Data Mining for Smart Cities from Coursera

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.

What's inside

Syllabus

Getting Started with the 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.

M1: Introduction to Data Mining for Smart Cities

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.

M2: Introduction to Python Programming for Data Mining

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.

M3: Supervised Learning

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.

M4: Unsupervised Learning

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.

M5: Anomaly Detection and Result Validation

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.

M6: Advanced Data Mining Techniques

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.

Final Project

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Examines Internet of Things' impact on city life, which is highly relevant to smart city initiatives

Provides hands-on labs and interactive materials, which facilitate practical application of concepts

Uses Python, a widely-used programming language in data mining, ensuring relevance to industry

Taught by Dr. Dheeraj Kumar, who has expertise in data mining and machine learning

Builds a strong foundation for beginners in data mining and machine learning

Requires prerequisites in probability and statistics, which may not be accessible to all learners

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Mining for Smart Cities with these activities:

Review Python Basics

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Review basic Python programming concepts to strengthen your foundation for data mining tasks.

Browse courses on Python

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Go over Python syntax and data types
Practice writing simple Python scripts

Participate in Peer Coding Sessions

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Foster your understanding and problem-solving skills by engaging in peer coding sessions where you can collaborate with fellow learners.

Browse courses on Collaborative Learning

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Find a study group or online community for data mining
Participate in coding sessions or discussions
Share your knowledge and learn from others

Follow Tutorials on Data Preprocessing

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Enhance your data mining skills by following guided tutorials that focus on data preprocessing techniques.

Browse courses on Data Preprocessing

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Find tutorials on data preprocessing using Python
Go through the tutorials and practice the techniques
Apply the techniques to real-life datasets

Three other activities

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Attend Data Mining Workshops

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Expand your knowledge and network with experts by attending data mining workshops or industry events where you can learn about the latest advancements and best practices.

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Research upcoming data mining workshops or conferences
Register for and attend the event
Engage with speakers and other attendees

Solve Supervised Learning Exercises

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Deepen your understanding of supervised learning algorithms by solving practice exercises and applying them to real-world data.

Browse courses on Supervised Learning

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Find online exercises or practice problems for supervised learning
Attempt to solve the exercises using Python
Compare your solutions with provided answers or discuss them in online forums

Contribute to Smart City Data Mining Projects

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Gain practical experience and contribute to the data mining community by participating in open-source projects related to smart city data analysis.

Browse courses on Open Source Projects

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Identify open-source projects related to smart city data mining
Explore the projects and find areas where you can contribute
Submit pull requests or contribute to documentation

Career center

Learners who complete Data Mining for Smart Cities will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts use data mining and machine learning techniques to analyze data and identify trends. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers design and build machine learning models. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use data mining and machine learning techniques to extract insights from large datasets. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

See salaries and explore the career path for Data Scientist

Business Analyst

Business Analysts use data mining and machine learning techniques to help businesses make better decisions. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers design and develop software applications. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

Quantitative Analysts use data mining and machine learning techniques to analyze financial data. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use data mining and machine learning techniques to analyze risk. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

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Market Researcher

Market Researchers use data mining and machine learning techniques to analyze market data. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

See salaries and explore the career path for Market Researcher

Insurance Analyst

Insurance Analysts use data mining and machine learning techniques to analyze insurance data. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

See salaries and explore the career path for Insurance Analyst

Healthcare Analyst

Healthcare Analysts use data mining and machine learning techniques to analyze healthcare data. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

See salaries and explore the career path for Healthcare Analyst

Operations Research Analyst

Operations Research Analysts use data mining and machine learning techniques to solve business problems. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

See salaries and explore the career path for Operations Research Analyst

Statistician

Statisticians use data mining and machine learning techniques to analyze data. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

See salaries and explore the career path for Statistician

Financial Analyst

Financial Analysts use data mining and machine learning techniques to analyze financial data. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

See salaries and explore the career path for Financial Analyst

Risk Analyst

Risk Analysts use data mining and machine learning techniques to analyze risk. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

See salaries and explore the career path for Risk Analyst

Data Engineer

Data Engineers design and build data pipelines. This course can help you develop the skills needed to be successful in this role by providing you with a foundation in data mining and machine learning. You will learn how to use Python to read and preprocess data, and then implement various data mining tasks to obtain desired results.

See salaries and explore the career path for Data Engineer