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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
Mostrar más📈 Análisis del canal de Telegram Data Science & Machine Learning
El canal Data Science & Machine Learning (@datasciencefun) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 75 810 suscriptores, ocupando la posición 2 118 en la categoría Educación y el puesto 4 300 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 810 suscriptores.
Según los últimos datos del 17 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 903, y en las últimas 24 horas de 2, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.39%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.40% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 573 visualizaciones. En el primer día suele acumular 1 064 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 4.
- Intereses temáticos: El contenido se centra en temas clave como learning, accuracy, distribution, panda, dataset.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 18 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
75 810
Suscriptores
+224 horas
+1887 días
+90330 días
Archivo de publicaciones
5 Python functions for statistical analysis:
🔹 mean(): Calculates the average of your data. Perfect for understanding central tendencies.
🔹 median(): Finds the middle value in your data. Useful when your data has outliers.
🔹 mode(): Identifies the most frequent value. Key for categorical data analysis.
🔹 std(): Computes the standard deviation. Crucial for measuring data dispersion.
🔹 var(): Calculates the variance. Helps in understanding data variability. DataAnalytics
Guesstimate questions are scary, simply because they really matter for impacting your performance in those all-important interviews — often for consulting, data analytics or product management. No need to worry; you can do it! In this guide, we are looking at how to approach guesstimate questions with confidence and make what sounds like a guessing game into an opportunity for showcasing our analytical thinking
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Data Analyst vs. Data Scientist 👇👇
https://t.me/sqlspecialist/775
How much Statistics must I know to become a Data Scientist?
This is one of the most common questions
Here are the must-know Statistics concepts every Data Scientist should know:
𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆
↗ Bayes' Theorem & conditional probability
↗ Permutations & combinations
↗ Card & die roll problem-solving
𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 & 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀
↗ Mean, median, mode
↗ Standard deviation and variance
↗ Bernoulli's, Binomial, Normal, Uniform, Exponential distributions
𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀
↗ A/B experimentation
↗ T-test, Z-test, Chi-squared tests
↗ Type 1 & 2 errors
↗ Sampling techniques & biases
↗ Confidence intervals & p-values
↗ Central Limit Theorem
↗ Causal inference techniques
𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴
↗ Logistic & Linear regression
↗ Decision trees & random forests
↗ Clustering models
↗ Feature engineering
↗ Feature selection methods
↗ Model testing & validation
↗ Time series analysis
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Common Python errors and what they mean:
🔹 SyntaxError: Incorrectly written code structure. Check for typos or missing punctuation (like missing '';,).
🔹 IndentationError: Inconsistent use of spaces and tabs. Keep your indentation consistent.
🔹 TypeError: Performing an operation on incompatible types. Like adding a string and an integer ⤵️
🔹 NameError: Using a variable or function that hasn't been defined. Like print(undeclared_variable)
🔹 ValueError: Function receives the correct type but an inappropriate value. When you are trying to convert str to ing, like int("abc")
7. 🔴 𝗗𝗜𝗦𝗔𝗗𝗩𝗔𝗡𝗧𝗔𝗚𝗘𝗦 🔴
• Sensitive to the choice of kernel function
• Sensitive to the choice of regularization parameter, which determines the trade-off between finding a good boundary and avoiding overfitting.
6. 🟢 𝗔𝗗𝗩𝗔𝗡𝗧𝗔𝗚𝗘𝗦 🟢
• useful when the data is not linearly separable
• very effective in high-dimensional data and can handle a large number of features with relatively small datasets
5. To transform the data to a higher-dimensional space, SVMs use what is called 𝗸𝗲𝗿𝗻𝗲𝗹 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀.
There are two main types:
1️⃣ Polynomial kernels
2️⃣ Radial kernels
4. But let’s go back to finding the boundaries...
To overcome linear limitations, SVMs take the data and project it into a higher-dimensional space, where finding the boundary becomes much easier.
This boundary is called the maximum margin hyperplane.
3. For data with non-linear relationships, finding a boundary is impossible. This boundary is called 𝘀𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗻𝗴 𝗵𝘆𝗽𝗲𝗿𝗽𝗹𝗮𝗻𝗲.
The points closest to this boundary, named 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝘃𝗲𝗰𝘁𝗼𝗿𝘀, play a key role in shaping the SVM’s decision-making process.
2. Its goal is to find a boundary that maximally separates the data into different classes (classification) or fits the data with a line/plane (regression).
They excel at handling intricate datasets where finding the right boundary seems challenging.
Support Vector Machines clearly explained👇
1. Support Vector Machine is a useful Machine Learning algorithm frequently used for both classification and regression problems.
⭐ this is a 𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺.
Basically, they need labels or targets to learn!
6 essential Python functions for file handling:
🔹 open(): Opens a file and returns a file object. Essential for reading and writing files
🔹 read(): Reads the contents of a file
🔹 write(): Writes data to a file. Great for saving output
🔹 close(): Closes the file
🔹 with open(): Context manager for file operations. Ensures proper file handling
🔹 pd.read_excel(): Reads Excel files into a pandas DataFrame. Crucial for working with Excel data
Advanced AI and Data Science Interview Questions
1. Explain the concept of Generative Adversarial Networks (GANs). How do they work, and what are some of their applications?
2. What is the Curse of Dimensionality? How does it affect machine learning models, and what techniques can be used to mitigate its impact?
3. Describe the process of hyperparameter tuning in deep learning. What are some strategies you can use to optimize hyperparameters?
4. How does a Transformer architecture differ from traditional RNNs and LSTMs? Why has it become so popular in natural language processing (NLP)?
5. What is the difference between L1 and L2 regularization, and in what scenarios would you prefer one over the other?
6. Explain the concept of transfer learning. How can pre-trained models be used in a new but related task?
7. Discuss the importance of explainability in AI models. How do methods like LIME or SHAP contribute to model interpretability?
8. What are the differences between Reinforcement Learning (RL) and Supervised Learning? Can you provide an example where RL would be more appropriate?
9. How do you handle imbalanced datasets in a classification problem? Discuss techniques like SMOTE, ADASYN, or cost-sensitive learning.
10. What is Bayesian Optimization, and how does it compare to grid search or random search for hyperparameter tuning?
11. Describe the steps involved in developing a recommendation system. What algorithms might you use, and how would you evaluate its performance?
12. Can you explain the concept of autoencoders? How are they used for tasks such as dimensionality reduction or anomaly detection?
13. What are adversarial examples in the context of machine learning models? How can they be used to fool models, and what can be done to defend against them?
14. Discuss the role of attention mechanisms in neural networks. How have they improved performance in tasks like machine translation?
15. What is a variational autoencoder (VAE)? How does it differ from a standard autoencoder, and what are its benefits in generating new data?
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