Rahul Roy

🚀 Data Analyst | SQL Enthusiast | Python | MS Excel | Data Visualization | Looking for freelance work💻📊

[Rahul Roy @PatternDecoder]

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[Rahul Roy @PatternDecoder]

@TelsaNow Got double payment, great.

[Rahul Roy @PatternDecoder]

@tesla_news2025 Can confirm this works.

[Rahul Roy @PatternDecoder]

✅ Use EXISTS for large datasets
✅ Use IN for small value lists
#SQL #Database #Performance #DataAnalystJobs #FreelanceDataAnalyst #DataScienceJobs #DataDriven #AnalyticsJobs #FreelanceOpportunities #RemoteJobs #DataAnalystForHire #TechJobs #DataAnalysis #OpenToWork

[Rahul Roy @PatternDecoder]

Day 15 of my #30DayChallenge to secure a role in data science!

EXISTS vs IN in SQL
🔹 EXISTS – Checks if a subquery returns any rows (stops at first match).
🔹 IN – Checks if a value exists in a list/subquery result (compares all values).
🔹 Performance – EXISTS is often faster.

[Rahul Roy @PatternDecoder]

1️⃣ DELETE – Removes specific rows with a WHERE clause. Can be rolled back!
2️⃣ TRUNCATE – Removes ALL rows but keeps the table structure. Faster than DELETE.
3️⃣ DROP – Deletes the entire table (structure + data).

#DataAnalystJob #FreelanceDataAnalyst #DataScienceJobs #DataDriven

[Rahul Roy @PatternDecoder]

🚀 Day 14 of my #30DayChallenge to secure a role in data science!
SQL DELETE vs TRUNCATE vs DROP – What’s the Difference? 🧵⬇️

[Rahul Roy @PatternDecoder]

🚀 Day 13 of my #30DayChallenge to secure a role in data science!
Today, I explored the difference between WHERE vs. HAVING in SQL.

🔹 WHERE filters before aggregation (raw data).
🔹 HAVING filters after aggregation (grouped data).

[Rahul Roy @PatternDecoder]

Using WITHIN GROUP:
SELECT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY sales)
FROM sales_table;

#DataAnalystJobs #FreelanceDataAnalyst #DataScienceJobs #DataDriven #AnalyticsJobs #FreelanceOpportunities #RemoteJobs #DataAnalystForHire #TechJobs #DataAnalysis #OpenToWork

[Rahul Roy @PatternDecoder]

🚀 Day 12 of my #30DayChallenge to secure a role in data science!
Today, I explored WITHIN GROUP—a powerful SQL clause used in ordered-set aggregate functions like PERCENTILE_CONT().
It helps calculate percentiles, rankings, and mode by sorting data before aggregation.📊

[Rahul Roy @PatternDecoder]

4️⃣ Sort Categories Logically: Sort your bars in ascending or descending order, or in a meaningful sequence (like "Dream," "Torgersen," and "Biscoe" in your chart).

5️⃣ Avoid Overloading: Too many categories or colors can make a bar plot overwhelming. Focus on key comparisons.

[Rahul Roy @PatternDecoder]

🚀 Day 11 of my #30DayChallenge to secure a role in data science!

Tips for Better Bar Plots
1️⃣ Clear Labels: Ensure both the x-axis and y-axis have meaningful and concise labels. If your data uses units (e.g., mm), always include them.

#DataScience #DataVisualization #Seaborn

[Rahul Roy @PatternDecoder]

2️⃣ Consistent Colors: Use a color palette that is easy on the eyes and consistent across categories. For instance, you could use complementary or sequential colors for better differentiation.

3️⃣ Add Data Labels: Consider adding the exact values on top of the bars .

[Rahul Roy @PatternDecoder]

✨ Key highlights:

Leveraged hue to compare categories.
Ordered bars using the order parameter for better storytelling.
📚 Visualization tip: Always consider ordering your categories to enhance interpretability.

How do you structure your plots for maximum clarity?
#DataScience

[Rahul Roy @PatternDecoder]

🚀 Day 10 of my #30DayChallenge to secure a role in data science!

📊 Today’s focus: creating bar plots with Seaborn! This chart compares the average bill length of penguins 🐧 across islands, grouped by sex.

#DataScience #DataVisualization #Seaborn

[Rahul Roy @PatternDecoder]

🎨 Key techniques:

Used hue and style to highlight patterns across categories.
Experimented with size and color palettes for clarity.
📈 Visualization is a powerful storytelling tool in data science. How do you make your plots stand out?

#DataScience #DataVisualization #Python

[Rahul Roy @PatternDecoder]

Day 9 of my #30DayChallenge to land a data science role!

🔍 Today, I explored Seaborn's lineplot to visualize relationships in data. The chart below shows the relationship between bill length and flipper length, categorized by penguin sex 🐧.

#DataScience #DataVisualization

[Rahul Roy @PatternDecoder]

Clean data = better models, sharper predictions! 💡
What’s your go-to preprocessing tip? Share below! ⬇️
#DataAnalystJobs #FreelanceDataAnalyst #DataScienceJobs #DataDriven #AnalyticsJobs #FreelanceOpportunities #RemoteJobs #DataAnalystForHire #TechJobs #DataAnalysis #OpenToWork

[Rahul Roy @PatternDecoder]

Struggling with inconsistent data? Normalize it!
Whether it's scaling or handling outliers, data preprocessing is KEY to unlocking meaningful insights.

Try MinMaxScaler in Python to scale your features:

scaler = MinMaxScaler()
scaled_data = https://t.co/JWWDufmrvU_transform(df)

[Rahul Roy @PatternDecoder]

Day 9 of my 30-Day freelance work/job Hunt Challenge

🔢 Data Analysis Hack: Turn Messy Data into Insights!

🧵⬇️