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Big Data, Artificial Intelligence, and Ethics

This course gives you context and first-hand experience with the two major catalyzers of the computational science revolution: big data and artificial intelligence. With more than 99% of all mediated information in digital format and with 98% of the world population using digital technology, humanity produces an impressive digital footprint. In theory, this provides unprecedented opportunities to understand and shape society. In practice, the only way this information deluge can be processed is through using the same digital technologies that produced it. Data is the fuel, but machine learning it the motor to extract remarkable new knowledge from vasts amounts of data. Since an important part of this data is about ourselves, using algorithms in order to learn more about ourselves naturally leads to ethical questions. Therefore, we cannot finish this course without also talking about research ethics and about some of the old and new lines computational social scientists have to keep in mind. As hands-on labs, you will use IBM Watson’s artificial intelligence to extract the personality of people from their digital text traces, and you will experience the power and limitations of machine learning by teaching two teachable machines from Google yourself.

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What's inside

Syllabus

Getting Started and Big Data Opportunities

In this module, you will be able to define the idea of big data and digital footprint. You will be able to discuss how big data is represented in social science and identify the opportunities of big data.

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Read about what's good

what should give you pause

and possible dealbreakers

Taught by instructor Martin Hilbert, a widely recognized expert in big data and computational social science

Develops a strong foundational understanding of big data analysis, machine learning modeling, and research ethics

Provides unique insights into the ethical considerations of using big data and AI in social science research

Suitable for individuals with prior knowledge in social science research or related fields

Offers a comprehensive overview of computational social science, covering topics such as data representation, AI-based personality analysis, and applications of AI in the field

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Reviews summary

Introduction to big data, ai, and ethics

According to learners, this course offers a largely positive introduction to the interconnected fields of big data, artificial intelligence, and ethics. Students appreciate the structured approach that balances theoretical concepts with practical, hands-on exercises using tools like IBM Watson and Google Teachable Machines. Many find the ethics module particularly insightful and relevant in today's world. While some note the course is foundational and may lack deep technical detail, it is widely seen as providing a solid overview and sparking further interest. The instructors and materials are frequently praised for clarity and accessibility.

Best suited for beginners or those new.

"It's a good introductory course, especially if you are new to these topics."

"I think this course is best for beginners seeking an overview."

"Those with prior knowledge might find some parts basic, but it's excellent for newcomers."

Course modules are well-organized.

"The course structure was logical and easy to follow."

"Modules flowed well, building upon previous topics."

"I found the organization of the course material very helpful."

Exercises with AI tools are useful.

"The hands-on labs with IBM Watson and Teachable Machines were great."

"Getting to use real AI tools gave me practical experience."

"I liked the practical exercises that reinforced the concepts taught."

Ethics content is relevant and thought-provoking.

"The module on research ethics was timely and very relevant."

"I especially enjoyed the ethics section; it made me think critically."

"Discussing the ethical implications of AI and big data was crucial and well-covered."

Provides a solid overview of key concepts.

"Provides a good foundation in big data, AI, and the ethical considerations."

"I got a great overview of the landscape of big data and AI."

"This course balanced theory and practical examples nicely."

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 Big Data, Artificial Intelligence, and Ethics with these activities:

Review Big Data Concepts

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Refresh understanding of big data, digital footprint, and data representation to prepare for the course.

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Define big data and digital footprint
Discuss opportunities and limitations of big data

Review Probability and Statistics Concepts

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Refreshing your knowledge of probability and statistics will provide a strong foundation for understanding the concepts of machine learning and data analysis.

Browse courses on Probability

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Review textbooks or online resources on probability and statistics.
Solve practice problems.
Review concepts related to data distribution, hypothesis testing, and statistical inference.

Read 'Big Data: A Revolution That Will Transform How We Live, Work, and Think'

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This book provides a comprehensive overview of the big data landscape, its implications, and its potential impact on various aspects of society.

View Reinventing Capitalism in the Age of Big Data on Amazon

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Read the book thoroughly.
Take notes and highlight key concepts.
Discuss the book's ideas with peers or online.

Nine other activities

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Explore the IBM Watson Developer Cloud

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Familiarizing yourself with the IBM Watson Developer Cloud will provide you with access to powerful tools and resources for working with big data and artificial intelligence.

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Create an account on the IBM Watson Developer Cloud.
Explore the various services offered by Watson.
Follow tutorials to learn how to use Watson's APIs.

Analyze Natural Language

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Identify personality traits and emotions from text using IBM Watson's AI to reinforce module 1 and 2.

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Use IBM Watson's Natural Language Processing (NLP) tools
Extract personality traits from text
Identify emotions from text

Practice Data Manipulation and Analysis with Python

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Regularly practicing data manipulation and analysis will enhance your proficiency in handling and extracting insights from data.

Browse courses on Data Manipulation

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Import data into a Python environment.
Clean and preprocess the data.
Explore and analyze the data using statistical methods.
Visualize the data to identify patterns and trends.
Practice data transformation techniques.

Build a Machine Learning Model

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Develop a basic understanding of machine learning algorithms and hands-on experience building a model to complement module 3.

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Choose a machine learning algorithm
Develop a training dataset
Train and evaluate the model

Participate in a Study Group for Machine Learning

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Engaging in a study group will provide you with opportunities to collaborate with peers, discuss concepts, and reinforce your understanding of machine learning.

Browse courses on Machine Learning

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Find or form a study group with other students.
Establish regular meeting times.
Review course materials and discuss concepts together.
Work on assignments and projects collaboratively.

Build a Machine Learning Model to Identify Personality Traits

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Building a machine learning model will provide you with hands-on experience in applying the concepts of machine learning and natural language processing to extract personality traits.

Browse courses on Machine Learning

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Gather a dataset of text data containing personality traits.
Preprocess the data and extract relevant features.
Choose and train a machine learning algorithm.
Evaluate the performance of your model.
Deploy your model and use it to analyze text data.

Design an AI Tool

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Apply knowledge of AI and ethics to create a hypothetical AI tool that addresses a social issue, reinforcing module 3 and 4.

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Identify a social issue
Design the AI tool
Consider the ethical implications
Create a presentation or document showcasing the tool

Attend a Workshop on Ethical Considerations in AI

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Attending a workshop will provide you with valuable insights and practical guidance on the ethical dimensions of AI and its applications.

Browse courses on Ethics in AI

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Research and identify relevant workshops.
Register for the workshop.
Attend the workshop and actively participate in discussions.
Implement ethical practices in your own AI projects.

Develop a Research Proposal on a Topic in Computational Social Science

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Creating a research proposal will challenge you to synthesize your understanding of the field, identify a research gap, and propose a plan for investigação future.

Browse courses on Computational Social Science

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Identify a research topic of interest.
Review existing literature and identify a research gap.
Develop a research question and hypothesis.
Design a research methodology.
Write and submit a research proposal.

Career center

Learners who complete Big Data, Artificial Intelligence, and Ethics will develop knowledge and skills that may be useful to these careers:

Artificial Intelligence Engineer

An Artificial Intelligence Engineer designs, develops, and deploys artificial intelligence systems. This course may be useful, as it provides a foundation in artificial intelligence and its applications.

See salaries and explore the career path for Artificial Intelligence Engineer

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models. This course may be useful, as it provides foundational knowledge in machine learning and artificial intelligence.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A Data Analyst collects, processes, and analyzes data to extract meaningful insights. This course may be useful, as it helps build a foundation in big data and data analysis techniques.

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Business Intelligence Analyst

A Business Intelligence Analyst uses data analysis to provide insights that can help businesses make better decisions. This course may be useful, as it provides a foundation in big data and data analysis techniques.

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Data Architect

A Data Architect designs and manages data systems. This course may be useful, as it provides a foundation in big data and data engineering techniques.

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Data Engineer

A Data Engineer designs, builds, and maintains data systems. This course may be useful, as it helps build a foundation in big data and data engineering techniques.

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Data Scientist

A Data Scientist carries out the creation and deployment of algorithms and prediction models to allow for data analysis. This course may be useful, as it helps build a foundation in big data and artificial intelligence, which are key components of data science. It may be more useful for an individual looking to specialize in a particular application of data science, such as natural language processing.

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Computer Scientist

A Computer Scientist conducts research and develops new computer technologies. This course may be useful, as it provides a foundation in artificial intelligence and its applications, which are key areas of research in computer science.

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Database Administrator

A Database Administrator manages and maintains databases. This course may be useful, as it provides a foundation in big data and data engineering techniques.

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Systems Analyst

A Systems Analyst analyzes and designs computer systems. This course may be useful, as it provides a foundation in artificial intelligence and its applications, which can be valuable for designing systems that incorporate artificial intelligence.

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IT Architect

An IT Architect designs and manages IT systems. This course may be useful, as it provides a foundation in artificial intelligence and its applications, which can be valuable for designing systems that incorporate artificial intelligence.

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Product Manager

A Product Manager plans and manages the development and launch of new products. This course may be useful, as it provides a foundation in artificial intelligence and its applications, which can be valuable for developing products that incorporate artificial intelligence.

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Software Engineer

A Software Engineer designs, develops, and maintains software applications. This course may be useful, as it provides a foundation in artificial intelligence and its applications, which can be valuable for developing software applications that incorporate artificial intelligence.

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Marketing Manager

A Marketing Manager plans and executes marketing campaigns. This course may be useful, as it provides a foundation in artificial intelligence and its applications, which can be valuable for developing marketing campaigns that leverage artificial intelligence.

See salaries and explore the career path for Marketing Manager

Project Manager

A Project Manager plans, executes, and completes projects. This course may be useful, as it provides a foundation in project management skills, which are valuable for managing projects that involve big data and artificial intelligence.

See salaries and explore the career path for Project Manager

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Big Data, Artificial Intelligence, and Ethics.

Artificial Intelligence

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Classic textbook on artificial intelligence. It covers a wide range of topics, from the basics of AI to the latest advances in the field. It will help you understand the fundamental concepts of AI and how it is used to solve real-world problems.

Big Data, Artificial Intelligence, and Ethics

Two deals to help you save

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Introduction to big data, ai, and ethics

Activities

Career center

Reading list

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