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Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence
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Сітка:📈 Аналітичний огляд Telegram-каналу Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence
Канал Data Science Portfolio - Kaggle Datasets & AI Projects | Artificial Intelligence (@dataportfolio) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 37 697 підписників, посідаючи 3 558 місце в категорії Технології та додатки та 10 484 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 37 697 підписників.
За останніми даними від 06 липня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -44, а за останні 24 години на 13, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.51%. Протягом перших 24 годин після публікації контент зазвичай збирає 0.97% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 1 325 переглядів. Протягом першої доби публікація в середньому набирає 367 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 7.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як learning, dataset, sql, link:-, analyst.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Free Datasets For Data Science Projects & Portfolio
Buy ads: https://telega.io/c/DataPortfolio
For Promotions/ads: @coderfun @love_data”
Завдяки високій частоті оновлень (останні дані отримано 07 липня, 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 Технології та додатки.
37 697
Підписники
+1324 години
+377 днів
-4430 день
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| Дата | Залучення підписників | Згадування | Канали | |
| 07 липня | 0 | |||
| 06 липня | +13 | |||
| 05 липня | 0 | |||
| 04 липня | +8 | |||
| 03 липня | +2 | |||
| 02 липня | +13 | |||
| 01 липня | +6 |
Дописи каналу
🎯 2026 Free Course 👇
1/ Python for Beginners (Microsoft):
https://learn.microsoft.com/en-us/shows/intro-to-python-development/
2/ SQL for Data Science (IBM via edX):
https://www.edx.org/learn/data-science/ibm-sql-for-data-science
3/ Java Programming (Infosys Springboard)
https://placementdriveinsta.in/infosys-free-java-course-with-certificate/
4/ AI Fundamentals - AI-900 (Microsoft)
https://learn.microsoft.com/en-us/training/paths/introduction-to-ai-on-azure/
5/ Fundamentals of Machine Learning (NVIDIA DLI)
https://www.coursera.org/learn/fundamentals-of-machine-learning
6/ AWS Cloud Practitioner Essentials (AWS)
https://explore.skillbuilder.aws/learn/course/external/view/elearning/134/aws-cloud-practitioner-essentials
7/ Get Started with Microsoft Data Analytics
https://learn.microsoft.com/en-us/training/modules/get-started-data-analytics/
8/ Data Science Foundations (IBM SkillsBuild)
https://skillsbuild.org/students/course-catalog/data-science
| 2 | ✅ Python for Data Science – Part 1: NumPy Interview Q&A 📊
🔹 1. What is NumPy and why is it important?
NumPy (Numerical Python) is a powerful Python library for numerical computing. It supports fast array operations, broadcasting, linear algebra, and random number generation. It’s the backbone of many data science libraries like Pandas and Scikit-learn.
🔹 2. Difference between Python list and NumPy array
Python lists can store mixed data types and are slower for numerical operations. NumPy arrays are faster, use less memory, and support vectorized operations, making them ideal for numerical tasks.
🔹 3. How to create a NumPy array
import numpy as np
arr = np.array([1, 2, 3])
🔹 4. What is broadcasting in NumPy?
Broadcasting lets you perform operations on arrays of different shapes. For example, adding a scalar to an array applies the operation to each element.
🔹 5. How to generate random numbers
Use np.random.rand() for uniform distribution, np.random.randn() for normal distribution, and np.random.randint() for random integers.
🔹 6. How to reshape an array
Use .reshape() to change the shape of an array without changing its data.
Example: arr.reshape(2, 3) turns a 1D array of 6 elements into a 2x3 matrix.
🔹 7. Basic statistical operations
Use functions like mean(), std(), var(), sum(), min(), and max() to get quick stats from your data.
🔹 8. Difference between zeros(), ones(), and empty()
np.zeros() creates an array filled with 0s, np.ones() with 1s, and np.empty() creates an array without initializing values (faster but unpredictable).
🔹 9. Handling missing values
Use np.nan to represent missing values and np.isnan() to detect them.
Example:
arr = np.array([1, 2, np.nan])
np.isnan(arr) # Output: [False False True]
🔹 10. Element-wise operations
NumPy supports element-wise addition, subtraction, multiplication, and division.
Example:
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
a + b # Output: [5 7 9]
💡 Pro Tip: NumPy is all about speed and efficiency. Mastering it gives you a huge edge in data manipulation and model building.
Double Tap ❤️ For More | 1 325 |
| 3 | 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 | 0 |
| 4 | 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 |
| 5 | 🔹 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 |
| 6 | 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 |
