Introduction to Data Science and Toolchain Setup

With your environment fully configured, the next logical step is to validate that all components are working as expected before diving into your first end-to-end data analysis workflow. A misconfigured environment is one of the most common barriers for beginners, so we will walk through a quick validation check first, then build a simple, reproducible analysis project that covers every core step of a standard data science workflow, using a publicly available dataset that requires no additional downloads.

Environment Validation and First Data Analysis Script

Verify Core Library Installation

First, open the terminal appropriate for your operating system: use Anaconda Prompt on Windows, and the default Terminal on macOS/Linux. Activate the `ds_env` environment you created earlier with the command `conda activate ds_env`. If the command prompt shows `(ds_env)` at the beginning, the environment is successfully activated.

Next, run the following script to confirm all core libraries are installed and importable, and print their version numbers for future reference: ```python import sys print(f"Python version: {sys.version}")

import numpy as np print(f"NumPy version: {np.__version__}")

import pandas as pd print(f"Pandas version: {pd.__version__}")

import matplotlib print(f"Matplotlib version: {