INTRODUCTION
TO WEB3
Course concept
and description
This course will provide a comprehensive overview of the fast-developing world of Web3 and blockchain technologies. We will cover the history of the web, from Web1 to Web3, and focus on the applications of blockchains, cryptocurrencies, and decentralization through technology.
Students will learn about blockchains, the decentralization of trust and power through technology, and how to launch a cryptocurrency token and create non-fungible tokens. We will also discuss the ethical implications and policy questions surrounding decentralization, and students will have the opportunity to follow a tutorial to create their own smart contracts.
The course will be taught through a combination of in-class lectures and asynchronous learning, with micro-education materials provided by the Blockchain Academy and access to their education platform for an additional certificate.
Instructor:
Maltem Ballan
Meltem Ballan is a neurodiverse computer engineer and neuroscientist. She researches the effects of AI and studies Sustainable AI solutions on different disciplines, behavioral interventions and software tools as medical treatment, reducing the environmental effects of blockchain and fashion using AI. She is passionate about TRUE Diversity and Inclusion in the STEM domain, particularly AI and Data Science.
Biography:
Meltem is an accomplished technology executive and educator with a unique combination of analytical and business expertise developed over 20 years both in industry and academia. She is a pioneering woman data scientist (the first woman neurodiversity fellow at GM) who has nurtured and mentored hundreds of budding analysts and scientists as a recognized leader and as an advisory board member. She is a technology M&A advisor and partner at Great-Orion Holding.
She co-founded a technology startup providing a big-data analytics and ML platform. Managed large multinational and multidisciplinary projects in automotive, aviation, healthcare, software and marketing. She established labs, worked on academic projects, and authored over 30 publications on ML/AI implementation, analytics and neuroscience.
She worked as a senior member of Chief Data and Analytics Office at General Motors. She partnered with startup incubators (Global 500 and ERANYC) to evaluate their early seed company portfolio on tech stacks. During her career, she has designed complex machine learning models and implemented AI projects, including natural language processing (NLP), linear and logistic regression, supervised and unsupervised learning, neural network, deep learning algorithms and hybrid approaches of computer vision. Her passion for cognitive and biological bases of data prompted her to have a career in academia, where she received a post graduate degree in Complex Systems and Brain Sciences with a minor in cognitive and behavioral neuroscience. She implemented her knowledge of neuroscience and analytics while a professor at the University of North Carolina Chapel Hill Medical School.
She is a regular invited speaker for institutions and conferences, including CES, UT Austin, UT Dallas, Harvard, MIT, and Stanford, along with technology and data science conferences within the US and internationally. Ballan joined the Bennington faculty in the Fall of 2022.
What students will Learn:
Blockchain basics: Introduction to blockchain technology, blockchain terminology, blockchain properties, centralization vs decentralization, blockchains in action, a brief history of money, properties of money, how money is created, Chaumian e-cash, and the issue of double spending;
Introduction to Bitcoin and Ethereum: Money backed by computation, hash cash and proof of work, the Bitcoin blockchain, how Bitcoin solves the double spend issue, bitcoin issuance, introduction to Ethereum, smart contracts, differences between Bitcoin and Ethereum, Ethereum transactions;
Blockchain and cryptography: Cryptography in blockchain, public/private keypairs, and how wallets work;
Hashing and block mining: What is a hash, properties of cryptographic hashing, how Bitcoin blocks are mined, anatomy of a Bitcoin block, what is forking, what is hash power, how miners can tamper with proof of work, energy implications of proof of work, other ways to mine/validate a block, proof of stake;
Blockchain nodes and networking: Distributed systems, running a blockchain node, how blockchain nodes communicate, interfacing with the blockchain network, full nodes vs archive nodes vs light clients, the consensus in depth;
Web3 and the future of blockchain: Problems that blockchain can solve, layer 0, layer 1, and layer two blockchains, anonymity and confidential transactions, zkledger, zkrollups, scams and attacks, culture, public policy, regulations, blockchain governance, cryptocurrencies and DeFi, DAOs, interoperability and scalability, NFTs, the metaverse, decentralized computing.
Prerequisites: Basic computer skills and general familiarity with how computers work. Comfort working in Linux, Unix, Windows, or MacOS.
Textbook and Required Materials:
Materials will be presented a week before each topic and updated on the syllabus;
Access to the Blockchain Academy education platform.
Grading policy:
The course will have a midterm exam and a final exam. Both exams will have written and oral sections. Written exams will be conducted individually or in a small setting. The oral exam will be optional for students who want to practice their presentation skills. The presentations will boost your results by 5%. For example, if your grade is 80 from midterm and you give a presentation, your midterm will be 84. Assignments that will affect your grade up to 25%.
Attendance policy:
Students are required to attend 90 percent of the classes. Each student is required to be in the classroom on time. If they are 5 min late they will be counted as absent.
Academic integrity policy:
Students are responsible for their own work and their own actions related to this class. Students will turn in their own assignments, work alone on all assignments unless otherwise directed by the instructor, follow the rules provided for all assignments and examinations, will not resubmit previous work they have done, and will avoid dishonesty in interactions with peers and faculty. If students witness or become aware of a violation of academic integrity, they are encouraged to communicate this to the appropriate faculty member.
Examples of academic integrity violations are provided here but are not limited to these examples:
Submitting another’s work as one's own, including purchasing a paper or other content for the assignment.
Sharing/giving graded or ungraded work to another student, for example, circulating exam questions.
Plagiarism & Self-plagiarism: Submitting the same or similar assignment, or any other content, that the student has previously completed from the same or different courses as original work without the instructor’s permission.
Falsifying, forging, or altering signatures and course documents.
Breaches of student identity or impersonating other students
