Phillip Manywanda

[Phillip Manywanda @DataguyPhill]

@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! 🚀🐍

[Phillip Manywanda @DataguyPhill]

🚀 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

[Phillip Manywanda @DataguyPhill]

@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 = 💡

[Phillip Manywanda @DataguyPhill]

@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! 🔥

[Phillip Manywanda @DataguyPhill]

@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! 🚀

[Phillip Manywanda @DataguyPhill]

@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! ✅

[Phillip Manywanda @DataguyPhill]

Thanks @TDataImmersed @daBert17055

[Phillip Manywanda @DataguyPhill]

🚀 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! 🧵👇

[Phillip Manywanda @DataguyPhill]

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:
🌐 hhttps://anaconda.cloud/share/notebooks/bab3f1ea-092c-4be5-ac0d-4b16fad8224e/overview

[Phillip Manywanda @DataguyPhill]

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!

[Phillip Manywanda @DataguyPhill]

🔄 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!

[Phillip Manywanda @DataguyPhill]

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!

[Phillip Manywanda @DataguyPhill]

💰 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! 🛳️

[Phillip Manywanda @DataguyPhill]

👀 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! 📊

[Phillip Manywanda @DataguyPhill]

🔁 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? 🤯

[Phillip Manywanda @DataguyPhill]

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! 📷

[Phillip Manywanda @DataguyPhill]

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!

[Phillip Manywanda @DataguyPhill]

🧼 Why is data cleaning important?

Missing values can bias analysis 📉
Duplicates distort insights 🔄
Outliers skew statistics 📊
A clean dataset = better decisions! ✅

[Phillip Manywanda @DataguyPhill]

@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! ✨

[Phillip Manywanda @DataguyPhill]

🌟 Week 3 of my Python journey was all about diving into File Handling, CSVs, and NumPy! 🚀
From reading Titanic data to exploring arrays with NumPy, this week was packed with exciting tasks. Let’s break it down: 🧵
@DabereNnamani @TDataImmersed @JacobAjala #TDI

[Phillip Manywanda @DataguyPhill]

@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! 🐍