Learn with Yasir

Share Your Feedback

Learn Data Analysis with Python & Pandas – 100-Day Bootcamp for Beginners


Master Python & Pandas in 100 days with real-world projects! Analyze TikTok trends, Spotify data, and sports stats. Perfect for beginners (15+) – includes cheat sheets, datasets, and career prep. Start today!

🌐 Phase 1: Python & Pandas Foundation (Days 1-30)

📅 Week 1-2: Python Basics

Goal: Automate boring tasks with Python.

  • Day 1-3: Variables/Loops → Simulate a McDonald’s order system 🍟
  • Day 4-5: Functions → Build a meme generator (PIL library)
  • Day 6-7: Lists/Dicts → Track FIFA player stats ⚽

Pandas Sneak Peek:

  • Day 8: Pandas Series → Analyze your Spotify Wrapped 🎵
  • Day 9: DataFrames → Clean a messy COVID-19 dataset 🦠

📅 Week 3-4: Pandas Core Skills

Goal: Clean, filter, and analyze data like a pro.

  • Day 10: Reading Data → CSV/Excel/JSON (Try: Netflix Shows)
  • Day 11: Filtering → Find the most expensive Pokémon cards 💰
  • Day 12: groupby() → Compare pizza toppings by country 🍕
  • Day 13: apply() → Calculate BMI from health data 🏋️
  • Day 14: Data Detective Challenge → Solve a bank fraud case (fake data) 🕵️

Advanced Pandas:

  • Day 15: Multi-indexing → Analyze stock market tiers 📈
  • Day 16: pivot_table() → Summarize school exam results 📚
  • Day 17: merge() → Combine TikTok + Instagram hashtags

📊 Phase 2: Data Wrangling & Visualization (Days 31-60)

📅 Week 5-6: Data Cleaning

Goal: Handle messy real-world data.

  • Day 31: Missing Data → Fix a hospital patient record 🏥
  • Day 32: Duplicates → Clean e-commerce orders 🛒
  • Day 33: Regex → Extract emails from text 📧
  • Day 34: DateTime → Analyze Uber ride patterns 🚖
  • Day 35: Project: Clean a Wikipedia dataset

Pandas Pro Tips:

  • Day 36: eval() for fast calculations ⚡
  • Day 37: Styler → Highlight data in Jupyter 🎨

📅 Week 7-8: Visualization

Goal: Tell stories with data.

  • Day 38: Matplotlib → Plot your sleep cycle 😴
  • Day 39: Seaborn → Spotify song moods 🎧
  • Day 40: Plotly → Interactive map of UFO sightings 👽
  • Day 41: Pandas + Seaborn → Correlation heatmaps 🔥
  • Day 42: Misleading Graphs → Spot the lie 🤥

Pandas Integration:

  • Day 43: df.plot() → Customize plots directly in Pandas
  • Day 44: Export to HTML → Build a simple dashboard

💻 Phase 3: Advanced Pandas & Real Projects (Days 61-100)

📅 Week 9-10: Web Scraping + APIs

Goal: Extract live data.

  • Day 61: Pandas + BeautifulSoup → Scrape weather data 🌦️
  • Day 62: Pandas + APIs → Analyze Twitter trends 🐦
  • Day 63: Project: Real-time COVID-19 tracker

Optimization:

  • Day 64: dtype optimization → Reduce memory usage by 70%
  • Day 65: swifter → Speed up apply() functions ⚡

📅 Week 11-12: Machine Learning with Pandas

Goal: Prep data for ML models.

  • Day 66: Feature engineering → Predict exam scores 📝
  • Day 67: One-hot encoding → Classify spam emails 📧
  • Day 68: Project: House price predictor 🏠

Pandas Tricks:

  • Day 69: pd.cut() → Bin data into categories
  • Day 70: qcut() → Auto-binning by quantiles

📅 Week 13-14: Capstone Projects

Choose Your Track:

  1. Business: Optimize supermarket sales 🛒
  2. Social Media: Predict viral TikTok songs 🎶
  3. Sports: NBA player performance dashboard 🏀

Final Deliverables:

  • Jupyter Notebook report
  • Interactive Plotly dashboard
  • GitHub repository

🎁 Pandas Cheat Sheet

# Top 10 Pandas Tricks  
1. df.query('price > 100')  # Fast filtering  
2. df.value_counts(normalize=True)  # Percentages  
3. df.nlargest(5, 'likes')  # Top N rows  
4. pd.read_clipboard()  # Paste data from Excel  
5. df.style.background_gradient()  # Heatmap in Notebook

📂 Dataset Ideas

| Topic | Dataset Example | Pandas Skill Applied |
|—————-|———————————-|——————————-|
| Music | Spotify Top 100 | groupby(), datetime |
| Sports | FIFA 23 Player Stats | merge(), query() |
| Finance | Bitcoin Historical Prices | resample(), rolling() |

🛠️ Tools & Resources

  • For Slow Internet: Use PandasGUI (offline data exploration).
  • Debugging Helper: ChatGPT → “Explain this Pandas error: [paste error]”
  • Extensions:
    • Add a “Pandas Battle” day (Day 85) where students compete to clean a messy dataset fastest.

📈 Tesla Stock Analysis Project

Skills Applied: Python, Pandas, Visualization, Time Series, Financial Analysis

🎯 Learning Goals

✔ Analyze Tesla’s stock performance (2010-Present)
✔ Compare with competitors (Ford, GM, NIO)
✔ Predict trends using moving averages
✔ Build an interactive dashboard

Day 1: Data Collection & Setup

📌 Task: Gather Tesla’s historical stock data

  • Tools:
  • Code Example: 
    import yfinance as yf
tesla = yf.Ticker("TSLA")
df = tesla.history(period="max")  # All-time data
df.to_csv("tesla_stock.csv")      # Save for offline use
  • Discussion:
    • Why Tesla? (EV market, Elon Musk’s influence, volatility)
    • Key metrics: Open, Close, High, Low, Volume

Day 2: Data Cleaning & EDA

📌 Task: Explore trends and clean data

  • Activities:
    • Handle missing values (Tesla had gaps pre-2013)
    • Add Daily Return column: 
      df['Daily_Return'] = df['Close'].pct_change() * 100
    • Plot: 
      df['Close'].plot(title="Tesla Stock Price (2010-2023)")
  • Critical Thinking:
    • Identify key events (e.g., 2020 stock split, COVID dip).

Day 3: Competitor Comparison

📌 Task: Benchmark vs. Ford (F), GM, NIO

  • Code: 
    competitors = yf.download("F GM NIO", start="2010-01-01")
  • Visualization: 
    competitors['Close'].plot(figsize=(10,5), title="EV Stocks Comparison")
  • Insight Questions:
    • Why did Tesla outperform legacy automakers?
    • How does NIO (China’s Tesla) compare?

Day 4: Technical Analysis

📌 Task: Predict trends with moving averages

  • Code: 
    df['MA_50'] = df['Close'].rolling(50).mean()  # 50-day avg
df['MA_200'] = df['Close'].rolling(200).mean()
  • Plot: 
    df[['Close', 'MA_50', 'MA_200']].plot(title="Moving Averages")
  • Golden Cross Strategy:
    • When MA_50 > MA_200 → Buy signal (highlight on plot).

Day 5: Volatility Analysis

📌 Task: Measure risk using Bollinger Bands

  • Code: 
    df['Std_Dev'] = df['Close'].rolling(20).std()
df['Upper_Band'] = df['MA_20'] + (df['Std_Dev'] * 2)
df['Lower_Band'] = df['MA_20'] - (df['Std_Dev'] * 2)
  • Discussion:
    • High volatility → High risk/reward (Tesla vs. S&P 500).

Day 6: Sentiment Analysis (Bonus)

📌 Task: Link stock spikes to Elon Musk’s tweets

  • Tools:
  • Example Event:
    • “Tesla stock too high IMO” tweet (May 1, 2020) → Price dropped 10%.

Day 7: Interactive Dashboard

📌 Deliverable: Build a Plotly Dash/Tableau report

  • Components:
    1. Stock price timeline
    2. Moving averages + Bollinger Bands
    3. Competitor comparison
    4. Tweet sentiment correlation (if data available)
  • Example Output:
    Tesla Dashboard

📂 Datasets & Resources

  1. Primary:
  2. Competitors: F (Ford), GM (General Motors), NIO
  3. News/Tweets:

🧠 Extended Learning (Optional)

  • Fundamental Analysis: P/E Ratio, Earnings Reports
  • Machine Learning: Predict next month’s price with LSTM
  • API Automation: Email stock alerts (Twilio + Python)

🎓 Assessment Ideas

  1. Quiz:
    • “What caused Tesla’s 2020 stock surge?” (Answer: S&P 500 inclusion)
  2. Presentation:
    • “Is Tesla overvalued? Evidence from data.”

Reply with:

  • “Code” for full Python notebook
  • “Dataset” for pre-cleaned CSV
  • “Slides” for a ready-made lesson PowerPoint