Hey Guys
Is anybody working in Capgemini
One of us from our community need to get a referral in Capg

Please ping me on linkedin ,if you are a Capg employee

Let’s help each other and grow together😊

Hey Guys
Another freelancing role
Needed skill set - Snowflake,Aws,python,sql,Terraform, pyspark

Expectation -2.5 hours daily in evening

Please ping me urgently if you are interested on linkedin asap
linkedin.com/in/adityabansal095

This is an urgent opportunity

#jobalert #jobopportunity #freelancer

India won 🏆
It was a great match
At a moment I thought , England might win it 🙂😬

🚀 Data Analytics Essentials — Skills Every Analyst Must Master

If you want to grow faster as a Data Analyst, it’s not just about tools.
You need the right mix of technical + business skills.

Here’s a quick breakdown 👇

🧠 Technical Skills

1️⃣ SQL
• Joins, GROUP BY, Window Functions
• Handling NULLs & duplicates
💡 Example: Use LEFT JOIN to identify churn vs non-churn users.

2️⃣ Excel
• Pivot Tables, Lookups, IF logic
• Fast data cleaning
💡 Example: Summarize 50K rows instantly using pivots.

3️⃣ Power BI / Tableau
• Data modeling, measures, filters
• Build dashboards that answer one clear question.
📊 Example: Track sales drop by region & month.

4️⃣ Python
• pandas for data cleaning
• matplotlib / seaborn for quick visuals
💡 Example: Cohort-wise revenue analysis.

5️⃣ Statistics Basics
• Mean vs Median, Variance, Correlation
⚠️ Example: Median often explains salary distribution better than average.

⸻

🤝 Soft Skills (Equally Important)

1️⃣ Business Thinking – Always ask why before how.
2️⃣ Communication – Explain insights in simple terms.
3️⃣ Problem Framing – Convert vague questions into clear metrics.
4️⃣ Attention to Detail – Small data errors break trust.
5️⃣ Stakeholder Management – Listen first, challenge with data.

⸻

📌 Lesson:
Tools alone won’t make you a great analyst.
The real edge comes from combining technical skills with business thinking.

🔥 Pick one skill from this list and improve it.

#DataAnalytics #SQL #Python #PowerBI #AnalyticsSkills

🚀 Thinking of Hosting a Live SQL Interview Masterclass

I’m considering conducting a live SQL interview masterclass where we’ll solve real interview questions and learn the thinking process behind them.

Topics would include:
• Conditional aggregation questions
• Ranking & window function problems
• Real interview questions from companies
• Common SQL interview traps

It will be a small live session (max 50 people) so we can keep it interactive.

Before planning it, I want to check interest 👇

Would you be interested in joining?

📊 Vote below.

🚀 Essential SQL Commands Every Data Professional Should Know

Whether you’re preparing for SQL interviews or working with databases daily, these commands form the backbone of SQL.

Here’s a quick refresher 👇

🔹 SELECT – Retrieve data from a table
🔹 INSERT – Add new records into a table
🔹 UPDATE – Modify existing data
🔹 DELETE – Remove specific records

📦 Database Structure

🔹 CREATE – Create new database objects (tables, databases)
🔹 ALTER – Modify the structure of an existing object
🔹 DROP – Delete an entire table or database

📊 Filtering & Analysis

🔹 WHERE – Filter records based on conditions
🔹 GROUP BY – Group rows with similar values
🔹 HAVING – Filter aggregated results

🔗 Working with Multiple Tables

🔹 JOIN – Combine data from multiple tables

⚡ Performance

🔹 INDEX – Speeds up data retrieval in large tables

📌 Tip:
Mastering these fundamentals makes solving complex SQL problems much easier.

🔥 Try this today:
Pick one command and write a small query to practice it.

#SQL #LearnSQL #DataEngineering #DataAnalytics

📊 Common SQL Window Functions You Should Know

Window functions = next-level SQL 🚀
They help you analyze data without losing rows.

🔢 ROW_NUMBER()
Gives a unique number to each row
👉 Perfect for deduplication & ordering

🏆 RANK()
Assigns rank with gaps
👉 If 2 people tie, next rank is skipped (1, 2, 2, 4)

🥇 DENSE_RANK()
Assigns rank without gaps
👉 No skipping (1, 2, 2, 3)

📈 SUM() OVER()
Calculates running totals
👉 Great for cumulative sales, revenue trends

⏮️⏭️ LAG() / LEAD()
Compare with previous/next row
👉 Useful for time-based analysis, growth tracking

⚡ Why it matters?
Because real-world data problems need row-level + aggregated insights together

🚨 New Video is LIVE! (SQL Interview Alert) 🚨

Most candidates fail these SQL questions…
Not because they’re hard —
But because they test how you think, not just syntax 👀

I’ve compiled Top 5 Hard SQL Interview Questions
that are actually asked in real interviews.

👉 Can YOU solve them all?

🎥 Watch now:
👉 TOP 5 HARD SQL INTERVIEW QUESTIONS | MOST CANDIDATES FAIL THESE QUESTIONS
https://youtu.be/mptGQeGlMWc

Comment honestly after watching:
How many did you get right? (1–5) 😏

⸻

🔥 If you’re preparing for:
• Data Analyst roles
• Data Engineering interviews
• SQL rounds

You don’t want to miss this.

#sql #dataengineer #datanalysis #sqlinterviewquestions

You can become a Data Engineer for FREE.

Here you have 5 project ideas (with resources):

𝟭. 𝗧𝗵𝗲 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗕𝗮𝘁𝗰𝗵 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲

- Get Data from an API
- Transform and load it into a database
- Schedule the process like in the real world
🔗PostgreSQL: https://lnkd.in/e2V9TR_H
🔗Openweathermap API: https://lnkd.in/eE6HgCXs

 𝟮. 𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗿𝗻 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲
- Build a local data warehouse
- Use data transformation tool to build tables
- Use a Reporting tool to build a dashboard on top

🔗dbt: https://lnkd.in/eDU_H3v8
🔗DuckDB: https://lnkd.in/exQn6wuF
🔗e-commerce dataset: https://lnkd.in/eNG55FKb

𝟯. 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲
- Generate real-time events
- Capture them and process them
- Store them in a local database like PostgreSQL

🔗Run Kafka on Docker: https://lnkd.in/eMKHQA9W
🔗Generate real-time data: https://lnkd.in/eEwgPEsS.

𝟰. 𝗧𝗵𝗲 𝗟𝗼𝗰𝗮𝗹 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲
- Process massive datasets using distributed compute
- Store the output as optimized Parquet files in minIO.

🔗MinIO qucikstar: https://charts.min.io/
🔗 Massive Dataset: https://lnkd.in/eVGTFxNc

𝟱. 𝗧𝗵𝗲 𝗪𝗲𝗯 𝗦𝗰𝗿𝗮𝗽𝗲𝗿 & 𝗡𝗼𝗦𝗤𝗟
- Build a Web Scrapper
- Extract text, image and links from website
- Store them as documents in a document database

🔗Beautifulsoup: https://lnkd.in/eZbeAUiU
🔗MongoDB in Docker: https://lnkd.in/ez7Fzfib

---

This are REAL Data Engineering projects.

I feel confused with correlated subqueries till date 😬

Since most of folks mentioned about CTEs and Subqueries here .

I will prepare a detailed video covering only CTEs and Subqueries soon🙂

🚀 2,500 on YouTube!

From 0 to 2.5K — built purely on SQL, data engineering, and consistency.

No shortcuts. Just showing up and sharing what I know.

To everyone who subscribed, watched, liked, or shared — thank you 🙏

Next goal is 5k

👉 Better content
👉 More real-world SQL
👉 More interview-focused learning

Let’s keep building together 🚀

If you still have not subscribed,please go ahead and subscribe today
YouTube.com/@adityabansal095