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Data Analytics
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Perfect channel to learn Data Analytics Learn SQL, Python, Alteryx, Tableau, Power BI and many more For Promotions: @coderfun @love_data
Показати більше📈 Аналітичний огляд Telegram-каналу Data Analytics
Канал Data Analytics (@sqlspecialist) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 109 605 підписників, посідаючи 1 124 місце в категорії Технології та додатки та 2 373 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 109 605 підписників.
За останніми даними від 19 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 624, а за останні 24 години на -15, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 3.26%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.27% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 3 575 переглядів. Протягом першої доби публікація в середньому набирає 1 388 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 9.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як row, sql, analytic, analyst, visualization.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Perfect channel to learn Data Analytics
Learn SQL, Python, Alteryx, Tableau, Power BI and many more
For Promotions: @coderfun @love_data”
Завдяки високій частоті оновлень (останні дані отримано 20 червня, 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.
109 605
Підписники
-1524 години
+1257 днів
+62430 день
Архів дописів
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Don't aim for this:
Excel - 100%
SQL - 0%
PowerBI/Tableau - 0%
Python/R - 0%
Aim for this:
Excel - 25%
SQL - 25%
PowerBI/Tableau - 25%
Python/R - 25%
You don't need to know everything straight away.
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Essential Python and SQL topics for data analysts 😄👇
Python Topics:
Python Resources - @pythonanalyst
1. Data Structures
- Lists, Tuples, and Dictionaries
- NumPy Arrays for numerical data
2. Data Manipulation
- Pandas DataFrames for structured data
- Data Cleaning and Preprocessing techniques
- Data Transformation and Reshaping
3. Data Visualization
- Matplotlib for basic plotting
- Seaborn for statistical visualizations
- Plotly for interactive charts
4. Statistical Analysis
- Descriptive Statistics
- Hypothesis Testing
- Regression Analysis
5. Machine Learning
- Scikit-Learn for machine learning models
- Model Building, Training, and Evaluation
- Feature Engineering and Selection
6. Time Series Analysis
- Handling Time Series Data
- Time Series Forecasting
- Anomaly Detection
7. Python Fundamentals
- Control Flow (if statements, loops)
- Functions and Modular Code
- Exception Handling
- File
SQL Topics:
SQL Resources - @sqlanalyst
1. SQL Basics
- SQL Syntax
- SELECT Queries
- Filters
2. Data Retrieval
- Aggregation Functions (SUM, AVG, COUNT)
- GROUP BY
3. Data Filtering
- WHERE Clause
- ORDER BY
4. Data Joins
- JOIN Operations
- Subqueries
5. Advanced SQL
- Window Functions
- Indexing
- Performance Optimization
6. Database Management
- Connecting to Databases
- SQLAlchemy
7. Database Design
- Data Types
- Normalization
Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!
Share with credits: https://t.me/sqlspecialist
Hope it helps :)
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✅ Complete Data Analyst Interview Roadmap – What You MUST Know 📊💼
Whether you're aiming for a junior role or a senior position, here's a comprehensive guide to ace your data analyst interviews in 2025:
🔰 1. Data Analysis Fundamentals:
• Statistical Concepts: Mean, median, mode, standard deviation, variance, distributions (normal, binomial), hypothesis testing.
• Experimental Design: A/B testing, control groups, statistical significance.
• Data Visualization Principles: Choosing the right chart type, effective dashboard design, data storytelling.
📚 2. Technical Skills Mastery:
• SQL:
• SELECT, FROM, WHERE clauses
• JOINs (INNER, LEFT, RIGHT, FULL OUTER)
• Aggregate functions (COUNT, SUM, AVG, MIN, MAX)
• GROUP BY and HAVING
• Window functions (RANK, ROW_NUMBER)
• Subqueries
• Excel:
• Pivot tables
• VLOOKUP, INDEX/MATCH
• Conditional formatting
• Data validation
• Charts and graphs
• Data Visualization Tools (choose at least one):
• Tableau
• Power BI
• Programming (Python or R - optional but highly valued):
• Data manipulation with Pandas (Python) or dplyr (R)
• Data visualization with Matplotlib, Seaborn (Python) or ggplot2 (R)
⚙️ 3. Data Wrangling and Cleaning:
• Handling Missing Data: Imputation techniques
• Data Transformation: Normalization, scaling
• Outlier Detection and Treatment
• Data Type Conversion
• Data Validation Techniques
💬 4. Problem-Solving Practice:
• Case Studies: Practice solving real-world business problems using data.
• Examples: Customer churn analysis, sales trend forecasting, marketing campaign optimization.
• Estimation Questions: Practice making reasonable estimates when data is limited.
💡 5. Business Acumen:
• Understand key business metrics (e.g., revenue, profit, customer lifetime value).
• Be able to connect data insights to business outcomes.
• Demonstrate an understanding of the industry you're interviewing for.
🧠 6. Communication Skills:
• Be able to clearly and concisely explain your findings to both technical and non-technical audiences.
• Practice presenting data in a visually compelling way.
• Be prepared to answer behavioral questions about your teamwork and problem-solving abilities.
📝 7. Resume and Portfolio:
• Highlight relevant skills and experience.
• Showcase your projects with clear descriptions and quantifiable results.
• Include links to your GitHub, Tableau Public profile, or personal website.
🔄 8. Mock Interviews and Feedback:
• Practice with friends, mentors, or online platforms.
• Focus on both technical proficiency and communication skills.
• Seek feedback on your approach and presentation.
🎯 Tips:
• Focus on demonstrating your ability to solve real-world business problems with data.
• Be prepared to explain your thought process and justify your choices.
• Show enthusiasm for data and a desire to learn.
👍 Tap ❤️ if you found this helpful! #dataanalyst #interviews #dataanalysis #analytics #sql #excel #career
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𝟴 𝗦𝗸𝗶𝗹𝗹𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗕𝗲𝗳𝗼𝗿𝗲 𝗘𝗻𝘁𝗲𝗿𝗶𝗻𝗴 𝗜𝗻𝘁𝗼 𝟮𝟬𝟮𝟲😍
- Python Programming
- Data Analytics
- ChatGPT
- Data Visualization With Power BI
- Generative AI
- Data Science
- Tableau
- Java & SQL
𝗦𝘁𝗮𝗿𝘁 𝗡𝗼𝘄👇:-
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✅ How to Apply for Data Analyst Jobs (Step-by-Step Guide) 📈💼
🔹 1. Build a Data-Focused Portfolio
- Create 3–5 strong projects using real datasets
(Sales dashboard, customer segmentation, churn analysis, etc.)
- Use tools like Excel, SQL, Power BI/Tableau, Python (Pandas/Matplotlib)
- Host projects on GitHub or publish dashboards publicly
🔹 2. Make a Sharp Resume
- Highlight key skills: SQL, Excel, Power BI/Tableau, Python, Statistics
- Emphasize impact:
"Built a dashboard that reduced report time by 40%"
- Add portfolio + GitHub + LinkedIn links
🔹 3. Build a Strong LinkedIn Profile
- Headline: "Aspiring Data Analyst | SQL | Excel | Tableau"
- Share insights from your projects, learning journey, or data visualizations
- Connect with analysts, hiring managers & recruiters
🔹 4. Apply on the Right Platforms
- General: LinkedIn, Indeed, Naukri
- Fresher Friendly: Internshala, Hirect, AICTE
- Tech-Specific: Analytics Vidhya Jobs, Kaggle Jobs, iMocha
- Freelance (for experience): Upwork, Fiverr
🔹 5. Apply Strategically
- Target entry-level/analyst/intern roles
- Personalize your applications with cover letters or project links
- Keep a spreadsheet to track applications
🔹 6. Prepare for Interviews
- Master:
- SQL queries & joins
- Excel formulas & dashboards
- Data visualization principles
- Basic statistics & business metrics
- Practice with mock interviews and case studies
💡 Bonus:
- Take part in Makeover Monday (Tableau challenge)
- Publish on Medium or LinkedIn to showcase your insights!
🧠 Tip: Data Analyst ≠ Just tools — always show business impact in your projects!
👍 Double Tap ❤️ For More #dataanalyst #jobs #hiring #datascience #data #analytics #career
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𝗙𝗥𝗘𝗘 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗜𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (Hyd/Pune/Noida)😍
Learn from the Top 1% of the data analytics industry
Master Excel, SQL, Python, Power BI & Data Visualization
Secure High-Paying Jobs with weekly hiring drives in just 5 Months.
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Hurry Up 🏃♂️! Limited seats are available.
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Data analyst starter kit:
- Become an expert at SQL and data wrangling.
- Learn to help others understand data through visualisations.
- Seek to answer specific questions and provide clarity.
- Remember, everything ends up in Excel.
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Step-by-step Guide to Create a Data Analyst Portfolio:
✅ 1️⃣ Choose Your Tools & Skills
Decide what tools you want to showcase:
• Excel, SQL, Python (Pandas, NumPy)
• Data visualization (Tableau, Power BI, Matplotlib, Seaborn)
• Basic statistics and data cleaning
✅ 2️⃣ Plan Your Portfolio Structure
Your portfolio should include:
• Home Page – Brief intro about you
• About Me – Skills, tools, background
• Projects – Showcased with explanations and code
• Contact – Email, LinkedIn, GitHub
• Optional: Blog or case studies
✅ 3️⃣ Build Your Portfolio Website or Use Platforms
Options:
• Build your own website with HTML/CSS or React
• Use GitHub Pages, Tableau Public, or LinkedIn articles
• Make sure it’s easy to navigate and mobile-friendly
✅ 4️⃣ Add 3–5 Detailed Projects
Projects should cover:
• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Data visualization dashboards or reports
• SQL queries or Python scripts for analysis
Each project should include:
• Problem statement
• Dataset source
• Tools & techniques used
• Key findings & visualizations
• Link to code (GitHub) or live dashboard
✅ 5️⃣ Publish & Share Your Portfolio
Host your portfolio on:
• GitHub Pages
• Tableau Public
• Personal website or blog
✅ 6️⃣ Keep It Updated
• Add new projects regularly
• Improve old ones based on feedback
• Share insights on LinkedIn or data blogs
💡 Pro Tips
• Focus on storytelling with data — explain what the numbers mean
• Use clear visuals and dashboards
• Highlight business impact or insights from your work
• Include a downloadable resume and links to your profiles
🎯 Goal: Anyone visiting your portfolio should quickly understand your data skills, see your problem-solving ability, and know how to reach you.
👍 Tap ❤️ if you found this helpful!
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𝟲 𝗙𝗥𝗘𝗘 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗜𝗻 𝟮𝟬𝟮𝟱 😍
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4lp7hXQ
𝗔𝗜 & 𝗠𝗟 :- https://pdlink.in/3U3eZuq
𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴:- https://pdlink.in/3GtNJlO
𝗖𝘆𝗯𝗲𝗿 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 :- https://pdlink.in/4nHBuTh
𝗢𝘁𝗵𝗲𝗿 𝗧𝗲𝗰𝗵 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 :- https://pdlink.in/3ImMFAB
𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 & 𝗔𝗪𝗦 :- https://pdlink.in/4m3FwTX
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✅ Data Analyst Resume Checklist (2025) 📊📝
1️⃣ Professional Summary
• 2-3 lines about your experience, skills, and career goals.
✔️ Example: "Data Analyst with 3+ years of experience in data mining, analysis, and visualization using Python, SQL, and Tableau."
2️⃣ Technical Skills
• Programming Languages: Python, R, SQL
• Data Visualization Tools: Tableau, Power BI, Matplotlib, Seaborn
• Statistical Analysis: Hypothesis Testing, Regression, Time Series Analysis
• Databases: SQL, NoSQL
• Cloud Technologies: AWS, Azure, GCP (if applicable)
• Other Tools: Excel, Jupyter Notebook, Git
3️⃣ Projects Section
• 2-4 data analysis projects with:
- Project name and brief description
- Tools/technologies used
- Key findings and insights
- Link to GitHub or live dashboard (if applicable)
✔️ Use bullet points and quantify achievements.
4️⃣ Work Experience (if any)
• Company name, role, and duration
• Responsibilities and achievements with metrics
✔️ Example: "Increased sales leads by 15% by identifying key customer segments using clustering techniques."
5️⃣ Education
• Degree, University/Institute, Graduation Year
✔️ Include relevant coursework or specializations (e.g., statistics, data science).
✔️ Add certifications (if any): Google Data Analytics Professional Certificate, etc.
6️⃣ Soft Skills
• Communication, problem-solving, critical thinking, teamwork, attention to detail
7️⃣ Clean & Professional Formatting
• Use a clear and easy-to-read font
• Keep it to one page if possible
• Save as a PDF
💡 Pro Tip: Tailor your resume to the specific requirements of the job. Highlight the skills and experiences that are most relevant to the position.
👍 Tap ❤️ if you found this helpful!
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Greetings from PVR Cloud Tech!! 🌈
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📊 Complete SQL Syllabus Roadmap (Beginner to Expert) 🗄️
🔰 Beginner Level:
1. Intro to Databases: What are databases, Relational vs. Non-Relational
2. SQL Basics: SELECT, FROM, WHERE
3. Data Types: INT, VARCHAR, DATE, BOOLEAN, etc.
4. Operators: Comparison, Logical (AND, OR, NOT)
5. Sorting & Filtering: ORDER BY, LIMIT, DISTINCT
6. Aggregate Functions: COUNT, SUM, AVG, MIN, MAX
7. GROUP BY and HAVING: Grouping Data and Filtering Groups
8. Basic Projects: Creating and querying a simple database (e.g., a student database)
⚙️ Intermediate Level:
1. Joins: INNER, LEFT, RIGHT, FULL OUTER JOIN
2. Subqueries: Using queries within queries
3. Indexes: Improving Query Performance
4. Data Modification: INSERT, UPDATE, DELETE
5. Transactions: ACID Properties, COMMIT, ROLLBACK
6. Constraints: PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK, DEFAULT
7. Views: Creating Virtual Tables
8. Stored Procedures & Functions: Reusable SQL Code
9. Date and Time Functions: Working with Date and Time Data
10. Intermediate Projects: Designing and querying a more complex database (e.g., an e-commerce database)
🏆 Expert Level:
1. Window Functions: RANK, ROW_NUMBER, LAG, LEAD
2. Common Table Expressions (CTEs): Recursive and Non-Recursive
3. Performance Tuning: Query Optimization Techniques
4. Database Design & Normalization: Understanding Database Schemas (Star, Snowflake)
5. Advanced Indexing: Clustered, Non-Clustered, Filtered Indexes
6. Database Administration: Backup and Recovery, Security, User Management
7. Working with Large Datasets: Partitioning, Data Warehousing Concepts
8. NoSQL Databases: Introduction to MongoDB, Cassandra, etc. (optional)
9. SQL Injection Prevention: Secure Coding Practices
10. Expert Projects: Designing, optimizing, and managing a large-scale database (e.g., a social media database)
💡 Bonus: Learn about Database Security, Cloud Databases (AWS RDS, Azure SQL Database, Google Cloud SQL), and Data Modeling Tools.
👍 Tap ❤️ for more
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𝐏𝐚𝐲 𝐀𝐟𝐭𝐞𝐫 𝐏𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 - 𝐆𝐞𝐭 𝐏𝐥𝐚𝐜𝐞𝐝 𝐈𝐧 𝐓𝐨𝐩 𝐌𝐍𝐂'𝐬 😍
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Hurry, limited seats available!🏃♀️
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10 Must-Have Habits for Data Analysts 📊🧠
1️⃣ Develop strong Excel & SQL skills
2️⃣ Master data cleaning — it’s 80% of the job
3️⃣ Always validate your data sources
4️⃣ Visualize data clearly (use Power BI/Tableau)
5️⃣ Ask the right business questions
6️⃣ Stay curious — dig deeper into patterns
7️⃣ Document your analysis & assumptions
8️⃣ Communicate insights, not just numbers
9️⃣ Learn basic Python or R for automation
🔟 Keep learning: analytics is always evolving
💬 Tap ❤️ for more!
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🚀 Agent.ai Challenge is LIVE!
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A step-by-step guide to land a job as a data analyst
Landing your first data analyst job is toughhhhh.
Here are 11 tips to make it easier:
- Master SQL.
- Next, learn a BI tool.
- Drink lots of tea or coffee.
- Tackle relevant data projects.
- Create a relevant data portfolio.
- Focus on actionable data insights.
- Remember imposter syndrome is normal.
- Find ways to prove you’re a problem-solver.
- Develop compelling data visualization stories.
- Engage with LinkedIn posts from fellow analysts.
- Illustrate your analytical impact with metrics & KPIs.
- Share your career story & insights via LinkedIn posts.
I have curated best 80+ top-notch Data Analytics Resources 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you 😊
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𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 | Microsoft & AWS included😍
- Microsoft Courses
- IT/Software
- Data Science & ML
- AI & Generative AI
- Management
- Cyber Security
- Cloud Computing
𝗘𝗻𝗿𝗼𝗹𝗹 𝗡𝗼𝘄 & 𝗚𝗲𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱👇:-
https://pdlink.in/48wVJ0O
Prep for jobs with AI mock interviews & resume builder
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📈 Want to Excel at Data Analytics? Master These Essential Skills! ☑️
Core Concepts:
• Statistics & Probability – Understand distributions, hypothesis testing
• Excel – Pivot tables, formulas, dashboards
Programming:
• Python – NumPy, Pandas, Matplotlib, Seaborn
• R – Data analysis & visualization
• SQL – Joins, filtering, aggregation
Data Cleaning & Wrangling:
• Handle missing values, duplicates
• Normalize and transform data
Visualization:
• Power BI, Tableau – Dashboards
• Plotly, Seaborn – Python visualizations
• Data Storytelling – Present insights clearly
Advanced Analytics:
• Regression, Classification, Clustering
• Time Series Forecasting
• A/B Testing & Hypothesis Testing
ETL & Automation:
• Web Scraping – BeautifulSoup, Scrapy
• APIs – Fetch and process real-world data
• Build ETL Pipelines
Tools & Deployment:
• Jupyter Notebook / Colab
• Git & GitHub
• Cloud Platforms – AWS, GCP, Azure
• Google BigQuery, Snowflake
Hope it helps :)
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📊 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: How do you find Duplicate Records in a table?
🙋♂️ 𝗠𝗲: Use GROUP BY with HAVING to filter rows occurring more than once:
SELECT column_name, COUNT(*) AS duplicate_count
FROM your_table
GROUP BY column_name
HAVING COUNT(*) > 1;