Duration
2 days
Overview
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.
Training Objectives
This course will help you to differentiate between the roles of a Data Analyst, Data Scientist, and Data Engineer. You will learn the responsibilities of a Data Analyst and exactly what data analysis entails. You will be able to summarize the data ecosystem, such as databases and data warehouses. You will then uncover the major vendors within the data ecosystem and explore the various tools on-premise and in the cloud. Continue this exciting journey and discover Big Data platforms such as Hadoop, Hive, and Spark. By the end of this course you will be able to visualize the daily life of a Data Analyst, understand the different career paths that are available for data analytics, and identify the many resources available for mastering this profession.
Day 1
9:00am-10:30am
Module 1: 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.
Lessons
- What is this Specialization About?
- Course Introduction
- Big Data Overview
- What is "Big Data"?
10:30am-10:45am Morning break 10:45am-12:30pm
- Digital Footprint
- Political Data-fusion and No-Sampling (Part 1)
- Political Data-fusion and No-Sampling (Part 2)
- Real-time
- Machine Learning
- Machine Learning Recommender Systems
Module 2: 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.
Lessons
- Big Data Limitations Overview
- Big Data Limitations
- Footprint ≠ Representativeness
- Data ≠ Reality
3:30pm-3:45pm Afternoon Teabreak 3:45pm-5:00pm
- Meaning ≠ Meaningful
- Discrimination ≠ Personalization
- Correlation ≠ Causation
- Past ≠ Future
Day 2
9:00am-10:30am
Module 3: 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.
Lessons
- Introduction to Artificial Intelligence
- A Short History of AI
- State of the Art
- The Most Intelligent Gamer
- Search and Robotics
- Vision and Machine Learning
- AI Challenges
- Moral Frames
10:30am-10:45am Morning break 10:45am-12:30pm
- Predictions From Morals
- Moral Brain Signatures
- Computational fMRI
- (A Personal) History of Dialogue Systems
- The Art of Dialogue
- Making Conversations
- AI Telling Stories
Module 4: 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.
Lessons
- Human Downgrading
- Attention Economy
- Exploiting Cognitive Biases
- Persuasive Tech is Everywhere
- Digital Exit Strategy
- Introduction to Research Ethics
- Origins: Unethical Medical Research
- Unethical Social Research
- Taking Responsibility
- The Common Rule
3:30pm-3:45pm Afternoon Teabreak 3:45pm-5:00pm
- Ethical Computational Social Science
- Concerns of an AI Pioneer
- Walker on Ethics
- Shelton on Ethics
- Language Acquisition
- Modeling Framework
- Computational Model
- Lessons Learned
Programme scope, delivery arrangements and suitability are confirmed during enquiry.