{"id":29573012,"url":"https://github.com/gperdrizet/ds-12","last_synced_at":"2026-05-17T17:45:53.411Z","repository":{"id":300855392,"uuid":"1007386877","full_name":"gperdrizet/ds-12","owner":"gperdrizet","description":"Course materials for 4Geeks Academy data science cohort 12","archived":false,"fork":false,"pushed_at":"2025-09-16T00:57:21.000Z","size":10987,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-09-16T02:41:34.624Z","etag":null,"topics":["data-science","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/gperdrizet.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-06-23T23:17:01.000Z","updated_at":"2025-09-16T00:57:24.000Z","dependencies_parsed_at":"2025-07-10T04:14:30.567Z","dependency_job_id":"775d96b2-2bfb-4c10-ae87-52f8decdf5c9","html_url":"https://github.com/gperdrizet/ds-12","commit_stats":null,"previous_names":["gperdrizet/ds-12"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/gperdrizet/ds-12","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fds-12","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fds-12/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fds-12/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fds-12/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gperdrizet","download_url":"https://codeload.github.com/gperdrizet/ds-12/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fds-12/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33148740,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-17T09:28:26.183Z","status":"ssl_error","status_checked_at":"2026-05-17T09:27:52.702Z","response_time":107,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","python"],"created_at":"2025-07-19T05:12:48.261Z","updated_at":"2026-05-17T17:45:48.403Z","avatar_url":"https://github.com/gperdrizet.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ds-12\nCourse materials for ds-12\n\n1. [YouTube playlist](https://youtu.be/607QEWYZQpU?si=rBIrfjwxsHJk3xf4)\n2. [Module slides](https://github.com/gperdrizet/ds-12/blob/main/pages/slides.md)\n3. [Project solutions](https://github.com/gperdrizet/ds-12/blob/main/pages/solutions.md)\n4. [How-to guides](https://github.com/gperdrizet/ds-12/blob/main/pages/guides.md)\n\n\n## Extras\n\n### 2025-07-18\n\nOpenAI just released their ChatGPT based agent yesterday - here are the details:\n\n- Press release/FAQ style overview: [ChatGPT agent](https://help.openai.com/en/articles/11752874-chatgpt-agent)\n- Full technical details: [ChatGPT Agent System Card](https://cdn.openai.com/pdf/839e66fc-602c-48bf-81d3-b21eacc3459d/chatgpt_agent_system_card.pdf)\n\n\n### 2025-07-16\n\nWhile we are on the 'math' portion of the course one good, if a little obscure, Python library to know about is [SymPy](https://www.sympy.org/en/index.html). It does symbolic math in Python - including derivatives. We won't run into it often, but its good to know its out there in case you ever need it. Here's and example from the documentation - calculating the first derivative of a cosine function:\n\n```python\nimport sympy as sp\n\nx = sp.symbols('x')\nderivative = sp.diff(sp.cos(x), x)\n\nprint(f'First derivative: str(derivative)')\n```\n```text\nFirst derivative: -sin(x)\n```\n\n\n### 2025-07-14\n\nAs promised here is an 'extra' assignment which will walk you through hard-coding your own optimizer in Python to fit a linear model to toy data. Highly recommend taking a look - the assignment will give you a good 'gut' feeling for what is happening under the hood when we train machine learning models:\n\n[Linear Regression \u0026 Optimization Assignment](https://github.com/4GeeksAcademy/gperdrizet-optimization-bonus-assignment)\n\n2024 Nobel prize in physics was awarded for early research which lead to modern neural networks. The prize was shared between two researchers: John Hopfield, who invented the 'Hopfield network' and Geoffrey Hinton, who designed early gradient descent algorithms.\n\n1. [2024 Nobel Prize in Physics](https://www.nobelprize.org/prizes/physics/2024/popular-information/): description of the history and importance of the works\n2. [ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION](https://arxiv.org/pdf/1412.6980): Scientific paper describing ADAM, one of the most common/popular optimization algorithms for training neural networks (note the publication year and the first authors affiliations!).\n\n\n### 2025-07-11\n\nInteresting further topic to read up on while we are learning about APIs: [Model Context Protocol](https://modelcontextprotocol.io/introduction). MCP was originally proposed by Anthropic, but is an open standard that anyone can use. It's basically a type of API designed for LLMs and agents to use. It standardizes communication between the model and data source, allowing a way to easily use and share tools for building agents. See also [A2A](https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/) (Google) and [ACP](https://www.ibm.com/think/topics/agent-communication-protocol) (IBM) - same idea, but for communication between agents.\n\n\n### 2025-07-02\n\nCool talk by Bohan Zhang of OpenAI's infrastructure team - covers their implementation of PostgreSQL and shows what is possible with a cutting edge, production grade SQL database at a top company: [OpenAI: Scaling PostgreSQL to the Next Level](https://www.pixelstech.net/article/1747708863-openai%3a-scaling-postgresql-to-the-next-level).\n\n\n### 2025-06-27\n\nUseful Pandas methods for the real estate data cleanup assignment:\n\n1. `.sort_values()` used to sort a dataframe\n2. `.unique()` \u0026 `.nunique()` used to get information about unique values in a dataframe/series\n3. `.isna()` checks for NaN (not a number) missing value placeholders\n3. `.dropna()` used to remove NaN (not a number) missing value placeholder from a dataframe or series\n\nYou can find more information about what these methods do and how to use them in the Pandas [DataFrame](https://pandas.pydata.org/docs/reference/frame.html) and [general function](https://pandas.pydata.org/docs/reference/general_functions.html) documentation.\n\nThere is a whole module about plotting coming up - but for now, a quick skim of the Matplotlib [hist](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html) documentation should be enough to complete the last question.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgperdrizet%2Fds-12","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgperdrizet%2Fds-12","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgperdrizet%2Fds-12/lists"}