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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 75 860 suscriptores, ocupando la posición 2 107 en la categoría Educación y el puesto 4 219 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 860 suscriptores.
Según los últimos datos del 22 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 728, 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 3.00%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.05% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 278 visualizaciones. En el primer día suele acumular 794 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
- 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 23 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.
75 860
Suscriptores
-224 horas
+637 días
+72830 días
Carga de datos en curso...
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| 01 junio | +24 |
Publicaciones del Canal
𝟳 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗘𝗻𝗿𝗼𝗹𝗹 𝗜𝗻 𝟮𝟬𝟮𝟲😍
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| 2 |  🔰 Important Pandas Methods for Data Science | 1 198 |
| 3 |  🔰 Important Pandas Methods for Data Science
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| 4 |  𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 - 𝗙𝘂𝗹𝗹𝘀𝘁𝗮𝗰𝗸𝗗𝗲𝘃 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗪𝗶𝘁𝗵 𝗚𝗲𝗻𝗔𝗜 😍
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| 5 | ✅ Tableau LOD Expressions Level of Detail 📊🔥
👉 LOD Level of Detail Expressions are one of the most powerful and frequently asked Tableau interview topics.
They allow you to perform calculations at a different level of granularity than what is currently shown in the visualization.
🔹 1. What are LOD Expressions?
LOD Expressions let you control how data is aggregated.
👉 Normally, Tableau calculates values based on the current view.
👉 LOD lets you calculate values independently of the visualization.
🔥 2. Why Use LOD Expressions?
✔ Calculate metrics at different levels
✔ Compare individual values to totals
✔ Create advanced KPIs
✔ Improve dashboard flexibility
🔹 3. Types of LOD Expressions ⭐
There are three main types:
✅ FIXED
Calculates values at a specific level.
{ FIXED [Region] : SUM([Sales]) }
👉 Calculates total sales for each region regardless of what's in the view.
✅ INCLUDE
Adds dimensions to the current view.
{ INCLUDE [Customer Name] : SUM([Sales]) }
👉 Includes customer-level calculations.
✅ EXCLUDE
Removes dimensions from the current view.
{ EXCLUDE [Product] : SUM([Sales]) }
👉 Ignores product-level detail.
🔹 4. Example of FIXED LOD
Suppose you want:
👉 Total Sales by Region
Even when viewing sales by product.
{ FIXED [Region] : SUM([Sales]) }
This value remains constant for the region.
🔹 5. Real-World Example
Calculate each customer's contribution to total regional sales:
SUM([Sales]) / { FIXED [Region] : SUM([Sales]) }
🔹 6. Difference Between Aggregate & LOD
Aggregate: Depends on current view, Simple calculations, Dynamic with visualization
LOD: Independent of current view, Advanced calculations, Fixed granularity control
🔹 7. When to Use LOD?
✔ Customer contribution analysis
✔ Regional benchmarking
✔ Advanced KPIs
✔ Performance comparisons
🔹 8. Common Interview Question ⭐
Q: Which LOD expression ignores the dimensions in the current view?
✅ Answer: FIXED
🔹 9. Why LOD is Important?
✔ Advanced Tableau skill
✔ Frequently asked in interviews
✔ Used in enterprise dashboards
✔ Makes complex calculations easier
🎯 Today's Goal
✔ Understand FIXED, INCLUDE, EXCLUDE
✔ Learn granularity concepts
✔ Build advanced Tableau calculations
👉 Double Tap ❤️ For More | 1 256 |
| 6 |  𝗔𝗰𝗰𝗲𝗻𝘁𝘂𝗿𝗲 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲 📊
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| 7 | Essential SQL Topics for Data Analysts 👇
- Basic Queries: SELECT, FROM, WHERE clauses.
- Sorting and Filtering: ORDER BY, GROUP BY, HAVING.
- Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN.
- Aggregation Functions: COUNT, SUM, AVG, MIN, MAX.
- Subqueries: Embedding queries within queries.
- Data Modification: INSERT, UPDATE, DELETE.
- Indexes: Optimizing query performance.
- Normalization: Ensuring efficient database design.
- Views: Creating virtual tables for simplified queries.
- Understanding Database Relationships: One-to-One, One-to-Many, Many-to-Many.
Window functions are also important for data analysts. They allow for advanced data analysis and manipulation within specified subsets of data. Commonly used window functions include:
- ROW_NUMBER(): Assigns a unique number to each row based on a specified order.
- RANK() and DENSE_RANK(): Rank data based on a specified order, handling ties differently.
- LAG() and LEAD(): Access data from preceding or following rows within a partition.
- SUM(), AVG(), MIN(), MAX(): Aggregations over a defined window of rows.
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| 8 |  🚀 𝗧𝗼𝗽 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗟𝗲𝗮𝗿𝗻 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘! 💼🔥
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| 9 | 🧠 7 Resume Tips for Data Science & ML Roles 📄✅
1️⃣ Start with a Strong Summary
⦁ Highlight skills, tools, and domain experience
⦁ Mention years of experience and key achievements
2️⃣ Showcase Projects that Matter
⦁ Focus on real-world impact, not just toy datasets
⦁ Mention metrics (e.g., “Improved accuracy by 12%”)
3️⃣ Tailor for the Role
⦁ Align keywords with the job description
⦁ Use relevant tools and models mentioned in the listing
4️⃣ Highlight Tools & Techniques
⦁ Python, SQL, Pandas, Scikit-learn, TensorFlow
⦁ Also list Git, Docker, AWS if used
5️⃣ Add Business Context
⦁ Mention how your model helped reduce costs, improve conversion, etc.
⦁ Show you understand the why behind the model
6️⃣ Keep It One Page
⦁ Concise and clean layout
⦁ Use bullet points, not long paragraphs
7️⃣ Include Public Work
⦁ GitHub, blog posts, Kaggle profile
⦁ Show you build, write, and share
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| 11 | 🚀 Complete Roadmap to Become a Data Scientist in 5 Months
📅 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 & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.
🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.
🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.
🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.
📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.
🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.
💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.
🎯 Week 25-26: Interview Preparation
💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.
👨💻 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 Data Science Trends.
🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.
🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!
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| 13 | 🔥 Top SQL Interview Questions with Answers
🎯 1️⃣ Find 2nd Highest Salary
📊 Table: employees
id | name | salary
1 | Rahul | 50000
2 | Priya | 70000
3 | Amit | 60000
4 | Neha | 70000
❓ Problem Statement: Find the second highest distinct salary from the employees table.
✅ Solution
SELECT MAX(salary) FROM employees WHERE salary < ( SELECT MAX(salary) FROM employees );
🎯 2️⃣ Find Nth Highest Salary
📊 Table: employees
id | name | salary
1 | A | 100
2 | B | 200
3 | C | 300
4 | D | 200
❓ Problem Statement: Write a query to find the 3rd highest salary.
✅ Solution
SELECT salary FROM ( SELECT salary, DENSE_RANK() OVER(ORDER BY salary DESC) r FROM employees ) t WHERE r = 3;
🎯 3️⃣ Find Duplicate Records
📊 Table: employees
id | name
1 | Rahul
2 | Amit
3 | Rahul
4 | Neha
❓ Problem Statement: Find all duplicate names in the employees table.
✅ Solution
SELECT name, COUNT(*) FROM employees GROUP BY name HAVING COUNT(*) > 1;
🎯 4️⃣ Customers with No Orders
📊 Table: customers
customer_id | name
1 | Rahul
2 | Priya
3 | Amit
📊 Table: orders
order_id | customer_id
101 | 1
102 | 2
❓ Problem Statement: Find customers who have not placed any orders.
✅ Solution
SELECT c.name FROM customers c LEFT JOIN orders o ON c.customer_id = o.customer_id WHERE o.customer_id IS NULL;
🎯 5️⃣ Top 3 Salaries per Department
📊 Table: employees
name | department | salary
A | IT | 100
B | IT | 200
C | IT | 150
D | HR | 120
E | HR | 180
❓ Problem Statement: Find the top 3 highest salaries in each department.
✅ Solution
SELECT * FROM ( SELECT name, department, salary, ROW_NUMBER() OVER( PARTITION BY department ORDER BY salary DESC ) r FROM employees ) t WHERE r <= 3;
🎯 6️⃣ Running Total of Sales
📊 Table: sales
date | sales
2024-01-01 | 100
2024-01-02 | 200
2024-01-03 | 300
❓ Problem Statement: Calculate the running total of sales by date.
✅ Solution
SELECT date, sales, SUM(sales) OVER(ORDER BY date) AS running_total FROM sales;
🎯 7️⃣ Employees Above Average Salary
📊 Table: employees
name | salary
A | 100
B | 200
C | 300
❓ Problem Statement: Find employees earning more than the average salary.
✅ Solution
SELECT name, salary FROM employees WHERE salary > ( SELECT AVG(salary) FROM employees );
🎯 8️⃣ Department with Highest Total Salary
📊 Table: employees
name | department | salary
A | IT | 100
B | IT | 200
C | HR | 500
❓ Problem Statement: Find the department with the highest total salary.
✅ Solution
SELECT department, SUM(salary) AS total_salary FROM employees GROUP BY department ORDER BY total_salary DESC LIMIT 1;
🎯 9️⃣ Customers Who Placed Orders
📊 Tables: Same as Q4
❓ Problem Statement: Find customers who have placed at least one order.
✅ Solution
SELECT name FROM customers c WHERE EXISTS ( SELECT 1 FROM orders o WHERE c.customer_id = o.customer_id );
🎯 🔟 Remove Duplicate Records
📊 Table: employees
id | name
1 | Rahul
2 | Rahul
3 | Amit
❓ Problem Statement: Delete duplicate records but keep one unique record.
✅ Solution
DELETE FROM employees WHERE id NOT IN ( SELECT MIN(id) FROM employees GROUP BY name );
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First explain logic
Then write query
Then optimize
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