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Data Science & Machine Learning
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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
Больше📈 Аналитический обзор Telegram-канала Data Science & Machine Learning
Канал Data Science & Machine Learning (@datasciencefun) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 75 800 подписчиков, занимая 2 117 место в категории Образование и 4 312 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 75 800 подписчиков.
Согласно последним данным от 16 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 924, а за последние 24 часа — 38, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.47%. В первые 24 часа после публикации контент обычно набирает 1.42% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 629 просмотров. В течение первых суток публикация набирает 1 075 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 5.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, accuracy, distribution, panda, dataset.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“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”
Благодаря высокой частоте обновлений (последние данные получены 17 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
75 800
Подписчики
+3824 часа
+2197 дней
+92430 день
Архив постов
𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐉𝐨𝐛 𝐎𝐩𝐞𝐧𝐢𝐧𝐠𝐬 𝐈𝐧 𝐌𝐲𝐧𝐭𝐫𝐚 🔥
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Industry Data Science vs Academia Data Science
Comparing Data Science in academia and Data Science in industry is like comparing tennis with table tennis: they sound similar but in the end, they are completely different!
5 big differences between Data Science in academia and in industry 👇:
1️⃣ Model vs Data: Academia focuses on models, industry focuses on data. In academia, it’s all about trying to find the best model architecture to optimise a defined metric. In industry, loading and processing the data accounts for around 80% of the job.
2️⃣ Novelty vs Efficiency: The end goal of academia is often to publish a paper and to do so, you will need to find and implement a novel approach. Industry is all about efficiency: reusing existing models as much as possible and applying them to your use case.
3️⃣ Complex vs Simple: More often than not, academia requires complex solutions. I know that this isn’t always the case but unfortunately, complex papers get a higher chance of being accepted at top conferences. In industry, it’s all about simplicity: trying to find the simplest solution that solves a specific problem.
4️⃣ Theory vs Engineering: To succeed in academia, you need to have strong theoretical and maths skills. To succeed in industry, you need to develop strong engineering skills. It is great to be able to train a model in a notebook but if you cannot deploy your model in production, it will be completely useless.
5️⃣ Knowledge impact vs $ impact: In academia, it’s all about creating new work and expanding human knowledge. In industry, it is all about using data to drive value and increase revenue.
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗚𝘂𝗶𝗱𝗲😍 | 𝗖𝗿𝗮𝗰𝗸 𝗬𝗼𝘂𝗿 𝗡𝗲𝘅𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄💫
Preparing for your first Data Analytics interview can feel overwhelming, but not anymore! 🚀
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Hey Guys👋,
The Average Salary Of a Data Scientist is 14LPA
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1️⃣ Key Concepts
Master statistics, machine learning, and programming basics. They’re always top priorities!
2️⃣ Essential Tools
Know your way around Python, SQL, and data visualization platforms like Tableau or Power BI.
3️⃣ Real-World Projects
Be ready to explain your projects—what problem you solved, how you did it, and the results you achieved! 🌟
4️⃣ Problem-Solving Skills
Practice coding challenges and case studies.
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Expected Salary:- 12 LPA
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🚨30 FREE Dataset Sources for Data Science Projects🔥
Data Simplifier: https://datasimplifier.com/best-data-analyst-projects-for-freshers/
US Government Dataset: https://www.data.gov/
Open Government Data (OGD) Platform India: https://data.gov.in/
The World Bank Open Data: https://data.worldbank.org/
Data World: https://data.world/
BFI - Industry Data and Insights: https://www.bfi.org.uk/data-statistics
The Humanitarian Data Exchange (HDX): https://data.humdata.org/
Data at World Health Organization (WHO): https://www.who.int/data
FBI’s Crime Data Explorer: https://crime-data-explorer.fr.cloud.gov/
AWS Open Data Registry: https://registry.opendata.aws/
FiveThirtyEight: https://data.fivethirtyeight.com/
IMDb Datasets: https://www.imdb.com/interfaces/
Kaggle: https://www.kaggle.com/datasets
UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php
Google Dataset Search: https://datasetsearch.research.google.com/
Nasdaq Data Link: https://data.nasdaq.com/
Recommender Systems and Personalization Datasets: https://cseweb.ucsd.edu/~jmcauley/datasets.html
Reddit - Datasets: https://www.reddit.com/r/datasets/
Open Data Network by Socrata: https://www.opendatanetwork.com/
Climate Data Online by NOAA: https://www.ncdc.noaa.gov/cdo-web/
Azure Open Datasets: https://azure.microsoft.com/en-us/services/open-datasets/
IEEE Data Port: https://ieee-dataport.org/
Wikipedia: Database: https://dumps.wikimedia.org/
BuzzFeed News: https://github.com/BuzzFeedNews/everything
Academic Torrents: https://academictorrents.com/
Yelp Open Dataset: https://www.yelp.com/dataset
The NLP Index by Quantum Stat: https://index.quantumstat.com/
Computer Vision Online: http://www.computervisiononline.com/dataset
Visual Data Discovery: https://www.visualdata.io/
Roboflow Public Datasets: https://public.roboflow.com/
Computer Vision Group, TUM: https://vision.in.tum.de/data/datasets
🚀𝐁𝐨𝐨𝐬𝐭 𝐘𝐨𝐮𝐫 𝐂𝐚𝐫𝐞𝐞𝐫 𝐰𝐢𝐭𝐡 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭’𝐬 𝐅𝐫𝐞𝐞 𝐂𝐨𝐮𝐫𝐬𝐞𝐬!
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Complete Roadmap to become a data scientist in 5 months
Free Resources to learn Data Science: https://t.me/datasciencefun
Week 1-2: Fundamentals
- Day 1-3: Introduction to Data Science, its applications, and roles.
- Day 4-7: Brush up on Python programming.
- Day 8-10: Learn basic statistics and probability.
Week 3-4: Data Manipulation and Visualization
- Day 11-15: Pandas for data manipulation.
- Day 16-20: Data visualization with Matplotlib and Seaborn.
Week 5-6: Machine Learning Foundations
- Day 21-25: Introduction to scikit-learn.
- Day 26-30: Linear regression and logistic regression.
Work on Data Science Projects: https://t.me/pythonspecialist/29
Week 7-8: Advanced Machine Learning
- Day 31-35: Decision trees and random forests.
- Day 36-40: Clustering (K-Means, DBSCAN) and dimensionality reduction.
Week 9-10: Deep Learning
- Day 41-45: Basics of Neural Networks and TensorFlow/Keras.
- Day 46-50: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Week 11-12: Data Engineering
- Day 51-55: Learn about SQL and databases.
- Day 56-60: Data preprocessing and cleaning.
Week 13-14: Model Evaluation and Optimization
- Day 61-65: Cross-validation, hyperparameter tuning.
- Day 66-70: Evaluation metrics (accuracy, precision, recall, F1-score).
Week 15-16: Big Data and Tools
- Day 71-75: Introduction to big data technologies (Hadoop, Spark).
- Day 76-80: Basics of cloud computing (AWS, GCP, Azure).
Week 17-18: Deployment and Production
- Day 81-85: Model deployment with Flask or FastAPI.
- Day 86-90: Containerization with Docker, cloud deployment (AWS, Heroku).
Week 19-20: Specialization
- Day 91-95: NLP or Computer Vision, based on your interests.
Week 21-22: Projects and Portfolios
- Day 96-100: Work on personal data science projects.
Week 23-24: Soft Skills and Networking
- Day 101-105: Improve communication and presentation skills.
- Day 106-110: Attend online data science meetups or forums.
Week 25-26: Interview Preparation
- Day 111-115: Practice coding interviews on platforms like LeetCode.
- Day 116-120: Review your projects and be ready to discuss them.
Week 27-28: Apply for Jobs
- Day 121-125: Start applying for entry-level data scientist positions.
Week 29-30: Interviews
- Day 126-130: Attend interviews, practice whiteboard problems.
Week 31-32: Continuous Learning
- Day 131-135: Stay updated with the latest trends in data science.
Week 33-34: Accepting Offers
- Day 136-140: Evaluate job offers and negotiate if necessary.
Week 35-36: Settling In
- Day 141-150: Start your new data science job, adapt to the team, and continue learning on the job.
ENJOY LEARNING 👍👍
𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐉𝐨𝐛𝐬 𝐈𝐧 𝐓𝐨𝐩 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 😍
Companies Hiring:-
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Expected Salary:- 8 To 24 LPA
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𝐀𝐈 & 𝐌𝐋 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 𝐅𝐫𝐨𝐦 𝐓𝐨𝐩 𝐈𝐧𝐬𝐭𝐢𝐭𝐮𝐭𝐢𝐨𝐧𝐬!😍
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In every family tree, there is 1 person who breaks out the middle-class chain and works hard to become a millionaire and changes the lives of everyone forever.
May that be you in 2025.
Happy New Year!
What's the most significant achievement you accomplished in 2024, and what's the target for 2025?
𝐈𝐧𝐟𝐨𝐬𝐲𝐬 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐇𝐢𝐫𝐢𝐧𝐠 𝐃𝐫𝐢𝐯𝐞😍
Office Location:- Bangalore
Role:- Data Analyst
Qualification:- MCA,MTech,MBA,BTech,BCA,Bachelor of Engineering
Expected Salary:- 10 To 25LPA
𝐀𝐩𝐩𝐥𝐲 𝐧𝐨𝐰👇:-
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𝐈𝐧𝐟𝐨𝐬𝐲𝐬 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐇𝐢𝐫𝐢𝐧𝐠 𝐃𝐫𝐢𝐯𝐞😍
Office Location:- Bangalore
Role:- Data Analyst
Qualification:- MCA,MTech,MBA,BTech,BCA,Bachelor of Engineering
Expected Salary:- 10 To 25LPA
𝐀𝐩𝐩𝐥𝐲 𝐧𝐨𝐰👇:-
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