logistic Regression7mNaïve Bayes Classifiers6mBayesian Network Classifiers6mDecision Tree Classifiers and How to Train Them9mPopular Decision Tree Algorithms and Their Shortcomings7mLinear SVMs and How to Train Them7mNonlinear SVMs: The Kernel Trick7mEnsemble Classifiers 7mClassifier Performance Evaluation8m

5 readings

Essential Reading: Regression Analysis20mRecommended Reading: Regression Analysis 1h 30mEssential Reading: Introduction to Statistical Classifier20mEssential Reading: Decision Trees20mEssential Reading: Support Vector Machines (SVMs) 30m

2 practice exercises

Supervised Learning1hGraded Quiz: Supervised Learning30m

Week4

Week 4

12 hours to complete

M4: Unsupervised Learning

12 hours to complete

11 videos (Total 79 min), 5 readings, 3 quizzes

11 videos

Introduction and Applications to Urban Systems6mAssociation Rule Mining: Brute Force vs. Apriori7mGenerating Association Rules from Frequent Itemsets6mData Clustering and Similarity/Distance Measures8mDistribution Model-Based Clustering Algorithms6mPartitional Clustering Algorithms5mLimitations of k-Means and Importance of Choosing Initial Centroids7mHierarchical Clustering7mDensity-Based Clustering Algorithms7mCluster Validity8mCharacteristics of Data, Clusters, and Clustering Algorithms6m

5 readings

Essential Reading: Association Rule Mining 30mEssential Reading: Data Clustering30mRecommended Reading: Data Clustering10mEssential Reading: Evaluating the Results of Data Clustering1mLive Session 21h

2 practice exercises

Practice Quiz: Unsupervised Learning1hGraded Quiz: Unsupervised Learning30m

Week5

Week 5

8 hours to complete

M5: Anomaly Detection and Result Validation

8 hours to complete

5 videos (Total 50 min), 2 readings, 2 quizzes

5 videos

The Anomaly Detection Problem 10mAnomaly Detection Techniques: Part 1 9mAnomaly Detection Techniques: Part 2 9mStatistical Significance Testing 10mHypothesis Testing10m

2 readings

Essential Reading: Introduction to Anomaly Detection 1h 20mEssential Reading: Avoiding False Discoveries20m

2 practice exercises

Practice Quiz: Anomaly Detection and Result Validation1hGraded Quiz: Anomaly Detection and Result Validation30m

Week6

Week 6

9 hours to complete

M6: Advanced Data Mining Techniques

9 hours to complete

10 videos (Total 94 min), 3 readings, 2 quizzes

10 videos

Neuron and ANN Models10mMultilayer Feed-Forward ANNs12mANN Applications8mIntroduction to Deep Learning7mTraining a Deep Neural Network11mDeep Learning for Smart Cities7mMarkov Process9mGraphical Models 12mDiscrete-State HMMs6mApplications of HMMs to Smart Cities8m

3 readings

Essential Reading: Neural Networks 1hEssential Reading: Deep Learning 20mEssential Reading: Hidden Markov Models (HMMs) 30m

1 practice exercise

Practice Quiz: Advanced Data Mining Techniques1h

Week7

Week 7

4 hours to complete

Final Project

4 hours to complete

1 video (Total 1 min), 2 readings, 1 quiz

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Data Mining for Smart Cities

Data Mining for Smart Cities

About this Course

What you will learn

Skills you will gain