Data Science Study Plan

🌟 Beginner Data Science Guide (6 Weeks) 🌟

🧠 Core Concepts (Explained Simply)

• Data Cleaning: Fixing typos, missing values. Like wiping dust off a mirror.
• Statistical Analysis: Finding patterns & averages. Like discovering trends in your shopping list.
• Data Visualization: Turning data into charts. Like painting your data to tell a story.
• Machine Learning (Bonus): Teaching machines to predict. Like training a pet to recognize your voice.

🛠️ Tools You’ll Use

• Python: The main language for data science.
• Pandas: For working with CSV files and tables.
• Jupyter: Combine code + output in notebooks.
• Tableau Public: Create dashboards without coding.
• Seaborn & Matplotlib: Beautiful visualizations.
• Kaggle: Datasets + practice competitions.

📆 6-Week Study Plan (With Free Resources)

Week	Focus	Free Resource
1	Python Basics	DataCamp Intro
2	Data Cleaning	Kaggle Pandas Course
3	Exploratory Data Analysis	Kaggle Challenge
4	Visualization	Seaborn Tutorial + Tableau
5	Project Work	Start dashboard project
6	Final Project	Polish, publish & share your work!

🚀 Capstone Project: Interactive Dashboard

• Dataset: Find one on Kaggle (e.g. Netflix, COVID, Sales)
• Clean it: Use Pandas to fix missing values & typos
• Analyze: Use charts (bar, line, pie) with Matplotlib/Seaborn
• Build dashboard: Use Tableau or Streamlit/Plotly
• Share: Post to Tableau Public or GitHub

🧪 5 Practice Problems (with Hints)

1. Load Netflix CSV Dataset
   df = pd.read_csv('netflix_titles.csv')
2. Count missing values
   df.isnull().sum()
3. Filter for Movies Only
   df[df['type'] == 'Movie']
4. Top 5 countries
   df['country'].value_counts().head(5)
5. Plot movies per year
   df['release_year'].value_counts().sort_index().plot(kind='bar')

✨ Tip: Start with one line of code, and experiment with it!