Analyze a Dataset with Descriptive Statistics

Table of Contents

1. Challenge

   Step 1: Load and inspect the dataset

   Descriptive statistics turn raw rows and columns into a profile you can explain to stakeholders. In this lab, you will build that profile for an employee compensation dataset using Python, pandas, and matplotlib.

   You will work in a single analysis script that loads a CSV file, computes summary statistics, saves distribution charts, and prints an interpretation you can share with a people analytics team. The lab's codebase lives in the application directory. It already includes analyze.py, data/employees.csv, and an output/ folder for saved charts. To begin, you will load the dataset and confirm its structure before adding any calculations.

   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. info> This lab experience was developed by the Pluralsight team using an internally developed AI tool. All sections were verified by human experts for accuracy prior to publications. However, content may still contain errors or inaccuracies, and we recommend independent verification.
   
   To report a problem or provide feedback, click here. Feedback may be used to improve accuracy in accordance with our Privacy Policy.

2. Challenge

   Step 2: Calculate measures of central tendency

   Your script now loads the compensation dataset and reports its structure. That inspection step confirms which columns are numeric and ready for statistical summaries.

   Here, you will add mean, median, and mode calculations for columns such as salary, years_experience, and performance_rating. Each measure highlights a different view of where values cluster in the distribution.

3. Challenge

   Step 3: Calculate measures of spread

   Central tendency shows where values cluster, but spread measures reveal how much variation exists across employees.

   In this step, you will add range, variance, and standard deviation calculations for the numeric columns in NUMERIC_COLUMNS. These metrics quantify how widely compensation and related fields vary.

4. Challenge

   Step 4: Compute the five-number summary

   Center and spread metrics describe a distribution in broad terms. Quartiles let you describe shape more precisely and flag unusual values.

   You will add the five-number summary and interquartile range for the numeric columns, then use the 1.5 × IQR rule to identify potential salary outliers.

5. Challenge

   Step 5: Visualize distributions

   Summary statistics quantify distribution characteristics, but charts make skewness and spread easier to communicate.

   You will add matplotlib visualizations that save histograms and box plots for the numeric columns in NUMERIC_COLUMNS. The saved files will complement the numeric summaries from earlier steps.

6. Challenge

   Step 6: Interpret the analysis

   Your script now computes statistics and saves charts. The final piece is a written summary that connects those numbers and visuals into a coherent dataset characterization.

   You will add an interpretation section that describes center, spread, and skewness for the compensation dataset, then run the completed script to review the full analysis end to end.

7. Challenge

   Lab Complete

   You built a descriptive statistics workflow in Python that characterizes an employee compensation dataset from first load through final interpretation.

   You loaded and inspected a CSV with pandas, calculated measures of central tendency and spread, computed quartiles and outlier fences, saved histogram and box plot visualizations, and synthesized those findings into a stakeholder-ready summary. These skills form a repeatable first pass you can apply to any tabular dataset before deeper modeling or inference work.