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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
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 75 816 suscriptores, ocupando la posición 2 113 en la categoría Educación y el puesto 4 286 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 816 suscriptores.
Según los últimos datos del 18 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 884, y en las últimas 24 horas de 6, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.25%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.38% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 462 visualizaciones. En el primer día suele acumular 1 043 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 19 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.
75 816
Suscriptores
+624 horas
+1657 días
+88430 días
Archivo de publicaciones
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Essential statistics topics for data science
1. Descriptive statistics: Measures of central tendency, measures of dispersion, and graphical representations of data.
2. Inferential statistics: Hypothesis testing, confidence intervals, and regression analysis.
3. Probability theory: Concepts of probability, random variables, and probability distributions.
4. Sampling techniques: Simple random sampling, stratified sampling, and cluster sampling.
5. Statistical modeling: Linear regression, logistic regression, and time series analysis.
6. Machine learning algorithms: Supervised learning, unsupervised learning, and reinforcement learning.
7. Bayesian statistics: Bayesian inference, Bayesian networks, and Markov chain Monte Carlo methods.
8. Data visualization: Techniques for visualizing data and communicating insights effectively.
9. Experimental design: Designing experiments, analyzing experimental data, and interpreting results.
10. Big data analytics: Handling large volumes of data using tools like Hadoop, Spark, and SQL.
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Learning data science in 2024 will likely involve a combination of traditional educational methods and newer, more innovative approaches.
Here are some steps you can take to learn data science in 2024:
1. Enroll in a data science program: Consider enrolling in a data science program at a university or online platform. Look for programs that cover topics such as machine learning, statistical analysis, and data visualization. I will recommend the subscription by 365datascience which update content as per latest requirements.
2. Take online courses: There are many online platforms that offer data science courses, such as Udacity, Udemy, and DataCamp. These courses can help you learn specific skills and techniques in data science.
3. Participate in data science competitions: Participating in data science competitions, such as those hosted on Kaggle, can help you apply your skills to real-world problems and learn from other data scientists.
4. Join data science communities: Joining data science communities, such as forums, meetups, or social media groups, can help you connect with other data scientists and learn from their experiences.
5. Stay updated on industry trends: Data science is a rapidly evolving field, so it's important to stay updated on the latest trends and technologies. Follow blogs, podcasts, and industry publications to keep up with the latest developments in data science.
6. Build a portfolio: As you learn data science skills, be sure to build a portfolio of projects that showcase your abilities. This can help you demonstrate your skills to potential employers or clients.
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Thanks for the amazing response. I added few more essential data science resources in "Projects" Folder today.
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Planning for Data engineering Interview.
Focus on SQL & Python first. Here are some important questions which you should know.
𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬
1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.
𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬
1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.
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Machine learning is a subset of artificial intelligence that involves developing algorithms and models that enable computers to learn from and make predictions or decisions based on data. In machine learning, computers are trained on large datasets to identify patterns, relationships, and trends without being explicitly programmed to do so.
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data, where the correct output is provided along with the input data. Unsupervised learning involves training the algorithm on unlabeled data, allowing it to identify patterns and relationships on its own. Reinforcement learning involves training an algorithm to make decisions by rewarding or punishing it based on its actions.
Machine learning algorithms can be used for a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, predictive analytics, and more. These algorithms can be trained using various techniques such as neural networks, decision trees, support vector machines, and clustering algorithms.
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