βπ»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.
π 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)
π οΈ What I Coded (Highlights):
Practiced data analysis using NumPy
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
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
GitHub Repo: Link
π‘ 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 itβs like to apply Python in real business contexts.
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