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Data Science - Wintersemester 24/25
  • Welcome
  • What’s Data Science and How Do I Do It?
    • 📆Timeline
    • 🏴‍☠️R Overview
      • 📩Installation
      • 🐈‍⬛GitHub Setup
      • 🥗DataCamp Courses
    • 🐍Python Overview
      • 📩Installation
      • 🐈‍⬛GitHub Setup
      • 📦Virtual Environment Setup
      • 🥗DataCamp Courses
  • Introduction to Your Project
    • About the Project Guide
    • What is this Project About?
  • Exploratory Data Analysis (EDA)
    • Getting started
    • Discovering the Data 🔎
      • Initial Exploration Tasks
      • Initial Data Visualization
    • Data Cleaning and Transformation
      • Cleaning the Crime Dataset👮🏼
      • Cleaning the Weather Dataset🌦️
    • Data Visualization
      • Crime Rate Over Time
      • Crime Types
    • Grouping and Merging Data
    • Linear Regression
    • Impress us!
    • Internship Complete!
  • Advanced
    • Introduction
    • K-Means Clustering
      • The Clustering Model
      • Visualize the clusters
    • Impress us!
  • ✅Exercise Checklist
  • Legal Disclaimer
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  1. Exploratory Data Analysis (EDA)

Data Visualization

Now that we’ve cleaned our datasets, it’s time to dig deeper into the data through visualizations. In these exercises, we’ll explore broader trends in crime cases over time and then analyze specific aspects, like the most common crime types and demographic details.

As you create each plot, keep in mind that clarity is key—your visualizations should convey the main message without needing extra explanation. Starting with general trends, we’ll work toward a more detailed understanding. Let's dive in and see what the data reveals!

PreviousCleaning the Weather Dataset🌦️NextCrime Rate Over Time

Last updated 6 months ago