DMG Data Science Awesome: A Turkish-Language Curated Index, Not a Toolkit
Source for Who're Interested in Data!
At a glance
- What is it?
- The repository is a hand-maintained README of data science, machine learning and generative AI links, written mostly in Turkish. It ships no code, no CLI and no installable package, so its value depends entirely on whether you read Turkish and whether the link list matches your stack.
- Who is it for?
- Adopt DMG-Data-Science-Awesome only if your team reads Turkish and wants a single README that mixes structured topics (Python and SQL, data science, machine learning, books, papers) with unstructured ones (generative AI, NLP, deep learning, computer vision, MLOps and cloud). Skip it if you need English-language material, a machine-readable index, or anything resembling a runnable tool.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 127 days ago.
- What is it written in?
- GitHub does not report a main language for this repository.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
What the repository actually is
This is a README, not a library. There is no source directory of importable code, no package manifest, no test suite and no release. The GitHub metadata lists no primary language and no releases, which is consistent with a project whose entire deliverable is a Markdown file plus an images folder referenced as /images/awesome.gif and /images/datacommit.png. The licence is MIT, declared both in the repository metadata and through the License: MIT badge in the README. The problem it addresses is discovery: a newcomer to data science in Turkey faces a scattered set of Turkish blogs, YouTube channels and translated course material, and this repository collects them into one page. The audience is therefore narrow and specific. It is for Turkish-speaking learners and practitioners who want a reading list, not for engineers looking for a dependency to add to a requirements file.
The two-column table of contents and its categories
The README opens with a table that splits the index into two halves. The left column is labelled Structured and contains Python & SQL, Data Science, Machine Learning, Data Books and Data Papers. The right column is labelled Unstructured and contains GenerativeAI, Natural Language Processing, Deep Learning, Computer Vision, and MLOps & Cloud. Each entry in that table is an anchor link into a section further down, and every section is wrapped in a <details> element with a <summary>Click to expand!</summary> line. That markup choice matters in practice: on GitHub the page loads collapsed, so you click a category open rather than scrolling a wall of links. Each expanded section repeats a back-to-top anchor written as [`^ back to top ^`](#dmg-data-science-awesome). The Structured versus Unstructured split is a taxonomy, not a technical claim; nothing in the repository enforces it, and the placement of MLOps & Cloud under Unstructured is a judgement call by the maintainers rather than a property of the tools listed there.
What a single section contains
The Python & SQL section shows the pattern. It begins with a Useful Links subsection holding seven entries: an SQL Cheatsheet hosted on Google Drive, sqlzoo.net, two Hackerrank practice domains (one for Python, one for SQL), NeetCode for data structures, algoleague, and the Grind 75 question list. Below that come two more subsections, SQL and Python, with three and four entries respectively. The descriptions are one line each and written in Turkish, for example the CS50 Python entry is described as Harvard's well-known CS50 course in a Python-specific version. The Data Science section adds a second layer of structure with emoji-prefixed subheadings for Video Tutorials, Blog Tutorials and Virtual Environment, and it points at roadmap.sh/ai-data-scientist for a concept flow diagram. So the granularity varies by section: some categories are a flat list, others are nested two or three levels deep. That inconsistency is real and you should expect to re-read a section twice before you find the entry you half-remember.
There is nothing to install
You cannot run this project. There is no pip install, no npm package, no Dockerfile and no CLI. The only way to use it is to open the repository page or clone it and read README.md. If you want a local copy, git clone https://github.com/Developer-MultiGroup/DMG-Data-Science-Awesome.git is the whole setup, and the only configuration involved is your Markdown viewer. The one operational instruction the README does give is procedural rather than technical: before contributing to the resource archive, it asks readers to look at the Contribution Guide at ./CONTRIBUTING.md. That file is the actual specification for how entries get added, and it is not reproduced in the README, so anyone planning to submit a link has to read it separately. There is also a Buy Me a Coffee badge linking to buymeacoffee.com/MultiGroup, which is funding, not installation.
The Turkish-language constraint
Most of the surrounding prose is Turkish, including the section blurbs, the descriptions attached to each link, and the event promotion. The README also points to Turkish-language resources directly, such as python.yazbel.com for learning Python in Turkish, veridefteri.com for Turkish blog and video material, and the Yapay Zekâ Araştırma İnisiyatifi page, described in the README as a site compiling translated versions of good deep learning resources. This is the project's main differentiator and its main limitation at the same time. A team that works in English will find the link titles useful but the annotations opaque, and the Turkish-specific entries will be dead weight. A team that works in Turkish gets something that is genuinely hard to assemble otherwise. Note that the metadata topics (artificial-intelligence, awesome, awesome-repos, data-science, generative-ai, machine-learning) are in English while the content is largely not, so the repository is more discoverable than it is readable for an English-only audience.
Maintenance cost and the link-rot problem
A curated list has one recurring cost: link checking. The repository has no releases, so there is no versioned snapshot you can pin to, and the default branch main is the only version that exists. The last push recorded in the metadata is 2026-05-11, which tells you the list is being touched, but it says nothing about whether individual links still resolve. Several entries point at third-party hosts that move or expire, including a Google Drive file for the SQL cheatsheet, a rawgit CDN URL for the Awesome badge, and YouTube playlist and channel links. If you fork this for internal use, the maintenance work is yours: you inherit a Markdown file with no automated validation, and every stale link becomes a support question. The MIT licence makes forking and internal redistribution straightforward, and it imposes no copyleft obligation on your own code. That is a statement about the licence text the repository declares, not legal advice; if you plan to republish the list inside a commercial product, read the full MIT text and decide for yourself.
Where a different resource fits better
The obvious alternative is the parent list this repository is modelled on: sindresorhus/awesome, whose badge the README displays at the top. The difference in approach is scale and language, not format. That list is an index of indexes, maintained in English, and it routes you to a subject-specific awesome list rather than holding the links itself. If your need is a single English page of data science links, you are better served by one of the data science lists that awesome points to, because you avoid the translation layer entirely. If your need is a runnable environment rather than a reading list, neither project helps and you want a distribution or a container image instead. The honest comparison is this: DMG-Data-Science-Awesome competes on Turkish-language curation and on the DataCommit connection, and it loses on breadth, on machine-readability and on English coverage. Pick it for the first two reasons or not at all.
The DataCommit section and the community angle
The README promotes an online series called DataCommit, described as an event where attendees can ask questions of data experts, open to everyone, with past recordings on a YouTube playlist. It also states that recommended resources from DataCommit are accessible through a DataCommit anchor in the same README, and the table of contents implies a section by that name. This is the part of the project that a plain link list cannot replicate: the entries carry the context of a live session and a named group behind them. It also means the index has an editorial centre of gravity. Resources recommended during those events are more likely to appear than resources that were not, and the README's own framing ties the archive to that series. If you disagree with that curation, you are free to fork under MIT, but you will be maintaining the result yourself, link by link, with no tooling in the repository to help you.
Editorial conclusion
Adopt DMG-Data-Science-Awesome only if your team reads Turkish and wants a single README that mixes structured topics (Python and SQL, data science, machine learning, books, papers) with unstructured ones (generative AI, NLP, deep learning, computer vision, MLOps and cloud). Skip it if you need English-language material, a machine-readable index, or anything resembling a runnable tool. Before relying on it, open CONTRIBUTING.md and check the DataCommit section, since those two files define what the maintainers accept and which links come from their own event series; the rest of the README is a plain list whose entries you will have to vet yourself.
Community notes