@TDataImmersed @DabereNnamani ✅ Cleaned messy data ✅ Uncovered job trends ✅ Created powerful visuals EDA & Visualization are 🔑 for Data Science! Want to see everything? Check out my notebook: 🌐 https://t.co/AYkbQwLt05 Which visualization do you use most? Let’s discuss! 🚀🐍

@TDataImmersed @DabereNnamani 🔥 Seaborn for Advanced Plots Heatmap: Correlation between key variables 🔥 Box Plot: Job title vs company ratings 🎭 Pair Plot: Relationships between salary, rating & founding year Aesthetics + Insights = 💡

@TDataImmersed @DabereNnamani 📉 Matplotlib for EDA Histogram: Salary distribution 💰 Bar Chart: Top locations for Data Science jobs 🗺️ Line Plot: Salary trends by company size 🏢 Visualizing data brings numbers to life! 🔥

@TDataImmersed @DabereNnamani 📊 EDA = Knowing Your Data Summary stats for Rating, Salary, and Revenue Identified top job titles & their average ratings Analyzed salary trends by company size EDA helps spot patterns & anomalies fast! 🚀

@TDataImmersed @DabereNnamani 🧼 Data Cleaning is the foundation of good analysis! Handled missing values 🕵️ Extracted & cleaned Salary Estimate 💰 Standardized Company Names & Locations 📍 Data cleaning = better insights! ✅

🚀 Week 6 was all about Exploratory Data Analysis (EDA) & Visualization! I cleaned, analyzed, and visualized an uncleaned dataset of Data Science jobs using Pandas, Matplotlib & Seaborn. Let's break it down! 🧵 @TDataImmersed #TDI @DabereNnamani

Thanks @TDataImmersed @daBert17055

Wrap-Up & Full Notebook ✅ Data cleaned ✅ New features created ✅ Data merged ✅ Insights uncovered This was real-world data prep at its finest! Check out my full notebook here: 🌐 h https://anaconda.cloud/share/notebooks/bab3f1ea-092c-4be5-ac0d-4b16fad8224e/overview

String Cleaning & Deck Extraction 🔡 Text manipulation in Pandas I extracted the deck from the Cabin column to analyze survival rates by deck. 📷 Question ➡️ 📷 My Solution Text data isn’t always clean—Pandas makes it easy!

🔄 Merge vs. Concatenate? merge() = Joins datasets on a key (like PassengerId) concat() = Stacks datasets (vertically or horizontally) 📷 Question ➡️ 📷 My Solution These techniques help when dealing with multiple data sources!

Creating New Features 🛠️ Feature Engineering I added: ✅ FamilySize = (sibsp + parch + 1) ✅ FarePerPerson = Fare ÷ FamilySize 📷 Question ➡️ 📷 My Solution Why? These features give new insights into passengers’ social & economic backgrounds!

💰 Outliers distort averages! I detected extreme fare prices using the IQR method and capped them instead of removing. 📷 Question ➡️ 📷 My Solution Capping ensures we keep all data while limiting extreme values! 🛳️

👀 Data transformation step! Instead of 1, 2, 3, I converted Pclass into "1st Class", "2nd Class", "3rd Class" for better readability. 📷 Question ➡️ 📷 My Solution Why? Clear labels improve data storytelling! 📊

🔁 Duplicate records skew analysis! Using drop_duplicates(), I checked and removed any duplicates in Titanic data. 📷 Question ➡️ 📷 My Solution Have you ever encountered duplicate headaches? 🤯

You may not know what to do with missing values... 🤔 Drop or Fill? dropna() – Remove missing data (good if there’s little missing) fillna() – Replace missing values (mean, median, etc.) I used the median for Age to avoid outliers! 📷

Finding Missing Data 🔍 Identifying missing values in the Titanic dataset using Pandas: 📷 Question ➡️ 📷 My Solution Missing values can break analysis—step 1 is always detection!

🧼 Why is data cleaning important? Missing values can bias analysis 📉 Duplicates distort insights 🔄 Outliers skew statistics 📊 A clean dataset = better decisions! ✅

🚀 Week 5 was all about Data Cleaning & Transformation with Pandas! From handling missing values to merging DataFrames, this was a deep dive into real-world data prep. Let’s break it down! 🧵👇

@DabereNnamani @TDataImmersed @JacobAjala That wraps up my Week 3 highlights! 🐍 Want to explore the complete code and dive into more details? Check it out here: 🌐 https://t.co/VRhcj8ulP5 What was your favorite part? Let’s discuss! ✨

@DabereNnamani @TDataImmersed @JacobAjala 📊 NumPy Adventures NumPy made math magical! I: Built and manipulated 1D/2D arrays Found fare stats (min, max, mean) for Titanic data Explored indexing and random arrays 🎲✨ 📷 Questions ➡️ 📷 My Solutions How do YOU use NumPy? Let me know! 🐍