?? Day 13 of 365: Real-World Example – Analyzing a Public Dataset ??
Ajinkya Deokate
Data Scientist | Researcher | Author | Public Speaking Expert @PlanetSpark | Freelancer
Hey everyone!
Welcome to Day 13 of my #365DaysOfDataScience journey! ??
?? Today, we’re going hands-on with a real-world dataset. It’s one thing to learn techniques in isolation, but now we get to apply everything we’ve learned so far—from data wrangling to visualization—on an actual dataset. Think of it as a mini project that ties everything together!
?? What We’ll Be Doing Today:
End-to-End Practice:?
??- We’re practicing the entire workflow: from collecting the dataset to cleaning it, performing Exploratory Data Analysis (EDA), and visualizing insights.
??- This is exactly what you’ll be doing in real-world data science projects, so let’s dive in!
?? Learning Resources:
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??Tip: Don’t hesitate to explore different Kaggle datasets—there’s something for every interest!
?? Today’s Task:
- Choose a public dataset (Titanic, Iris, or any dataset that catches your eye!).
- Data Wrangling: Clean the data by handling missing values, formatting, and preparing it for analysis.
- Exploratory Data Analysis (EDA): Perform EDA to understand the data’s characteristics and trends.
- Visualize Insights: Use Matplotlib and Seaborn to create meaningful visualizations that summarize your findings.
Once you’ve completed your analysis, document the entire process in a Jupyter Notebook. This will be a great addition to your portfolio and a step toward becoming a confident data scientist!
Feel free to share your results, visualizations, and any cool insights you’ve uncovered with the group. We’re all learning from each other, so let’s see what we discover! ??
Happy analyzing, and let’s make this mini-project awesome! ??
See ya!
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