{"signature": "0xcdb8240d345440681284acaf06b4c9eb730e1f00330dba6eddb3af660ad65a3575e1043ec3fbd2f533eb6edd67f78f97a0851650e0772fc90e84789ea7ae48e81b", "application": {"round": "0x0c0412ddb846096ea1e37de717921ebf4fef9a39", "answers": [{"type": "email", "hidden": true, "question": "Email Address", "questionId": 0, "encryptedAnswer": {"ciphertext": "QX3J21vhEmIanmmgVLE9dI4klPkee0/uGMQ5/i+TJfnMY/0QBWES/t8es2eN9/jg", "encryptedSymmetricKey": "918eb5e81301aa84044a661058d877a8af1594982f50f4a48e967a79875197c681a5c6b08c2e7e137a9e4577122f6b149b952e38874a15c88918dc4411e43707143122326e7d55817f74255dfaeb712bdae951edccfd0ac6ad1f2ad56e95e7b4452da9c19432ab5828ac4481810884b7223ef9f4150127a8b9008e8b9b3dc70500000000000000201ed80160016e60f1590b18262869a42a8993030f608e29aa4e3ef2da78a5745a43104a0a6b09b5b3ae5d5bb9099f68ab"}}, {"type": "short-answer", "answer": "Donations", "hidden": false, "question": "Funding Sources", "questionId": 1}, {"type": "number", "answer": "3", "hidden": false, "question": "Team Size", "questionId": 2}, {"type": "short-answer", "hidden": true, "question": "Telegram username", "questionId": 3}, {"type": "paragraph", "answer": "Approximately $5,000", "hidden": true, "question": "Total prior funding for project in USD", "questionId": 4}, {"type": "paragraph", "hidden": true, "question": "Profiles or socials of other main team members publicly associated with project", "questionId": 5}, {"type": "short-answer", "answer": "36", "hidden": true, "question": "How old is the project? (Months)", "questionId": 6}, {"type": "paragraph", "answer": "We want to share our joy of data science with the Ethereum eco-system through an informative set of online courses and case studies that merge data analysis, blockchain analytics and statistical programming.\n\nWe aspire to create a modular online course to help new blockchain developers understand the principles and best practices of data science. Using open data sources from across the Ethereum landscape (think StateoftheDApps, UniSwap, & Etherscan), the course will teach the topics of data munging, data visualization, exploratory data analysis, machine learning, and dabble in a bit of deep learning and artificial intelligence. As the course grows, we intend to create case studies around how to apply data science to specific DApps (think machine learning techniques for predicting markets with Numer.ai data or analyzing traits in CryptoKitties).\n\nOur ultimate vision is to spur and inspire the next generation of developers through interesting applications of data science to emerging blockchain technologies.", "hidden": false, "question": "Describe your project in a few paragraphs (ideally including a problem statement, solution, roadmap, and timeline for implementation)", "questionId": 7}, {"type": "paragraph", "answer": "We are the only open source Ethereum centric data science learning course on Github. That naturally implies that we offer a freely accessible course, that teaches both data science and web3 concepts in such a way that spurs interest and collaboration across both fields. This adheres to both the spirit and the letter of decentralized science!", "hidden": false, "question": "How does your project support the mission or goals of DeSci?", "questionId": 8}, {"type": "paragraph", "answer": "Funds will be used to find and incentivize new contributors to the course. ", "hidden": false, "question": "Describe how funds from the grants program will be used for your project. ", "questionId": 9}, {"type": "paragraph", "hidden": true, "question": "If you participated in past grant rounds (i.e. DeSci GR15/ Beta) using a different project payout wallet address, please share it here.", "questionId": 10}, {"type": "paragraph", "answer": "To date our \"Learning Data Science on the Ethereum Blockchain\" course is made up of 5 modules consisting of over 38 different topics, 11 videos and covers both R and Python programming. With 119 repo stars, 29 forks and 4 active watchers, we are ready to further build upon the foundation we've established by including new case studies touching on modern Machine Learning and AI as applied to projects on the Ethereum blockchain. Stay tuned to our Github to see the newest course updates.", "hidden": false, "question": "If this project participated in past grant rounds, please share any new updates or milestones since the last round.", "questionId": 11}, {"type": "paragraph", "hidden": true, "question": "Is there any other information you would like to share about your project, previous work, other project affiliations, or potential conflicts of interest? (or anything not covered elsewhere in this application that you feel is relevant to share)", "questionId": 12}, {"type": "checkbox", "answer": ["Yes"], "hidden": false, "question": "Does 50% of your team reflect the diversity of the community your project serves with a particular emphasis on including women, Black, Indigenous, and People of Color (BIPoC), individuals with disabilities, and members of the LGBTQ+ community?", "questionId": 13}, {"type": "multiple-choice", "answer": "I already read the above exclusion criteria and understand those exclusion criteria are not permitted and will result in rejection or removal from the round.", "hidden": true, "question": "Please read the following exclusion criteria that are not permitted and will result in rejection or removal from the round.", "questionId": 14}], "project": {"id": "0x63a01b4264c704216da0be99d7b2bb1ad2bd414365eeada4af114e8f0183213a", "title": "Learning Data Science on the Ethereum Blockchain with @omnianalytics", "logoImg": "bafkreihcyoo7lfvta5iyvvyhb3pabewyrgevujaednjcnmb2hhaaebj6oe", "metaPtr": {"pointer": "bafkreihibrn3f7uy27mo5phuwdscr2ldezuogv7bthyqbhppxuhkgmlquq", "protocol": "undefined"}, "website": "https://github.com/Omni-Analytics-Group/eth-data-science-course", "bannerImg": "bafkreic2kbfvy5qvqj2r2arwrp3ovuorfcllrwj5w3f4grnillbdhq67n4", "createdAt": 1681833930870, "userGithub": "omnianalytics", "credentials": {"twitter": {"type": ["VerifiableCredential"], "proof": {"jws": "eyJhbGciOiJFZERTQSIsImNyaXQiOlsiYjY0Il0sImI2NCI6ZmFsc2V9..6x3t7oocGzLlRcKkSlSKBv0G6N7P5V7L8GOp1jHgQgEFIwvb_BIu2MSDfJK6_okYfFk4Rpz3IoWdidMMvLWHBg", "type": "Ed25519Signature2018", "created": "2023-04-18T16:04:26.534Z", "proofPurpose": "assertionMethod", "verificationMethod": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC#z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC"}, "issuer": "did:key:z6MkghvGHLobLEdj1bgRLhS4LPGJAvbMA1tn2zcRyqmYU5LC", "@context": ["https://www.w3.org/2018/credentials/v1"], "issuanceDate": "2023-04-18T16:04:26.534Z", "expirationDate": "2023-07-17T16:04:26.534Z", "credentialSubject": {"id": "did:pkh:eip155:1:0x41D2a18E1DdACdAbFDdADB62e9AEE67c63070b76", "hash": "v0.0.0:E0dC+6b0/4V+V0J2qCgqUFYPt8M/2yClikp8BHUJLzk=", "@context": [{"hash": "https://schema.org/Text", "provider": "https://schema.org/Text"}], "provider": "ClearTextTwitter#OmniAnalytics"}}}, "description": "We want to share our joy of data science with the Ethereum eco-system through an informative set of online courses and case studies that merge data analysis, blockchain analytics and statistical programming.\n\n\n\nWe aspire to create a modular online course to help new blockchain developers understand the principles and best practices of data science. Using open data sources from across the Ethereum landscape (think DefiLlama, UniSwap, & Etherscan), the course will teach the topics of data munging, data visualization, exploratory data analysis, machine learning, and dabble in a bit of deep learning and artificial intelligence. As the course grows, we intend to create case studies around how to apply data science to specific DApps (think machine learning techniques for predicting markets with Numer.ai data or analyzing traits in Hashmasks).\n\n\n\nOur ultimate vision is to spur and inspire the next generation of developers through interesting applications of data science to emerging blockchain technologies.\n\n\n\nCourse Link: https://github.com/Omni-Analytics-Group/eth-data-science-course\n\n\nCourse Outline\n💻Module 1 - Basic Data Structures and Munging\n\nMotivating Example (Slides, Python Slides, Video)\nReading Files (Slides, Python Slides, Video)\nBasics (Slides, Video)\nData structures (Slides, Video)\n\n\nData Sources: CryptoPunks, Crypto Art Pulse\n\n\n\n📊 Module 2 - Statistical Graphics and Visualization\n\nWhy graph?\nVisualization Principles and Practices\nPlotting Basics\nBuilding Plots Layer by Layer\nPolishing Your Graphs\n\n\nData Sources: Omni Analytics Group, CryptoPunks\n\n\n\n🤖 Module 3 - Supervised and Unsupervised Machine Learning\n\nThe ML workflow\nSupervised learning for classification\nUnsupervised learning for grouping\nForecasting what's next\nDeep learning for sequences\n\n\nData Sources: Omni Analytics Group\n\n\n\n💡 Module 4 - Case Studies\n\nClustering and segmenting Ethereum validator performance (R, Python, Video)\nVisualizing slashings in the Ethereum Medalla testnet (R, Python)\nReconstructing the Crypto Sentiment Investment Curve in ggplot2\nInteracting with and Analyzing Numerai Network Growth with GraphQL and ggplot2\nTornado.Cash Initial Distribution Analysis\nStable Coin Analysis\nUsing R Shiny to explore Numerai tournament data (Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7, Part 8, Part 9)\nCrypto Punks NFT Value Analysis (Slides, Video)\nHashmask Rarity Analysis (Slides, Video)\nGeocoding and Mapping Cryptojobs (Slides, Video)\nCryptojobs Exploratory Data Analysis (Slides, Video)\nMaking Graphs fun with BadgerDAO\nAn Exploratory Data Analysis of PoolTogether\nAn Exploratory Data Analysis of Yearn.Finance\nInteracting with the Covalent API via httr\nAnalyzing the Ethereum Name Service (ENS) (Slides)\nForecasting the Trend in Bitcoin Dominance (Slides)\nAn Analysis of the Uniswap Platform (Slides)\nForefront Social Token Analysis (Slides)\nGitcoin Grants Analysis (Slides)\nPredicting Growth in L2 Chains (Slides)\nA Statistical Dive into the Unlock Protocol (Slides, Video)\nFilecoin Miner Index API Exploration (Slides)\nUniswap Airdrop Liquidity Provider Analysis (Slides)\nUniswap Governance Analysis (Slides)\n\n\nData Sources:  Beaconscan, Numerai Tournament Data, Haskmasks, Crypto Punks, Crypto Jobs, PoolTogether, BadgerDAO, Yearn Finance, ENS, Uniswap, Unlock Protocol, Optimisim, ForeFront, Filecoin\n\n\n\n💜 Module  5 - Understanding and Defending Gitcoin\n\nQuadratic Funding in the Wild: A Post-Round Analysis of Gitcoin’s Fund Matching Mechanism (Slides)\nGitcoin Grants Analysis (Slides)\n\n\nData Sources: Gitcoin, Omni Analytics Group\n\n\n\n🎁 Lagniappe\n\n\n\nThe Omniacs’ Data Science Code Snippet Tweet Book Vol 1\nKids Data Collection Worksheet\n\n\n\nUpdates\n\nGitcoin Grant Round 8\n\n\n\nLike everything else in the world, 2020 flipped our course development plans upside down. Instead of building the course from the bottom up, we chose to repurpose and refactor our Medalla research into motivating case studies on how to perform data analysis on Ethereum 2.0 blockchain data. The case studies walk through, in detail, how we performed the analysis that ultimately netted us a bronze prize. We also included a tutorial on how to use R Shiny as an exploration tool for understanding the Numer.ai dApp's tournament data.\n\n\n\n📝 Change log\n\nRestructured the repository for clarity\nAdded 3 case studies\n\n\nGitcoin Grants Round 9\n\n\n\nFor Round 9 we've doubled down on our \"case study first\" approach to teaching data science using projects on the Ethereum blockchain as examples. This update includes a look at NFTs, stable coins, market capitalization estimation, and an introduction to GraphQL. We expanded two of our original case studies to include Python version, so if you are interested in learning more about that language you can check those out here and here. This update also includes our first attempt at creating video lectures for the eager learners who would like to dive deeper into the concepts. We intend to use funding from this round to further expand the set of case studies we produce and improve the quality of our video content.\n\n\n\n📝 Change log\n\nMajor update to the course aesthetics\nModule 1 updated\n5 New case studies (with 2 more to be published during the active grant round)\nFirst video lecture published to Youtube\n\n\nGitcoin Grants Round 10\n\n\n\nThis round update had us working with our first outside contributor. @Amelia188 gave our course a proper copy edit by fixing tons of typos, correcting grammar errors and improving the overall readability of the material. We look forward to her continued contributions and encourage others to reach out to us about opportunities to collaborate. To further expand out the base content for the course, we completed the material for the second module that focuses on statistical graphics. Other updates for this round include two NFT related case studies and a host of new video lectures. Your support for this round will help us expand our contributor pool and further improve on the quality of our content.\n\n\n\n📝 Change log\n\nTons of copy edits\n4 Video lectures for Module 1 have been published\nSlides for Module 2 - Statistical Graphics and Visualization have been completed\n2 NFT related case studies were created and videos produced\n\nGitcoin Grants Round 11\n\n\n\nWe've been busy! Over the last quarter we've been working with DAOs to help them understand their data and it has been this work that inspired us to create two new case studies all about blockchain jobs using data from Cryptojobs. In addition to these two case studies, we've included another one on Yearn.Finance created by our newest collaborator @vintro. This update also includes 3 new videos to supplement the case studies. As usual, we really appreciate the support and contributions this round will help us find and compensate additional course contributors.\n\n\n\n📝 Change log\n\n3 video lectures\n3 New case studies\nVarious copy edits\n\n\nGitcoin Grants Round 12\n\n\n\nOur update for this round is on the smaller side. We have fixed a few links and added a couple of case new case studies. If you can, be a little patient with us. We're going to try to come back with some big updates for the next round. Stay tuned!\n\n\n\n📝 Change log\n\n2 New case studies\nVarious link fixes and copy edits\n\n\nGitcoin Grants Round 13\n\n\n\nIn addition to making progress on Module 3, we've doubled down on our cross-platform initiative to include more Python coded examples. We now have the first two sections of Module 1 translated into Python. A special shout out to @JSchoonmaker! Stay tuned because we're actually going to be updating the course throughout this round.\n\n\n\n📝 Change log\n\nAdded a Python tutorials for Module 1\nUpdates to Module 3 (sit tight!)\nVarious link fixes and copy edits\n\n\nGitcoin Grants Round 14\n\n\n\nWe’re bouncing all around for this season! The new case studies for this round touch on forecasting L2 contract deployment, exploring ENS domain names, characterizing Filecoin miners, understanding Unlock Protocol contract interactions, and analyzing Uniswap with the uniswappeR package. We’ll be updating throughout the period so don’t be too surprised if a few new case studies or additional content pops up out of nowhere! Also, we don't want to forget to extend kudos to @NadiaAntony for her course content contributions!\n\n\n\n📝 Change log\n\n5+ new Case Studies\nVarious link fixes and copy edits\nMinor updates to Module 3\n\n\nGitcoin Grant Round 15 and Beta Round\n\nThis round we've decided to experiment with a little bit of alternative styled content! In the lagniappe folder you'll find a compilation \"Tweet Book\" of R programming related tweets from our Twitter. As our first foray into children's content, we created a data collection worksheet that has young, budding data scientists describing crypto currency logos in spreadsheet form. It should be a short, fun exercise for anyone getting used to how data is structured. We've also included two new Uniswap-centric case studies, one of which won an award for most insightful analysis of the protocol's governance. With that, we hope you enjoy this update!\n\n\n\n📝 Change log\n\n2 new Case Studies\nAdded our first kids data collection worksheet\nAdded Vol. 1 of our quick stats \"Tweet book\"\nVarious link fixes\n\n\n\n\nFAQ\n\nDo you all have experience in this stuff?\n\nWhy yes, we do! Omni Analytics Group is a team of PhD level statistical consultants that have been teaching and solving difficult data science problems for nearly a decade. We are passionate about data science and blockchain technologies. Just check out our twitter.\n\n\n\nDo I need any prior experience before taking this course?\n\nOur intention is to start from the beginning and build up not only your data chops, but your statistical intuition and programming knowledge. At the end of these courses, you should be able to match a statistical technique to a blockchain data problem, write a basic script to analyze it and confidently search online for more advanced knowledge.\n\n\n\nWhat programming languages will the course focus on?\n\nWe'll initially focus on the statistical language R, but then expand to Python. As the course grows, we hope to include examples with contracts written in Solidity.\n\n\n\nCan I request a topic?\n\nSure! Once we flesh out the initial course material. If funding persists, we'd be more than happy to take suggestions on case studies or topics.\n\n\nTestimonials\n\n\"This course is like a rain following a drought. It kindly walks you through the process starting from the use of R to introduction of graphs and machine learning concepts with interesting case studies. I strongly recommend it not only to researchers interested in Ethereum blockchain but also to any students or professionals that have interest in learning data analysis and science.\" - Will Shin (Principal Economist at Klaytn)\n\n\nImpact and Accolades\n\n\nMarch 11th, 2022 - 59 Stars - 16 Forks\nJune 8th, 2022     - 76 Stars - 17 Forks\nSept 6th, 2022     - 90 Stars - 21 Forks\nApril 16th, 2023  - 101 Stars - 25 Forks\n\n\nFeatured Projects\n\n\nCryptoPunks\nCrypto Art Pulse\nNumerai\nTornado Cash\nHashmasks\nCryptojobs\nBadgerDAO\nYearn.Finance\nCovalent\nPoolTogether\nENS\nUnlock Protocol\nFilecoin\nOptimism\nPolygon\nUniswap\nGitcoin\n", "lastUpdated": 0, "projectGithub": "", "projectTwitter": "omnianalytics"}, "recipient": "0x41d2a18e1ddacdabfddadb62e9aee67c63070b76"}}