Hi everyone! Welcome to the first lesson of this exciting data science notes course. I’m Kunal, and I’ll make sure you understand data science in the simplest and most practical way — no boring textbook definitions!
🔹 What Is Data Science? (Plain English, 30–40 secs)
Data Science is just a fancy way of saying we collect and study data to make better decisions. For example, Netflix recommending a movie, or a company deciding what product to launch next — that’s all data science.
(Optional visual example: “Say you run a pizza shop, and you want to know which pizza sells best on weekends — that’s data science in action.”)
🔹 Why Should You Learn Data Science? (40–60 secs)
Data is everywhere — from your phone to hospitals to banks.
Data science helps in:
Making smarter decisions
Predicting things (like stock prices or weather)
Automating things (like self-driving cars)
Giving personal experiences (like YouTube recommendations)
Also, great jobs and high salaries 🚀
🔹 Do You Need Math or Coding? (30–45 secs)
People often think you need to be a math genius. Not true! I’ll guide you through basic concepts, and we’ll use tools like Python and simple logic. No worries if you don’t know much math — we’ll build it step by step.
🔹 Major Fields Inside Data Science:
Field
What It Does
Data Analytics
Analyzing past data to find patterns, trends, and insights.
Machine Learning (ML)
Using algorithms to make predictions or automate decisions.
Data Engineering
Building pipelines and systems to collect, clean, and store large data sets.
Deep Learning
A branch of ML using neural networks for complex tasks like image/video/audio.
AI (Artificial Intelligence)
Broader field including ML, used for creating smart systems (like chatbots, robots).
Business Intelligence (BI)
Creating dashboards and reports for decision-making. Often used by managers.
Data Visualization
Presenting data using charts, graphs, dashboards. Tools like Power BI, Tableau.
Big Data
Handling massive amounts of data using tools like Hadoop, Spark.
Natural Language Processing (NLP)
Working with text and language (e.g., chatbots, translations, sentiment analysis).
Computer Vision
Teaching machines to understand images and videos.
🔹 Process and Steps of Data Science ? (Quick Overview)
STEP
SIMPLE PURPOSE
COMMON TOOLS (FOR BEGINNERS)
EXAMPLE: Pizza Shop
EXAMPLE: Netflix
1. Problem Definition
Understand what problem we are solving
Pen & Paper, Notion, Google Docs
“Which pizza sells best on weekends?”
“What movie should we recommend to a new user?”
2. Data Collection
Gather data from different sources
Excel, SQL, Python (Pandas), APIs
Order data from POS, feedback forms
User watch history, ratings, device types
3. Data Cleaning
Fix errors, remove duplicates, handle missing
Python (Pandas), Excel, Power Query
Remove duplicate orders, fix missing toppings info
