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Edward Chia
Edward Chia

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Day 24 - 25: Learning Python Libraries (NumPy, Pandas, Matplotlib)

✍🏻Log Date: 2 June 2025

For the past 2 days, I've been continuing with my Full Stack Development with AI course at NUS. The current module shifts into Python libraries β€” foundational tools that will support the AI-focused parts of the curriculum coming later.

πŸ“š What I Studied:

  • Got familiar with Jupyter Notebook as an interactive coding environment.
  • Explored NumPy for working with n-dimensional arrays and basic data analysis techniques:
    • np.array(), np.mean(), np.std(), np.percentile()
  • Learned Pandas basics:
    • Data structures like Series and DataFrames
    • Read, cleaned, modified, and wrote CSV files
    • Used accessors like df.iloc[-10:], df.index.size, df.shape[0], and methods like df.fillna()
  • Got a first look at Matplotlib for visualizing data:
    • Created line plots and scatter plots
    • Customized basic chart characteristics (labels, titles, colors)

πŸ› οΈ What I Coded (Highlights):
Practiced data analysis using NumPy

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GitHub Repo: Link

  • Simulated analysis of a customer dataset (~1000 data points) to extract business insights.
  • Identified and flagged statistical outliers based on spending thresholds using NumPy.
  • Grouped data into defined spending tiers (e.g. low, mid, high) using binning techniques.
  • Projected potential revenue uplift from a simulated marketing campaign using filtered data.

Applied Pandas to manipulate and explore datasets

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GitHub Repo: Link

  • Explored the structure of a user dataset (rows, columns, column names, and data types) using pandas.
  • Analyzed the distribution of user occupations and counted unique job titles.
  • Reviewed descriptive statistics of the dataset, including mean and least common ages.
  • Added a new salary column to the DataFrame.
  • Computed salary for each user by multiplying their age by 100.

Basic Data Visualization using Matplotlib

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GitHub Repo: Link

πŸ’‘ Reflection:
Learning how to derive insights from data has been quite satisfying. Even with a simple dataset, visualizing spending trends and segmenting users helped me better understand how data analysts think. It's still early, but this gave me a small taste of what it’s like to apply Python in real business contexts.

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