¡Ya disponible! Investigación de Telegram 2025 — los principales insights del año 
Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
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Data Analysis Useful Resources #dataanalysis #dataanalysisbooks #sqlbooks #pythonbooks #tableau #powerbi #datavisualization For promotions: @coderfun
Mostrar más📈 Análisis del canal de Telegram Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
El canal Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources (@learndataanalysis) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 51 838 suscriptores, ocupando la posición 3 362 en la categoría Educación y el puesto 7 262 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 51 838 suscriptores.
Según los últimos datos del 14 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 525, y en las últimas 24 horas de 20, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 7.70%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.28% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 991 visualizaciones. En el primer día suele acumular 665 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 8.
- Intereses temáticos: El contenido se centra en temas clave como analyst, |--, excel, visualization, analytic.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Data Analysis Useful Resources
#dataanalysis
#dataanalysisbooks
#sqlbooks
#pythonbooks
#tableau
#powerbi
#datavisualization
For promotions: @coderfun”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 15 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.
51 838
Suscriptores
+2024 horas
+1377 días
+52530 días
Archivo de publicaciones
Choosing the Right Chart Type
Selecting the appropriate chart can make or break your data storytelling. Here's a quick guide to help you choose the perfect visualization:
↳ 𝐁𝐚𝐫 𝐂𝐡𝐚𝐫𝐭𝐬: Perfect for comparing quantities across categories (Think: regional sales comparison)
↳ 𝐋𝐢𝐧𝐞 𝐂𝐡𝐚𝐫𝐭𝐬: Ideal for showing trends and changes over time (Example: monthly website traffic)
↳ 𝐏𝐢𝐞 𝐂𝐡𝐚𝐫𝐭𝐬: Best for showing parts of a whole as percentages (Use case: market share breakdown)
↳ 𝐇𝐢𝐬𝐭𝐨𝐠𝐫𝐚𝐦𝐬: Great for showing the distribution of continuous data (Like salary ranges across your organization)
↳ 𝐒𝐜𝐚𝐭𝐭𝐞𝐫 𝐏𝐥𝐨𝐭𝐬: Essential for exploring relationships between variables (Perfect for marketing spend vs. sales analysis)
↳ 𝐇𝐞𝐚𝐭 𝐌𝐚𝐩𝐬: Excellent for showing data density with color variation (Think: website traffic patterns by hour/day)
↳ 𝐁𝐨𝐱 𝐏𝐥𝐨𝐭𝐬: Invaluable for displaying data variability and outliers (Great for analyzing performance metrics)
↳ 𝐀𝐫𝐞𝐚 𝐂𝐡𝐚𝐫𝐭𝐬: Shows cumulative totals over time (Example: sales growth across product lines)
↳ 𝐁𝐮𝐛𝐛𝐥𝐞 𝐂𝐡𝐚𝐫𝐭𝐬: Powerful for displaying three dimensions of data (Combines size, position, and grouping)
𝐏𝐫𝐨 𝐓𝐢𝐩: Always consider your audience and the story you want to tell when choosing your visualization type.
I have curated the best interview resources to crack Power BI Interviews 👇👇
https://t.me/PowerBI_analyst
Hope you'll like it
Like this post if you need more resources like this 👍❤️
Repost from Python Projects & Resources
𝗣𝗼𝘄𝗲𝗿𝗕𝗜 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁😍
✅ Beginner-friendly
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✅ And yes… a badge for that resume flex
Perfect for beginners, job seekers, & Working Professionals
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Data types are foundational in computing, and it's essential to understand them to work effectively in any programming environment.
Let's take a dive into the top ten commonly used data types:
1. Integer (int):
- Represents whole numbers.
- Examples: -2, -1, 0, 1, 2, 3
2. Floating Point (float/double):
- Represents numbers with decimals.
- Examples: -2.5, 0.0, 3.14
3. Character (char):
- Represents single characters.
- Examples: 'A', 'b', '1', '%'
4. String:
- Represents sequences of characters, basically text.
- Examples: "Hello", "ChatGPT", "1234"
5. Boolean (bool):
- Represents true or false values.
- Examples: True, False
6. Array:
- Represents a collection of elements, often of the same type.
- Examples: [1, 2, 3], ["apple", "banana", "cherry"]
7. Object:
- Used in object-oriented programming, represents a combination of data and methods to manipulate the data.
- Examples: A Car object might have data like color and speed and methods like drive() and park().
8. Date & Time:
- Represents date and time values.
- Examples: 23-10-2023, 12:30:45
9. Byte & Binary:
- Represents raw binary data.
- Examples: 01010101 (Byte), 101000111011 (Binary)
10. Enum:
- Represents a set of named constants.
- Examples: Days of the week (Monday, Tuesday...), Colors (Red, Blue, Green)
Repost from SQL Programming Resources
𝗧𝗼𝗽 𝟰 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 😍
These FREE resources are all you need to go from beginner to confident analyst! 💻📊
✅ Hands-on projects
✅ Beginner to advanced lessons
✅ Resume-worthy skills
𝗟𝗶𝗻𝗸:-👇
https://pdlink.in/4jkQaW1
Learn today, level up tomorrow. Let’s go!✅
Steps to become data analyst when you are fresher 👇👇
1 - First try to focus 3 mandatory skills i.e. Sql, Ms excel and python -
- For sql you can refer Ankit Bansal Or Thoufiq Mohammed (techtfq) on @sqlanalyst
- For Ms excel refer Leila Gharani or @excel_analyst
- For python refer freecodecamp from YouTube or @pythonanalyst
2 - After that try to be clear with basic idea of tableau or powerbi. (Not mandatory for every job). You can refer this channel for free resources https://t.me/PowerBI_analyst
3 - Add your college project in your resume, if it's a data science related project it will help a lot. If you don't have project then you can make some dashboarding projects from YouTube in tableau/powerbi.
4 - And start applying for jobs which is having 0-1 yr experience required, you can also apply for 1 yr experience required job in analytics because sometimes they may consider fresher also. You can refer this channel @jobs_sql for job opportunities
Repost from Artificial Intelligence
𝗟𝗲𝗮𝗿𝗻 𝗡𝗲𝘄 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗘𝗮𝗿𝗻 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀!😍
Looking to upgrade your skills in Data Science, Programming, AI, Business, and more? 📚💡
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TOP CONCEPTS FOR INTERVIEW PREPARATION!!
🚀TOP 10 SQL Concepts for Job Interview
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
🚀TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
🚀TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
Like ❤️ the post if it was helpful to you!!!
Data Analysis vs Data Science
Data analysis often focuses on interpreting and summarizing existing data, requiring skills like statistical analysis, SQL, and data visualization.
On the other hand, data science involves a broader set of skills, including machine learning, predictive modeling, and advanced programming.
In essence, data analysis is a subset of data science, with data scientists often having a more extensive toolkit for handling complex and unstructured data.
Free Resources to become data analyst -> https://www.linkedin.com/posts/sql-analysts_freecertificates-dataanalysts-python-activity-7113004712412524545-Uw4k
Steps to become data scientist -> https://t.me/learndataanalysis/559
Repost from SQL Programming Resources
𝗪𝗲𝗯 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
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𝐋𝐢𝐧𝐤 👇:-
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Checklist to become a Data Analyst
𝟱 𝗙𝗥𝗘𝗘 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
Explore AI, machine learning, and cloud computing — straight from Google and FREE
1. 🌐Google AI for Anyone
2. 💻Google AI for JavaScript Developers
3. ☁️ Cloud Computing Fundamentals (Google Cloud)
4. 🔍 Data, ML & AI in Google Cloud
5. 📊 Smart Analytics, ML & AI on Google Cloud
𝐋𝐢𝐧𝐤 👇:-
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Want to build your first AI agent?
Join a live hands-on session by GeeksforGeeks & Salesforce for working professionals
- Build with Agent Builder
- Assign real actions
- Get a free certificate of participation
Registeration link:👇
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9 secrets about Data Storytelling every analyst should know (number 6 is a must):
1/ Start with the end in mind—what’s the key takeaway?
2/ Don’t just present numbers—explain the 'so what' behind them.
3/ Data should drive decisions—frame your analysis as a solution to a problem.
#DataAnalytics
4/ Visualise trends over time to tell a story.
5/ Add context to your data—it makes your insights relevant.
6/ Speak the language of your audience—simplify complex terms.
7/ Use metaphors or analogies to explain difficult concepts. Don't use professional jargon.
8/ Include both the big picture and the details—it appeals to different stakeholders.
9/ Conclude with a call to action—what should they do next?
𝗙𝗥𝗘𝗘 𝗪𝗲𝗯𝘀𝗶𝘁𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗖𝗼𝗱𝗶𝗻𝗴 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 😍
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📖 Starter Kit 2025 - Data Analyst
Want to make a transition to a career in data?
Here is a 7-step plan for each data role
Data Scientist
Statistics and Math: Advanced statistics, linear algebra, calculus.
Machine Learning: Supervised and unsupervised learning algorithms.
xData Wrangling: Cleaning and transforming datasets.
Big Data: Hadoop, Spark, SQL/NoSQL databases.
Data Visualization: Matplotlib, Seaborn, D3.js.
Domain Knowledge: Industry-specific data science applications.
Data Analyst
Data Visualization: Tableau, Power BI, Excel for visualizations.
SQL: Querying and managing databases.
Statistics: Basic statistical analysis and probability.
Excel: Data manipulation and analysis.
Python/R: Programming for data analysis.
Data Cleaning: Techniques for data preprocessing.
Business Acumen: Understanding business context for insights.
Data Engineer
SQL/NoSQL Databases: MySQL, PostgreSQL, MongoDB, Cassandra.
ETL Tools: Apache NiFi, Talend, Informatica.
Big Data: Hadoop, Spark, Kafka.
Programming: Python, Java, Scala.
Data Warehousing: Redshift, BigQuery, Snowflake.
Cloud Platforms: AWS, GCP, Azure.
Data Modeling: Designing and implementing data models.
#data
𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 😍
Mercedes :- https://pdlink.in/3RPLXNM
TechM :- https://pdlink.in/4cws0oN
SE :- https://pdlink.in/42feu5D
Siemens :- https://pdlink.in/4jxhzDR
Dxc :- https://pdlink.in/4ctIeis
EY:- https://pdlink.in/4lwMQZo
Apply before the link expires 💫
Hi Guys,
Here are some of the telegram channels which may help you in data analytics journey 👇👇
SQL: https://t.me/sqlanalyst
Power BI & Tableau: https://t.me/PowerBI_analyst
Excel: https://t.me/excel_analyst
Python: https://t.me/dsabooks
Jobs: https://t.me/jobs_SQL
Data Science: https://t.me/datasciencefree
Artificial intelligence: https://t.me/machinelearning_deeplearning
Data Engineering: https://t.me/sql_engineer
Data Analysts: https://t.me/sqlspecialist
Hope it helps :)
𝗝𝗣 𝗠𝗼𝗿𝗴𝗮𝗻 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍
JPMorgan offers free virtual internships to help you develop industry-specific tech, finance, and research skills.
- Software Engineering Internship
- Investment Banking Program
- Quantitative Research Internship
𝐋𝐢𝐧𝐤 👇:-
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Enroll For FREE & Get Certified 🎓
