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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
Показати більше📈 Аналітичний огляд Telegram-каналу Data Science & Machine Learning
Канал Data Science & Machine Learning (@datasciencefun) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 75 821 підписників, посідаючи 2 110 місце в категорії Освіта та 4 270 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 75 821 підписників.
За останніми даними від 19 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 855, а за останні 24 години на 10, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.21%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.26% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 2 431 переглядів. Протягом першої доби публікація в середньому набирає 953 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 3.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як learning, accuracy, distribution, panda, dataset.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
Завдяки високій частоті оновлень (останні дані отримано 20 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.
75 821
Підписники
+1024 години
+1447 днів
+85530 день
Архів дописів
4 AI Certifications for Developers 🔥🔥
1. Intro to AI Ethics
https://kaggle.com/learn/intro-to-ai-ethics
2. AI matters
https://open.edu/openlearn/education-development/ai-matters/content-section-overview
3. Elements of AI
https://course.elementsofai.com
4. Ethics of AI
https://ethics-of-ai.mooc.fi
Hands on Machine Learning with Scikit Learn and Tensorflow.pdf15.42 MB
Chapman_&_Hall_CRC_machine_learning_&_pattern_recognition_series.pdf14.57 MB
First thing if you are a beginner and have decided you want to learn Data Science then know that it is a journey (6 months - 1 year at least) and not a 1 month thing where u do some courses and you are a data scientist. There are different fields in Data Science that you have to first get familiar and strong in basics as well as do hands-on to get the abilities that are required to function in a full time job opportunity. Then further delve into advanced implementations.
There are plenty of roadmaps and online content both paid and free that you can follow. In a nutshell. A few essential things that will be necessary and in no particular order that will at least get your data science journey started are below:
Basic Statistics, Linear Algebra, calculus, probability
Programming language (R or Python) - Preferably Python if you rather want to later on move into a developer role instead of sticking to data science.
Machine Learning - All of the above will be used here to implement machine learning concepts.
Data Visualisation - again it could be simple excel or via r/python libraries or tools like Tableau,PowerBI etc.
This can be overwhelming but again its just an indication of what lies ahead. So most important thing is to just START instead of just contemplating the best way to go about this. Since lot of things can be learnt independently as well in no particular order.
You can use the below Sources to prepare your own roadmap:
@free4unow_backup - some free courses from here
@datasciencefun - check & search in this channel with #freecourses
Python - https://bit.ly/45q7pxh
Kaggle - https://www.kaggle.com/learn
Data Science Fundamentals for Python and MongoDB
David Paper, 2018
📚 Title: TensorFlow Developer Certificate Guide (2023)
👇👇
https://t.me/machinelearning_deeplearning/79
Top Platforms for Building Data Science Portfolio
Build an irresistible portfolio that hooks recruiters with these free platforms.
Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.
1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace
Want to practice for your next interview?
Then use this prompt and ask Chat GPT to act as an interviewer
https://t.me/learndataanalysis/536
New developers: whenever you work on something interesting, write it down in a document which you keep updating. This will be very helpful when you need to create a resume or have to talk about your achievements in an interview. (Or for college essays.)
I can guarantee you that if you don't do this, you will forget half the interesting things you've done; and for a majority of us, our brains are experts in convincing us that we haven't really done anything interesting.
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Additional Resources To Assist Research
https://www.reddit.com/r/MachineLearning/
• https://www.reddit.com/r/deeplearning/
• https://paperswithcode.com/
• https://papers.nips.cc/
• https://icml.cc/
• https://iclr.cc/
• https://www.researchgate.net/
©How fresher can get a job as a data scientist?©
ans:-India as a job market is highly resistant to hire data scientist as a fresher. Everyone out there asks for at least 2 years of experience, but then the question is where will we get the two years experience from?
The important thing here to build a portfolio. As you are a fresher I would assume you had learnt data science through online courses. They only teach you the basics, the analytical skills required to clean the data and apply machine learning algorithms to them comes only from practice.
Do some real-world data science projects, participate in Kaggle competition. kaggle provides data sets for practice as well. Whatever projects you do, create a GitHub repository for it. Place all your projects there so when a recruiter is looking at your profile they know you have hands-on practice and do know the basics. This will take you a long way.
All the major data science jobs for freshers will only be available through off-campus interviews.
Some companies that hires data scientists are:
Siemens
Accenture
IBM
Cerner
Creating a technical portfolio will showcase the knowledge you have already gained and that is essential while you got out there as a fresher and try to find a data scientist job.
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Free Datasets to practice data science projects
1. Enron Email Dataset
Data Link: https://www.cs.cmu.edu/~enron/
2. Chatbot Intents Dataset
Data Link: https://github.com/katanaml/katana-assistant/blob/master/mlbackend/intents.json
3. Flickr 30k Dataset
Data Link: https://www.kaggle.com/hsankesara/flickr-image-dataset
4. Parkinson Dataset
Data Link: https://archive.ics.uci.edu/ml/datasets/parkinsons
5. Iris Dataset
Data Link: https://archive.ics.uci.edu/ml/datasets/Iris
6. ImageNet dataset
Data Link: http://www.image-net.org/
7. Mall Customers Dataset
Data Link: https://www.kaggle.com/shwetabh123/mall-customers
8. Google Trends Data Portal
Data Link: https://trends.google.com/trends/
9. The Boston Housing Dataset
Data Link: https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html
10. Uber Pickups Dataset
Data Link: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city
11. Recommender Systems Dataset
Data Link: https://cseweb.ucsd.edu/~jmcauley/datasets.html
Source Code: https://bit.ly/37iBDEp
12. UCI Spambase Dataset
Data Link: https://archive.ics.uci.edu/ml/datasets/Spambase
13. GTSRB (German traffic sign recognition benchmark) Dataset
Data Link: http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset
Source Code: https://bit.ly/39taSyH
14. Cityscapes Dataset
Data Link: https://www.cityscapes-dataset.com/
15. Kinetics Dataset
Data Link: https://deepmind.com/research/open-source/kinetics
16. IMDB-Wiki dataset
Data Link: https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/
17. Color Detection Dataset
Data Link: https://github.com/codebrainz/color-names/blob/master/output/colors.csv
18. Urban Sound 8K dataset
Data Link: https://urbansounddataset.weebly.com/urbansound8k.html
19. Librispeech Dataset
Data Link: http://www.openslr.org/12
20. Breast Histopathology Images Dataset
Data Link: https://www.kaggle.com/paultimothymooney/breast-histopathology-images
21. Youtube 8M Dataset
Data Link: https://research.google.com/youtube8m/
