Introduction to Data Science and Analysis

Data science is about extracting useful insights from data to help make decisions. This section covers the basics, introducing you to key concepts like datasets, features, and labels.

Key Concepts:

• **Data:**Raw facts or observations collected for analysis.

• Dataset: A collection of data often organized in a table.

• Features: Input variables that help predict the outcome (e.g., Age, Salary)

• Labels: The output variable or the value you want to predict (e.g., House Price).

Moss_Add: Imagine features as ingredients, and the label as the dish you're cooking. The better you understand the ingredients, the better the final result!

Data Manipulation

After gathering your data, the next step is manipulating( to organize it and clean it up for analysis).Python makes this easy with two important tools: libraries like NumPy and Pandas are essential tools for this.

Core Libraries:

NumPy: A tool that helps you work with numbers and lists of numbers, making it faster and easier than doing math manually.

Pandas: This is the go-to tool for working with tables of data. Think of it like a supercharged spreadsheet where you can sort, clean, and filter your data with just a few commands.

Basic Tasks:

1.Loading Data:Most of the time, your data will come from external files like Excel or CSV files. Pandas makes it very easy to read these files and get your data into Python.

Loading a CSV file into a DataFrame df = pd.read_csv('data.csv') Explanation: This code reads a file called data.csv and stores it in a format called a DataFrame.