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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
Больше📈 Аналитический обзор Telegram-канала Data Analytics
Канал Data Analytics (@sqlspecialist) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 109 605 подписчиков, занимая 1 124 место в категории Технологии и приложения и 2 373 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 109 605 подписчиков.
Согласно последним данным от 19 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 624, а за последние 24 часа — -15, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.26%. В первые 24 часа после публикации контент обычно набирает 1.27% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 3 575 просмотров. В течение первых суток публикация набирает 1 388 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 9.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как row, sql, analytic, analyst, visualization.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Perfect channel to learn Data Analytics
Learn SQL, Python, Alteryx, Tableau, Power BI and many more
For Promotions: @coderfun @love_data”
Благодаря высокой частоте обновлений (последние данные получены 20 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.
109 605
Подписчики
-1524 часа
+1257 дней
+62430 день
Архив постов
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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!
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𝗙𝗥𝗘𝗘 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗜𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (Hyd/Pune/Noida)😍
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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
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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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📊 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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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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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;