Analyze Relationships in Data using Correlation

Table of Contents

1. Challenge

   Step 1: Load and inspect the dataset

   Correlation analysis starts with clean, well-understood data. In this lab, you will build a Python script that loads employee operational metrics, inspects the table structure, and isolates the numeric columns you will analyze later.

   The lab's codebase lives in the application directory. You will work primarily in analysis.py, which already contains function stubs, and data/metrics.csv, which holds the dataset.

   The first row lists the dataset's column names, followed by a sample of the employee operational metrics you will analyze.

   By the end of this step, the CSV will be loaded into a pandas DataFrame, and you will have a definitive list of numeric columns for correlation work.

   Note: The functions in analysis.py are intentionally incomplete. For each task, implement the required functionality and replace the existing raise NotImplementedError(...) statement.

   info> Feeling stuck? Check out the matching solution/stepN/ folder for the step you're on to see a working implementation. Give it a try on your own first. The solution folder is your safety net, not your starting point. The dataset is located at application/data/metrics.csv. Before loading it with pandas, inspect the first few rows to become familiar with its columns.

   In the Terminal, run:

   cd /home/ps-user/workspace/application
   head data/metrics.csv
   

   The first row lists the dataset's column names, followed by a sample of the employee operational metrics you will analyze.

2. Challenge

   Step 2: Visualize variable pairs with scatter plots

   With the dataset loaded and the numeric columns identified, you can inspect relationships visually. A scatter plot reveals the direction and strength of a relationship and helps you determine whether a linear pattern is plausible before you compute a correlation coefficient.

   Here, you'll add plotting support with matplotlib and save scatter plots for two variable pairs the analytics team wants to analyze: income versus performance, and tenure versus support call volume.

3. Challenge

   Step 3: Calculate Pearson correlation coefficients

   Visual patterns are useful, but Pearson correlation puts a numeric value on linear association. Values range from -1 to 1, where values near 1 or -1 indicate strong linear relationships and values near 0 indicate weak linear relationships.

   In this step, you will compute Pearson coefficients for the same variable pairs you plotted so your visual read can be checked against a standard metric.

4. Challenge

   Step 4: Build and interpret a correlation matrix

   Individual pair coefficients answer targeted questions, but a correlation matrix shows every numeric relationship at once. Heatmaps make it easy to spot clusters of strong positive or negative associations across all variables.

   Here, you will build a full matrix from your numeric columns and save a seaborn heatmap for quick visual comparison.

5. Challenge

   Step 5: Summarize findings and assess modeling potential

   The analytics team needs more than charts and coefficients. They need a structured assessment: which relationships are strongest, which are weakest, which high correlation might be misleading, and which variables are the best candidates for a future predictive model.

   In this final step, you will add summary functions, then run the already wired pipeline to see the full report. A complete pipeline ties loading, plotting, correlation, and reporting into one script the team can rerun as new data arrives. main() is already wired up to call the functions you built throughout this lab: it loads the dataset, generates both scatter plots, computes correlations, builds the matrix, saves the heatmap, and prints the summary, caution note, and predictor recommendations.

   With Tasks 5.1 through 5.3 complete, run the full pipeline in the Terminal:

   python3 analysis.py
   

6. Challenge

   Lab Complete

   You built a correlation analysis script that loads operational metrics, visualizes relationships with scatter plots and a correlation heatmap, quantifies relationships using Pearson correlation coefficients, and generates a concise summary of the results. You practiced comparing visual patterns with numeric correlation values, identifying relationships across multiple variables, and recognizing when a strong correlation should be interpreted with caution.

   These skills transfer directly to exploratory data analysis: use visualizations to explore the data, confirm observations with correlation coefficients, compare relationships across variables with a correlation matrix, and always consider the context before drawing conclusions from the numbers.