Уже доступно! Исследование Telegram 2025 — ключевые инсайты года 
Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence
Открыть в Telegram
Free Datasets For Data Science Projects & Portfolio Buy ads: https://telega.io/c/DataPortfolio For Promotions/ads: @coderfun @love_data
Больше📈 Аналитический обзор Telegram-канала Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence
Канал Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence (@dataportfolio) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 37 738 подписчиков, занимая 3 637 место в категории Технологии и приложения и 10 965 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 37 738 подписчиков.
Согласно последним данным от 10 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 122, а за последние 24 часа — 4, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 5.66%. В первые 24 часа после публикации контент обычно набирает N/A% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 0 просмотров. В течение первых суток публикация набирает 0 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 0.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, dataset, sql, link:-, analyst.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Free Datasets For Data Science Projects & Portfolio
Buy ads: https://telega.io/c/DataPortfolio
For Promotions/ads: @coderfun @love_data”
Благодаря высокой частоте обновлений (последние данные получены 11 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 Технологии и приложения.
37 738
Подписчики
+424 часа
+97 дней
+12230 день
Загрузка данных...
Похожие каналы
Облако тегов
Входящие и исходящие упоминания
---
---
---
---
---
---
Привлечение подписчиков
июнь '26
июнь '26
+56
в 0 каналах
май '26
+291
в 0 каналах
Get PRO
апрель '26
+159
в 1 каналах
Get PRO
март '26
+175
в 1 каналах
Get PRO
февраль '26
+451
в 2 каналах
Get PRO
январь '26
+629
в 1 каналах
Get PRO
декабрь '25
+552
в 2 каналах
Get PRO
ноябрь '25
+707
в 0 каналах
Get PRO
октябрь '25
+607
в 1 каналах
Get PRO
сентябрь '25
+493
в 2 каналах
Get PRO
август '25
+792
в 7 каналах
Get PRO
июль '25
+965
в 3 каналах
Get PRO
июнь '25
+1 338
в 8 каналах
Get PRO
май '25
+2 450
в 10 каналах
Get PRO
апрель '25
+3 541
в 8 каналах
Get PRO
март '25
+1 028
в 9 каналах
Get PRO
февраль '25
+1 039
в 7 каналах
Get PRO
январь '25
+1 365
в 13 каналах
Get PRO
декабрь '24
+996
в 7 каналах
Get PRO
ноябрь '24
+983
в 12 каналах
Get PRO
октябрь '24
+966
в 6 каналах
Get PRO
сентябрь '24
+1 456
в 4 каналах
Get PRO
август '24
+1 429
в 9 каналах
Get PRO
июль '24
+2 284
в 8 каналах
Get PRO
июнь '24
+2 641
в 6 каналах
Get PRO
май '24
+1 937
в 2 каналах
Get PRO
апрель '24
+2 081
в 2 каналах
Get PRO
март '24
+2 387
в 3 каналах
Get PRO
февраль '24
+1 473
в 0 каналах
Get PRO
январь '24
+4 649
в 5 каналах
| Дата | Привлечение подписчиков | Упоминания | Каналы | |
| 11 июня | 0 | |||
| 10 июня | +4 | |||
| 09 июня | +4 | |||
| 08 июня | 0 | |||
| 07 июня | +2 | |||
| 06 июня | +7 | |||
| 05 июня | +10 | |||
| 04 июня | +7 | |||
| 03 июня | +9 | |||
| 02 июня | +6 | |||
| 01 июня | +7 |
Посты канала
Real-world Data Science projects ideas: 💡📈
1. Credit Card Fraud Detection
📍 Tools: Python (Pandas, Scikit-learn)
Use a real credit card transactions dataset to detect fraudulent activity using classification models.
Skills you build: Data preprocessing, class imbalance handling, logistic regression, confusion matrix, model evaluation.
2. Predictive Housing Price Model
📍 Tools: Python (Scikit-learn, XGBoost)
Build a regression model to predict house prices based on various features like size, location, and amenities.
Skills you build: Feature engineering, EDA, regression algorithms, RMSE evaluation.
3. Sentiment Analysis on Tweets or Reviews
📍 Tools: Python (NLTK / TextBlob / Hugging Face)
Analyze customer reviews or Twitter data to classify sentiment as positive, negative, or neutral.
Skills you build: Text preprocessing, NLP basics, vectorization (TF-IDF), classification.
4. Stock Price Prediction
📍 Tools: Python (LSTM / Prophet / ARIMA)
Use time series models to predict future stock prices based on historical data.
Skills you build: Time series forecasting, data visualization, recurrent neural networks, trend/seasonality analysis.
5. Image Classification with CNN
📍 Tools: Python (TensorFlow / PyTorch)
Train a Convolutional Neural Network to classify images (e.g., cats vs dogs, handwritten digits).
Skills you build: Deep learning, image preprocessing, CNN layers, model tuning.
6. Customer Segmentation with Clustering
📍 Tools: Python (K-Means, PCA)
Use unsupervised learning to group customers based on purchasing behavior.
Skills you build: Clustering, dimensionality reduction, data visualization, customer profiling.
7. Recommendation System
📍 Tools: Python (Surprise / Scikit-learn / Pandas)
Build a recommender system (e.g., movies, products) using collaborative or content-based filtering.
Skills you build: Similarity metrics, matrix factorization, cold start problem, evaluation (RMSE, MAE).
👉 Pick 2–3 projects aligned with your interests.
👉 Document everything on GitHub, and post about your learnings on LinkedIn.
Here you can find the project datasets: https://whatsapp.com/channel/0029VbAbnvPLSmbeFYNdNA29
React ❤️ for more
| 2 | If I need to teach someone data analytics from the basics, here is my strategy:
1. I will first remove the fear of tools from that person
2. i will start with the excel because it looks familiar and easy to use
3. I put more emphasis on projects like at least 5 to 6 with the excel. because in industry you learn by doing things
4. I will release the person from the tutorial hell and move into a more action oriented person
5. Then I move to the sql because every job wants it , even with the ai tools you need strong understanding for it if you are going to use it daily
6. After strong understanding, I will push the person to solve 100 to 150 Sql problems from basic to advance
7. It helps the person to develop the analytical thinking
8. Then I push the person to solve 3 case studies as it helps how we pull the data in the real life
9. Then I move the person to power bi to do again 5 projects by using either sql or excel files
10. Now the fear is removed.
11. Now I push the person to solve unguided challenges and present them by video recording as it increases the problem solving, communication and data story telling skills
12. Further it helps you to clear case study round given by most of the companies
13. Now i help the person how to present them in resume and also how these tools are used in real world.
14. You know the interesting fact, all of above is present free in youtube and I also mentor the people through existing youtube videos.
15. But people stuck in the tutorial hell, loose motivation , stay confused that they are either in the right direction or not.
16. As a personal mentor , I help them to get of the tutorial hell, set them in the right direction and they stay motivated when they start to see the difference before amd after mentorship
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://topmate.io/analyst/861634
Hope this helps you 😊 | 0 |
| 3 | 🔹 DATA SCIENCE – INTERVIEW REVISION SHEET
1️⃣ What is Data Science?
> “Data science is the process of using data, statistics, and machine learning to extract insights and build predictive or decision-making models.”
Difference from Data Analytics:
• Data Analytics → past present (what/why)
• Data Science → future automation (what will happen)
2️⃣ Data Science Lifecycle (Very Important)
1. Business problem understanding
2. Data collection
3. Data cleaning preprocessing
4. Exploratory Data Analysis (EDA)
5. Feature engineering
6. Model building
7. Model evaluation
8. Deployment monitoring
Interview line:
> “I always start from business understanding, not the model.”
3️⃣ Data Types
• Structured → tables, SQL
• Semi-structured → JSON, logs
• Unstructured → text, images
4️⃣ Statistics You MUST Know
• Central tendency: Mean, Median (use when outliers exist)
• Spread: Variance, Standard deviation
• Correlation ≠ causation
• Normal distribution
• Skewness (income → right skewed)
5️⃣ Data Cleaning Preprocessing
Steps you should say in interviews:
1. Handle missing values
2. Remove duplicates
3. Treat outliers
4. Encode categorical variables
5. Scale numerical data
Scaling:
• Min-Max → bounded range
• Standardization → normal distribution
6️⃣ Feature Engineering (Interview Favorite)
> “Feature engineering is creating meaningful input variables that improve model performance.”
Examples:
• Extract month from date
• Create customer lifetime value
• Binning age groups
7️⃣ Machine Learning Basics
• Supervised learning: Regression, Classification
• Unsupervised learning: Clustering, Dimensionality reduction
8️⃣ Common Algorithms (Know WHEN to use)
• Regression: Linear regression → continuous output
• Classification: Logistic regression, Decision tree, Random forest, SVM
• Unsupervised: K-Means → segmentation, PCA → dimensionality reduction
9️⃣ Overfitting vs Underfitting
• Overfitting → model memorizes training data
• Underfitting → model too simple
Fixes:
• Regularization
• More data
• Cross-validation
🔟 Model Evaluation Metrics
• Classification: Accuracy, Precision, Recall, F1 score, ROC-AUC
• Regression: MAE, RMSE
Interview line:
> “Metric selection depends on business problem.”
1️⃣1️⃣ Imbalanced Data Techniques
• Class weighting
• Oversampling / undersampling
• SMOTE
• Metric preference: Precision, Recall, F1, ROC-AUC
1️⃣2️⃣ Python for Data Science
Core libraries:
• NumPy
• Pandas
• Matplotlib / Seaborn
• Scikit-learn
Must know:
• loc vs iloc
• Groupby
• Vectorization
1️⃣3️⃣ Model Deployment (Basic Understanding)
• Batch prediction
• Real-time prediction
• Model monitoring
• Model drift
Interview line:
> “Models must be monitored because data changes over time.”
1️⃣4️⃣ Explain Your Project (Template)
> “The goal was . I cleaned the data using . I performed EDA to identify . I built model and evaluated using . The final outcome was .”
1️⃣5️⃣ HR-Style Data Science Answers
Why data science?
> “I enjoy solving complex problems using data and building models that automate decisions.”
Biggest challenge:
“Handling messy real-world data.”
Strength:
“Strong foundation in statistics and ML.”
🔥 LAST-DAY INTERVIEW TIPS
• Explain intuition, not math
• Don’t jump to algorithms immediately
• Always connect model → business value
• Say assumptions clearly
Double Tap ♥️ For More | 0 |
| 4 | Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science
Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.
1. Basic python and statistics
Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset
2. Advanced Statistics
Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
3. Supervised Learning
a) Regression Problems
How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview
b) Classification problems
Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking
4. Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
5. Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Share with credits: https://t.me/sqlproject
ENJOY LEARNING 👍👍 | 0 |
| 5 | ✅ Useful Platform to Practice SQL Programming 🧠🖥️
Learning SQL is just the first step — practice is what builds real skill. Here are the best platforms for hands-on SQL:
1️⃣ LeetCode – For Interview-Oriented SQL Practice
• Focus: Real interview-style problems
• Levels: Easy to Hard
• Schema + Sample Data Provided
• Great for: Data Analyst, Data Engineer, FAANG roles
✔ Tip: Start with Easy → filter by “Database” tag
✔ Popular Section: Database → Top 50 SQL Questions
Example Problem: “Find duplicate emails in a user table” → Practice filtering, GROUP BY, HAVING
2️⃣ HackerRank – Structured & Beginner-Friendly
• Focus: Step-by-step SQL track
• Has certification tests (SQL Basic, Intermediate)
• Problem sets by topic: SELECT, JOINs, Aggregations, etc.
✔ Tip: Follow the full SQL track
✔ Bonus: Company-specific challenges
Try: “Revising Aggregations – The Count Function” → Build confidence with small wins
3️⃣ Mode Analytics – Real-World SQL in Business Context
• Focus: Business intelligence + SQL
• Uses real-world datasets (e.g., e-commerce, finance)
• Has an in-browser SQL editor with live data
✔ Best for: Practicing dashboard-level queries
✔ Tip: Try the SQL case studies & tutorials
4️⃣ StrataScratch – Interview Questions from Real Companies
• 500+ problems from companies like Uber, Netflix, Google
• Split by company, difficulty, and topic
✔ Best for: Intermediate to advanced level
✔ Tip: Try “Hard” questions after doing 30–50 easy/medium
5️⃣ DataLemur – Short, Practical SQL Problems
• Crisp and to the point
• Good UI, fast learning
• Real interview-style logic
✔ Use when: You want fast, smart SQL drills
📌 How to Practice Effectively:
• Spend 20–30 mins/day
• Focus on JOINs, GROUP BY, HAVING, Subqueries
• Analyze problem → write → debug → re-write
• After solving, explain your logic out loud
🧪 Practice Task:
Try solving 5 SQL questions from LeetCode or HackerRank this week. Start with SELECT, WHERE, and GROUP BY.
💬 Tap ❤️ for more! | 0 |
| 6 | 📢 Advertising in this channel
You can place an ad via Telega․io. It takes just a few minutes.
Formats and current rates: View details | 0 |
| 7 |  🚨 Anthropic dropped a FREE 33-page playbook revealing Claude's very own cheat code:
The 'Skills' folder.
Spend 30 minutes building it,
and you’ll never have to explain your process again.
Top-tier users don't just type commands, they build systems.
Grab your free copy of Anthropic's official guide to building Claude skills right here: https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf | 0 |
| 8 | ✅ GitHub Profile Tips for Data Analysts 🌐💼
Your GitHub is more than code — it’s your digital resume. Here's how to make it stand out:
1️⃣ Clean README (Profile)
• Add your name, title & tools
• Short about section
• Include: skills, top projects, certificates, contact
✅ Example:
“Hi, I’m Rahul – a Data Analyst skilled in SQL, Python & Power BI.”
2️⃣ Pin Your Best Projects
• Show 3–6 strong repos
• Add clear README for each project:
- What it does
- Tools used
- Screenshots or demo links
✅ Bonus: Include real data or visuals
3️⃣ Use Commits & Contributions
• Contribute regularly
• Avoid empty profiles
✅ Daily commits > 1 big push once a month
4️⃣ Upload Resume Projects
• Excel dashboards
• SQL queries
• Python notebooks (Jupyter)
• BI project links (Power BI/Tableau public)
5️⃣ Add Descriptions & Tags
• Use repo tags: sql, python, EDA, dashboard
• Write short project summary in repo description
🧠 Tips:
• Push only clean, working code
• Use folders, not messy files
• Update your profile bio with your LinkedIn
📌 Practice Task:
Upload your latest project → Write a README → Pin it to your profile
💬 Tap ❤️ for more! | 0 |
