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WWCode Data Science 7-Day Kaggle EDA Challenge

WWCode Data Science 7-Day Kaggle EDA Challenge

Ana Jesus / January 30, 2024

I participated in the WWCode Data Science 7-Day Kaggle EDA Challenge, a guided, week-long challenge running from January 24 to 30, 2024, aimed at practicing Exploratory Data Analysis (EDA) using Kaggle Notebooks. It was my first time working with Kaggle, and it helped me get comfortable with Python-based data analysis and basic visualization.

The challenge had three skill tracks (beginner, intermediate, advanced) — I followed the Beginner track and explored the Electric Vehicle Charging Dataset.

🗓️ Challenge Structure

Each day included a small task to build up EDA skills. Here's a breakdown of what I accomplished:

✅ Day 1: Dataset Overview

  • Explored dataset structure: columns, rows, data types
  • Identified multiple entries by the same userId

✅ Day 2: Missing Values & Data Characteristics

  • Checked for null values and column datatypes
  • Discovered patterns in repeated weekday usage per user

✅ Day 3: Basic Visualizations

  • Used bar and line charts to explore data
  • Grouped by weekday to see usage distribution
  • Visualized total charging sessions by day

✅ Day 4: Trend Patterns

  • Found Friday to be the most popular charging day
  • Most sessions last 2–4 hours
  • Identified weekday usage spread with moderate charging durations

✅ Day 5: Descriptive Statistics

  • Explored time-series data with startTime and endTime
  • Identified possible peak hours and session frequency

⏸️ Days 6–7:

  • Did not complete these days

📊 Key Takeaways

  • Friday is the busiest charging day, especially for users with frequent sessions
  • Charging duration is usually consistent, falling between 2–4 hours
  • There is a clear weekday usage pattern, but charging is still distributed throughout the week
  • First exposure to working with real-world data on Kaggle, using Python, Pandas, and Matplotlib

🛠 Technologies Used

  • Kaggle Notebooks
  • Python
  • Pandas
  • Matplotlib
  • Exploratory Data Analysis (EDA) concepts

Links

🎯 Skills Gained

  • Data cleaning and inspection
  • Grouping and aggregating with Pandas
  • Creating basic charts (bar, line, scatter)
  • Identifying patterns and trends
  • Using Kaggle as a collaborative data science platform

📌 Dataset

Electric Vehicle Charging Dataset – Contains user sessions, start and end times, charging durations, and user behavior over time.

👩‍💻 Reflections

This challenge was a valuable way to kick off the year. It pushed me to observe data more critically and translate numbers into stories, which is at the heart of good data science.

It was also interesting to get back into Python coding — I had previously worked with R and Python during my thesis, but I definitely needed a refresh. Kaggle turned out to be the perfect platform for that. The hands-on, guided format made it feel both practical and approachable, and it reminded me how much I enjoy working with data.

Now, I feel much more confident with Exploratory Data Analysis (EDA) and I’m excited to take on more Kaggle challenges in the future!