🧑‍🏫 Session 1: Introduction | Notion

🎯 Session Objectives:

By the end of this session, students will be able to:

Understand the difference between Data Science and Data Analysis
what is the SQL and DataBase
Understand the types of SQL commands (DDL, DML)
Create a table
Insert and view data
Filter and sort results
Write and test real SQL queries

🔍 1. Difference Between Data Science and Data Analysis

🎯 In Simple Terms:

Feature	Data Analysis	Data Science
Goal	Understand what happened and why	Predict what will happen and make smarter decisions
Focus	Cleaning, summarizing, and visualizing data	Building models, using machine learning, and deep analysis
Tools	Excel, SQL, Tableau, Power BI	Python, R, SQL, Machine Learning Libraries
Skills	Basic statistics, SQL, dashboards	Programming, advanced statistics, algorithms
Output	Reports and dashboards	Predictive models, recommendations, automation

📘 Example:

Let’s say we have a company’s sales data:

A Data Analyst will analyze past sales and answer:
- What products sold the most?
- When did sales go down?
- Create dashboards and charts to show this.
A Data Scientist will go deeper and:
- Predict future sales using machine learning.
- Build a model to recommend products to customers.
- Use algorithms to find patterns the analyst might miss.

💡 What is SQL?

SQL stands for Structured Query Language.

It is the standard language used to communicate with relational databases

💾 What is a Database?