Skip to content

lesson_plan.md

Latest commit

 

History

History
148 lines (104 loc) · 4.7 KB

lesson_plan.md

File metadata and controls

148 lines (104 loc) · 4.7 KB

🧠 Lesson Plan: Data Visualization with NumPy, Pandas & Matplotlib

Goal:

Learn how to visualize real-world data using Matplotlib and Pandas, starting from numerical operations (NumPy) to creating business-style insights with charts.

Lesson 1: NumPy Refresher — Working with Data

Objective: Master numerical operations and data generation for visualization. Topics:

  • What is NumPy and why it’s used for numerical data
  • Creating arrays: np.array(), np.arange(), np.linspace()
  • Array operations: addition, subtraction, mean, std, etc.
  • Indexing, slicing, reshaping
  • Generating random data: np.random.randn(), np.random.randint()

Practice:

  • Create an array of x values from 0 to 10 and compute y = x²
  • Generate 100 random values and calculate mean & std deviation

Lesson 2: Pandas Basics — Working with Real Tabular Data

Objective: Learn to load, explore, and prepare business datasets for visualization. Topics:

  • What is Pandas and why it’s used with Matplotlib
  • Series and DataFrames
  • Loading data from CSV: pd.read_csv()
  • Inspecting data: .head(), .info(), .describe()
  • Selecting columns and rows (.loc, .iloc)
  • Basic statistics: .mean(), .max(), .min()

Practice Dataset (from Kaggle): 🔹 Dataset: Sales Data Analysis 📍 Search: “Supermarket Sales Dataset” or “Retail Sales Dataset” 👉 Example: Supermarket Sales - Kaggle Tasks:

  • Load the CSV with Pandas
  • View first few rows and summary statistics
  • Find the top 5 cities by total sales

Lesson 3: Matplotlib Basics — Plotting with pyplot

Objective: Create and customize basic line charts for trend visualization. Topics:

  • import matplotlib.pyplot as plt
  • Basic line plot: plt.plot(x, y)
  • Labels, title, legend, grid
  • Changing line style, color, and markers
  • Displaying and saving plots (plt.show(), plt.savefig())

Practice Dataset (from Kaggle): 🔹 Dataset: Stock Prices or Business Trends 📍 Search: “Apple Stock Data”, “Tesla Stock Price”, or “Amazon Stock History” 👉 Example: Apple Stock Data - Kaggle Tasks:

  • Load the CSV and plot Date vs. Close price
  • Add labels, title, and grid
  • Highlight trends with colors or markers

Lesson 4: Multiple & Subplots

Objective: Compare multiple visualizations in one figure. Topics:

  • plt.subplot(rows, cols, index)
  • plt.subplots() and axes objects
  • Adjusting layout: plt.tight_layout()

Practice Dataset (from Kaggle): 🔹 Dataset: Sales Data by Product or Category 📍 Search: “E-commerce Sales Dataset” 👉 Example: E-commerce Data - Kaggle Tasks:

  • Create a 2x2 grid comparing monthly sales, profits, quantity, and customer count

Lesson 5: Common Plot Types

Objective: Master key visualization types for business insights. Topics:

  • Bar chart: plt.bar()
  • Scatter plot: plt.scatter()
  • Pie chart: plt.pie()
  • Histogram: plt.hist()

Practice Dataset (from Kaggle): 🔹 Dataset: Marketing or Customer Analysis 📍 Search: “Customer Personality Analysis” 👉 Example: Customer Personality Analysis - Kaggle Tasks:

  • Bar chart: total spending per category
  • Pie chart: customer types by region
  • Histogram: income distribution
  • Scatter plot: income vs. spending score

Lesson 6: Styling & Presentation

Objective: Make your charts clean, readable, and presentation-ready. Topics:

  • Figure size & DPI (plt.figure(figsize=(w,h)))
  • Colors, transparency (alpha), annotations (plt.text())
  • Grid lines, limits, tick customization (plt.xlim, plt.xticks)
  • Adding company logo or brand colors (optional)

Practice:

  • Recreate one of your earlier business charts with improved styling and annotations (e.g., “Monthly Sales Trend”)

Lesson 7: Mini Project — Business Insights Dashboard

Objective: Combine Pandas, NumPy, and Matplotlib to analyze a real business dataset. Dataset (Kaggle): 🔹 Supermarket Sales Dataset

Project Tasks:

  • Load and clean data

  • Use NumPy to calculate averages, max/min sales, and growth rate

  • Plot:

    • Line chart: monthly sales trend
    • Bar chart: top 5 products
    • Pie chart: sales by city

  • Add annotations for key insights (e.g., “Highest Sales in March”)

  • Save the figure as a professional report image or PDF