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Martin Hilbert
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/2mcoqu/big
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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.
Read more
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.
Read more
Big Data Limitations
In this module, you will be able to explain the limitations of big data. You will work with an AI interface, IBM Watson, and discover how AI can identify personality through Natural Language Processing. You will analyze the personality of a person.
Artificial Intelligence
In this module, you will discover the history of artificial intelligence (AI) and its fields of study. You'll be able to examine how AI is used through case studies. You will be able to discuss the application of AI and you will use AI to create a unique artifact through a hands-on exercise.
Research Ethics
In this module, you will be able to define the term research ethics. You will be able to examine the role ethics plays in conducting research. You will be able to discuss how ethics is applied when using AI and big data.
Good to know
Good to know
Know what's good ,
what to watch for , 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
Valuable course on big data and ai ethics
Learners say this five-star rated course provides excellent insights into Big Data, Artificial Intelligence (AI), and research ethics. Students particularly appreciate the engaging instructors, informative lectures, and exploration of current use cases and ethical dilemmas. However, some note that the sound quality in a few recordings could be better.
The course's engaging and exciting instructors create an environment that fosters learning.
"This course has a very engaging and exciting instructor that motivates me to learn more about this subject."
"Really it's very gud online course..... They will teach very gud"
"Great new insight in data, ai and ethics and great attractive video's."
The course is designed to be accessible to beginners, providing clear explanations and a solid foundation in the subject matter.
"Excellent course. Helps in developing a good base in artificial intelligence for beginners."
"A perfect course for beginners and non-computational professionals who are curious to explore Digital foot print, the state of the art Artificial Intelligence applications and the research ethics."
"This course is really very interesting.I have learned a lot about big data,artificial intelligence and ethics."
The course takes an in-depth look at the ethical implications of AI and research, providing valuable insights for those working in the field.
"Excellent course, lots of interesting concepts covering the field of artificial intelligence and a welcomed take on research ethics."
"An excellent and very complete ethics course, it has good current examples."
"The course is very interesting and provides a good basis for what digital footprint is, the limits of big data and a sense of artificial intelligence."
In some videos, the sound quality is poor, making it difficult to understand the speaker.
"Volume and speed of recordings are not as good. Needed to follow text most of the time, except for Dr Hilbert."
"The course provides an overall introduction to the theme and it is quite helpful. However, there were some videos with a very poor sound and it was necessary to add subtitles to understand better."
"I like the specialization. It offers a quick first glance on many topics of computational social sciences but don't expect deep insights. I see the course as a point of reference to start exploring more sophisticated tools, methods, and theories. Again, prof. Hilbert is an entertaining lecturer and his collaborators add practical and theoretical expertise. Be prepared that the peer-review process might take a couple of days. Again, the tools used are simple and limited in scope, but a good opportunity to get your hands dirty."
For those seeking a more hands-on approach, the course could benefit from including more practical exercises.
"Great and dynamic teachers - very philosophical and theoretical approach to a subject I consider to be practical (hence, I would've liked more practical exercises)."
"I expected it to give a brief knowledge about AI and big data with the help of coding, but it only showed the past of it and its ethics, which was not quite helpful."
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
Show steps
Refresh understanding of big data, digital footprint, and data representation to prepare for the course.
Show steps
-
Define big data and digital footprint
-
Discuss opportunities and limitations of big data
Review Probability and Statistics Concepts
Show steps
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
Show steps
-
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'
Show steps
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
Show steps
-
Read the book thoroughly.
-
Take notes and highlight key concepts.
-
Discuss the book's ideas with peers or online.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Explore the IBM Watson Developer Cloud
Show steps
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.
Show steps
-
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
Show steps
Identify personality traits and emotions from text using IBM Watson's AI to reinforce module 1 and 2.
Show steps
-
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
Show steps
Regularly practicing data manipulation and analysis will enhance your proficiency in handling and extracting insights from data.
Browse courses on
Data Manipulation
Show steps
-
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
Show steps
Develop a basic understanding of machine learning algorithms and hands-on experience building a model to complement module 3.
Show steps
-
Choose a machine learning algorithm
-
Develop a training dataset
-
Train and evaluate the model
Participate in a Study Group for Machine Learning
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
Apply knowledge of AI and ethics to create a hypothetical AI tool that addresses a social issue, reinforcing module 3 and 4.
Show steps
-
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
Show steps
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
Show steps
-
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
Show steps
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
Show steps
-
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.
Review Big Data Concepts
Show steps
Refresh understanding of big data, digital footprint, and data representation to prepare for the course.
Show steps
Show steps
Show steps
- Define big data and digital footprint
- Discuss opportunities and limitations of big data
Review Probability and Statistics Concepts
Show steps
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
Show steps
Show steps
Show steps
- 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'
Show steps
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
Show steps
Show steps
Show steps
- Read the book thoroughly.
- Take notes and highlight key concepts.
- Discuss the book's ideas with peers or online.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Explore the IBM Watson Developer Cloud
Show steps
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.
Show steps
Show steps
Show steps
- 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
Show steps
Identify personality traits and emotions from text using IBM Watson's AI to reinforce module 1 and 2.
Show steps
Show steps
Show steps
- 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
Show steps
Regularly practicing data manipulation and analysis will enhance your proficiency in handling and extracting insights from data.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- 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
Show steps
Develop a basic understanding of machine learning algorithms and hands-on experience building a model to complement module 3.
Show steps
Show steps
Show steps
- Choose a machine learning algorithm
- Develop a training dataset
- Train and evaluate the model
Participate in a Study Group for Machine Learning
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
Apply knowledge of AI and ethics to create a hypothetical AI tool that addresses a social issue, reinforcing module 3 and 4.
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
Show steps
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
Show steps
Show steps
Show steps
- 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
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.
Machine Learning 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.
Business Intelligence Analyst
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.
Data Analyst
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.
Data Architect
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.
Data Scientist
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.
Data Engineer
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.
Database Administrator
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.
Computer Scientist
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.
Software Engineer
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.
Systems Analyst
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.
IT Architect
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.
Product Manager
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.
Marketing Manager
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.
Project 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.
For more career information including salaries, visit:
OpenCourser.com/course/2mcoqu/big
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.
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.
Save
Provides a comprehensive overview of big data, its potential, and its challenges. It will help you understand the fundamental concepts of big data and how it is used in various industries.
Provides a comprehensive overview of the ethical issues surrounding AI. It will help you understand the potential benefits and risks of AI and how to use it responsibly.
Provides a comprehensive overview of the ethical issues surrounding AI. It will help you understand the potential benefits and risks of AI and how to use it responsibly.
Save
Explores the global race for AI supremacy. It will help you understand the challenges and opportunities that AI presents for countries around the world.
Explores the impact of the Fourth Industrial Revolution on society and the economy. It will help you understand the challenges and opportunities that AI and other emerging technologies are creating.
Explores the history and future of AI. It will help you understand the potential of AI and how it is likely to impact our lives in the years to come.
Explores the impact of AI on society, the economy, and the future of humanity. It will help you understand the challenges and opportunities that AI presents.
Save
Provides a comprehensive overview of deep learning, a subfield of AI that has made significant advances in recent years. It will help you understand the fundamental concepts of deep learning and how it is used to solve complex problems.
Explores the challenge of ensuring that AI is aligned with human values. It will help you understand the challenges of developing AI safely and responsibly.
Save
Explores the future of humanity in light of AI and other emerging technologies. It will help you understand the challenges and opportunities that we face as we navigate the future.
Save
Explores the potential risks and benefits of AI. It will help you understand the challenges of developing AI safely and responsibly.
Save
Explores the future of humanity in light of AI and other emerging technologies. It will help you understand the challenges and opportunities that we face as we navigate the future.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2mcoqu/big
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Effort
4 weeks of study, 2-5 hours/week
Level
Introductory
Via
Coursera
Institution
University of California, Davis
Instructor
Martin Hilbert
Language
English
Transcripts
Español
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Italiano
中文
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آذربایجان دیلی
This course belongs to a
collection
called
Computational Social Science. It's comprised of five courses:
- Social Network Analysis
- Computational Social Science Methods
- Big Data, Artificial Intelligence, and Ethics (viewing)
- Computational Social Science Capstone Project
- Computer Simulations
Good to know
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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