Data Science & Machine Learning

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Essential Excel Functions for Data Analysts 🚀 1️⃣ Basic Functions SUM() – Adds a range of numbers. =SUM(A1:A10) AVERAGE() – Calculates the average. =AVERAGE(A1:A10) MIN() / MAX() – Finds the smallest/largest value. =MIN(A1:A10) 2️⃣ Logical Functions IF() – Conditional logic. =IF(A1>50, "Pass", "Fail") IFS() – Multiple conditions. =IFS(A1>90, "A", A1>80, "B", TRUE, "C") AND() / OR() – Checks multiple conditions. =AND(A1>50, B1<100) 3️⃣ Text Functions LEFT() / RIGHT() / MID() – Extract text from a string. =LEFT(A1, 3) (First 3 characters) =MID(A1, 3, 2) (2 characters from the 3rd position) LEN() – Counts characters. =LEN(A1) TRIM() – Removes extra spaces. =TRIM(A1) UPPER() / LOWER() / PROPER() – Changes text case. 4️⃣ Lookup Functions VLOOKUP() – Searches for a value in a column. =VLOOKUP(1001, A2:B10, 2, FALSE) HLOOKUP() – Searches in a row. XLOOKUP() – Advanced lookup replacing VLOOKUP. =XLOOKUP(1001, A2:A10, B2:B10, "Not Found") 5️⃣ Date & Time Functions TODAY() – Returns the current date. NOW() – Returns the current date and time. YEAR(), MONTH(), DAY() – Extracts parts of a date. DATEDIF() – Calculates the difference between two dates. 6️⃣ Data Cleaning Functions REMOVE DUPLICATES – Found in the "Data" tab. CLEAN() – Removes non-printable characters. SUBSTITUTE() – Replaces text within a string. =SUBSTITUTE(A1, "old", "new") 7️⃣ Advanced Functions INDEX() & MATCH() – More flexible alternative to VLOOKUP. TEXTJOIN() – Joins text with a delimiter. UNIQUE() – Returns unique values from a range. FILTER() – Filters data dynamically. =FILTER(A2:B10, B2:B10>50) 8️⃣ Pivot Tables & Power Query PIVOT TABLES – Summarizes data dynamically. GETPIVOTDATA() – Extracts data from a Pivot Table. POWER QUERY – Automates data cleaning & transformation. You can find Free Excel Resources here: https://t.me/excel_data Hope it helps :) #dataanalytics

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🚀 Data Science Roadmap 2026 📘 Phase 2: Mathematics for Data Science 📖 Topic 7: Descriptive Statistics — Range, Percentiles, Quartiles, IQR & Five-Number Summary Welcome back! 👋 In the previous lesson you covered Probability Distributions. Now we’re moving to Descriptive Statistics — how we summarize data without predicting the population. Today we’ll cover: Range, Percentiles, Quartiles, IQR, Five-number summary, Outlier detection These are core for EDA. 🔹 1. What is Descriptive Statistics? Summarizes key characteristics of a dataset. Example: Salaries: 30000, 35000, 40000, 45000, 50000 Instead of checking each value, use: Min, Max, Mean, Median, Quartiles, Percentiles, Std Dev 🔹 2. Range Formula: Range = Maximum − Minimum Example: 10, 20, 30, 40, 50 → Range = 50 − 10 = 40 Note: Very sensitive to outliers. 50 → 500 makes range jump to 490. 🔹 3. Percentiles ⭐ Value below which X% of observations fall. 50th Percentile = Median 25th Percentile = 25% at or below 90th Percentile = 90% at or below 🔹 4. Real-World Example 90th percentile score ≠ 90% marks. It means you did better than ∼90% of people. 🔹 5. Quartiles Divide data into 4 equal parts: Q1 = 25th percentile Q2 = 50th percentile = Median Q3 = 75th percentile 🔹 6. Visualizing Quartiles 0% ---- Q1 ---- Q2 ---- Q3 ---- 100% 25% 50% 75% 🔹 7. Interquartile Range (IQR) ⭐ Formula: IQR = Q3 − Q1 Example: Q1=20, Q3=60 → IQR = 40. Middle 50% spans 40 units. 🔹 8. Why IQR Matters Less affected by outliers than Range. Data: 10,20,30,40,50,1000 → Range=990 but IQR ignores the 1000. 🔹 9. Detecting Outliers Using IQR ⭐ Lower Bound = Q1 − 1.5 × IQR Upper Bound = Q3 + 1.5 × IQR Values outside = potential outliers 🔹 10. Outlier Example Q1=20, Q3=60 → IQR=40 Lower = 20-60 = -40 Upper = 60+60 = 120 So < -40 or > 120 are outliers 🔹 11. Five-Number Summary ⭐ 1. Minimum 2. Q1 3. Median 4. Q3 5. Maximum Ex: 10, 20, 30, 40, 50 🔹 12. Box Plot Visualizes the 5-number summary. Box = Q1 to Q3. Line inside = Median. Whiskers = range without outliers. 🔹 13. Python Example

import numpy as np

data = [10, 20, 30, 40, 50, 60, 70]
q1 = np.percentile(data, 25)
median = np.percentile(data, 50)
q3 = np.percentile(data, 75)
iqr = q3 - q1
print("Q1:", q1, "Median:", median, "Q3:", q3, "IQR:", iqr)

🔹 14. Descriptive Statistics in Pandas

import pandas as pd

df = pd.DataFrame({"Salary": [30000, 35000, 40000, 45000, 50000]})
print(df["Salary"].describe())

describe() gives Count, Mean, Std, Min, 25%, 50%, 75%, Max 🔹 15. Real-World Example Transactions: Q1=₹500, Median=₹1000, Q3=₹2000 → IQR=₹1500 Use IQR to flag fraud, bulk orders, errors, or VIP customers. Investigate before deleting. 🔹 16. Range vs IQR Range: Easy but outlier-sensitive IQR: Middle 50% only, robust to outliers 🔹 17. Percentile vs Percentage Percentage = out of 100. Ex: 80% marks Percentile = relative position. Ex: 90th percentile 🔹 18. Common Mistakes ❌ 90th percentile = 90% score ❌ Deleting all outliers blindly ❌ Thinking IQR covers all data 🎯 Practice Questions 1. Range of 10, 20, 30, 40, 50 = ? 2. Median = which percentile? 3. Q1=25, Q3=75 → IQR = ? 4. Upper outlier boundary formula? 5. 5 components of five-number summary? 🎯 Key Takeaways ✅ Range = Max - Min ✅ Q1=25th, Q2=50th=Median, Q3=75th ✅ IQR = Q3 - Q1 ✅ 5-number summary = Min, Q1, Median, Q3, Max ✅ Percentile ≠ Percentage 👉 Double Tap ❤️ For More ----- 2.46 ₽ · /balance_help

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✅ SQL for Data Science 🗄️📊 👉 SQL is one of the most important skills for Data Scientists and Data Analysts. Almost every company stores data inside databases, and SQL helps retrieve and analyze that data. 🔹 1. What is SQL? SQL = Structured Query Language 👉 Used to: ✔ Store data ✔ Retrieve data ✔ Filter data ✔ Analyze data 🔥 2. Common Database Systems ✔ MySQL ✔ PostgreSQL ✔ SQLite ✔ Microsoft SQL Server 🔹 3. Basic SQL Query ✅ SELECT Statement Used to retrieve data from a table. SELECT * FROM employees; 👉 ** means all columns. 🔹 4. Select Specific Columns SELECT name, salary FROM employees; 🔹 5. WHERE Clause ⭐ Used for filtering data. SELECT * FROM employees WHERE salary > 50000; 🔹 6. ORDER BY Sort data. SELECT * FROM employees ORDER BY salary DESC; ✔ ASC → Ascending ✔ DESC → Descending 🔹 7. Aggregate Functions ⭐ Used for calculations. Function: COUNT() Purpose: Count rows Function: SUM() Purpose: Total Function: AVG() Purpose: Average Function: MAX() Purpose: Highest value Function: MIN() Purpose: Lowest value ✅ Example SELECT AVG(salary) FROM employees; 🔹 8. GROUP BY ⭐ Used to group data. SELECT department, AVG(salary) FROM employees GROUP BY department; 🔹 9. Why SQL is Important? ✔ Most asked interview skill ✔ Used daily by analysts & data scientists ✔ Essential for working with databases 🎯 Today’s Goal ✔ Learn SELECT queries ✔ Filter using WHERE ✔ Use aggregate functions ✔ Understand GROUP BY 👉 SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v 🗄️🔥 💬 Tap ❤️ for more!

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