Day 2/100: Mastering Data Types and Logic Today was all about how Python handles information. I dived deep into the mechanics of data and mathematical operations. What I tackled today: Data Types: Understanding Integers, Floats, and Booleans. Subscripting: Pulling specific characters out of a string (like a pro!). The len() Function: Measuring the length of data. Type Conversion: Converting data (e.g., String to Integer) to make calculations possible. Math in Python: Using mathematical operators, the round() function, and assignment operators. Daily Project: Tip/Bill Calculator I created a program that calculates how much each person should pay when splitting a bill, including the tip. It’s a real-world tool built with just a few lines of code! Step by step, I'm getting more comfortable with the logic. 🚀 #Python #100DaysOfCode #DataTypes #CodingCommunity #SoftwareDevelopment

𝗦𝗶𝗺𝗽𝗹𝗲 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 𝗳𝗼𝗿 𝗢𝘂𝘁𝗹𝗶𝗲𝗿 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: 𝗭-𝘀𝗰𝗼𝗿𝗲 𝘃𝘀 𝗜𝗤𝗥 Are you using the ±3σ rule to detect outliers? It works well, but there are some important considerations. Let’s break down some common methods. 1️⃣ Z-score Method / ±3σ rule The 3 sigma rule measures how far a value is from the mean in units of standard deviation: Z = (x−μ)/σ If |Z| > 3 → potential outlier. ✅ Works well when data is approximately normally distributed. If the data is skewed, it can affect the results. 2️⃣ IQR Method / Boxplot Rule The IQR method is based on quartiles: - Q1 (25th percentile) - Q3 (75th percentile) - IQR = Q3 − Q1. Outlier rule: x < Q1−1.5⋅IQR x > Q3+1.5⋅IQR ✅ It is more robust to skewness because it uses medians and percentiles instead of the mean. #DataScience #Statistics #Python #MachineLearning #OutlierDetection #DataAnalysis #Research

I used to think data analysis was only about creating charts. But now I realize… Before visualization, there’s cleaning. Before insights, there’s understanding the data. Still learning, but enjoying the process 😊 #DataAnalytics #Python #SQL #Beginner

Many people think data analysis is about dashboards. In reality, most mistakes happen before visualization. In a practice dataset, I found: – missing values – duplicate records – wrong data types I used Python pandas to clean the data before analysis. All in all: Good charts cannot fix bad data. #DataAnalysis #Python #LearningInPublic#powerBI

 Day 1 of Python for Data Analytics: Master the Logic, Master the Data. 💻 I’m currently exploring the "backbone" of modern Data Science—Python. While the syntax is famous for its simplicity, I’ve learned that precision is key. Today’s focus was on: Variable Naming Rules: No leading numbers and respecting reserved keywords like if or while. Operator Precedence: Understanding how Python evaluates logic, from parentheses down to logical OR. Conditional Branching: Using if-elif-else ladders to create complex decision-making logic in my code.

How to export and import files between Python and Excel? Stop manual work. Use these two snippets to automate your data workflow with pandas: Import: Read Excel files into Python for analysis. Export: Save results back to Excel (use index=False for a clean file). Simple, fast, and error-free. #Python #Excel #Pandas #Automation #DataAnalysis

A small but powerful data lesson I’ve been revisiting lately: SQL helps you ask the right questions. Python helps you explore the answers. SQL is incredible for: filtering large datasets aggregating data efficiently understanding what is happening Python shines when you want to: clean and transform messy data explore patterns and outliers visualise trends and test assumptions What I’m learning is that the real strength isn’t choosing one over the other — it’s knowing when to use each and how they work together in a data workflow. Strong data analysis isn’t about tools alone; it’s about clarity of thinking. #Python #SQL #DataAnalytics #OpenData #LearningInPublic #DataSkills #MScJourney