Master Data Science with Python: 17-Hour Beginner’s Guide to Text Classification

Why Choose Python for Data Science?

Python has become the undisputed leader in data science due to its intuitive syntax and powerful ecosystem. This completely free 17-hour course takes you from writing your first Python line to building a text classification model through 10 progressive modules. Here’s the complete learning roadmap:

Core Learning Path

graph LR
A[Python Basics] --> B[Pandas/NumPy]
B --> C[Web Scraping]
C --> D[Data Filtering]
D --> E[Data Visualization]
E --> F[GroupBy Operations]
F --> G[Regex]
G --> H[Data Cleaning]
H --> I[Machine Learning]
I --> J[Text Classification]

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Module Breakdown & Technical Implementation

Module 1: Python Fundamentals (5 hours)

Essential concepts for absolute beginners:

# Variables and operations
product_price = 49.9
discount_rate = 0.15
final_price = product_price * (1 - discount_rate)
print(f"Final price: ${final_price:.2f}")

# List operations
inventory = ["laptop", "mouse", "keyboard"]
for item in inventory:
    if len(item) > 4:  # Filter items with >4 characters
        print(item.upper())

Key learning objectives:

• Variables and data types
• Conditional statements (if/else)
• Loop structures (for/while)
• Function definition and usage

Watch Module: Python Fundamentals

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Module 2: Core Data Processing Libraries

NumPy vs Pandas comparison:

Library	Core Functionality	Application Example
NumPy	Multi-dimensional arrays/Math operations	Sales matrix calculations
Pandas	Tabular data manipulation/Data analysis	Customer data cleaning

Practical implementation:

import pandas as pd
import numpy as np

# Create sales dataset
sales_data = pd.DataFrame({
    "Product": ["A", "B", "A", "C"],
    "Revenue": [1200, 800, 1500, 600]
})

# Calculate average revenue with NumPy
avg_revenue = np.mean(sales_data["Revenue"])
print(f"Average revenue: ${avg_revenue}")

# Group by product using Pandas
product_summary = sales_data.groupby("Product")["Revenue"].sum()
print(product_summary)

Watch Module: Pandas/NumPy Deep Dive

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Module 3: Web Scraping Project

4-step web data extraction:

1. Identify target webpage (e.g., Wikipedia table)
2. Use pd.read_html() for extraction
3. Select appropriate table index
4. Convert to DataFrame for processing

# Web scraping example
box_office_table = pd.read_html("https://example.com/movie-data")[0]
print(box_office_table.head(3))  # Display first 3 rows

Applications: Competitor price tracking/Sentiment analysis
Watch Module: Web Scraping

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Modules 4-8: Core Data Processing Techniques

Data Filtering (Module 4)

customer_data = pd.DataFrame({
    "Name": ["John", "Sarah", "Mike"],
    "Age": [25, 17, 32],
    "Spend": [300, 150, 500]
})

# Filter adult customers
adult_customers = customer_data[customer_data["Age"] >= 18]

Data Visualization (Module 5)

customer_data.plot(kind='bar', x='Name', y='Spend', color='blue')
plt.title('Customer Spending Distribution')

GroupBy Operations (Module 6)

# Age group spending analysis
customer_data["AgeGroup"] = pd.cut(customer_data["Age"], 
                                bins=[0,18,30,50],
                                labels=["Teen","Young Adult","Adult"])
agegroup_spending = customer_data.groupby("AgeGroup")["Spend"].mean()

Regular Expressions (Module 7)

# Extract valid phone numbers
customer_data["Phone"] = ["212-555-1234", "4151234567", "invalid"]
valid_phones = customer_data[customer_data["Phone"].str.contains(r'^\d{3}-\d{3}-\d{4}')]

Data Cleaning (Module 8)

Solving common data issues:

# 1. Handle missing values
data.fillna(0, inplace=True) 

# 2. Remove duplicates
data.drop_duplicates(inplace=True)

# 3. Standardize date formats
data["Date"] = pd.to_datetime(data["Date"], errors='coerce')

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Modules 9-10: Machine Learning Implementation

ML Fundamentals (Module 9)

from sklearn.linear_model import LinearRegression

# Housing price prediction
model = LinearRegression()
model.fit(housing_features, prices)  # Train model
predicted_prices = model.predict(new_properties)  # Generate predictions

Text Classification (Module 10)

4-step sentiment analysis:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB

# 1. Text vectorization
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(movie_reviews)

# 2. Initialize classifier
classifier = MultinomialNB()

# 3. Train model
classifier.fit(X, sentiment_labels)

# 4. Predict new reviews
new_review = ["The plot was engaging but visuals were mediocre"]
new_vector = vectorizer.transform([new_review])
prediction = classifier.predict(new_vector)

Technical insight: TF-IDF converts text to numerical vectors
Watch Module: Text Classification

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Frequently Asked Questions (FAQ)

Q1: What programming background is required?

Zero prerequisites: Designed for complete beginners starting from “Hello World”

Q2: What skills will I gain?

You’ll be able to:

• Scrape and clean web data
• Perform exploratory data analysis
• Build basic ML models
• Implement text classification systems

Q3: Why Python for data science?

1. Natural language-like syntax
2. Optimized libraries (pandas/NumPy)
3. Rich ML ecosystem (scikit-learn)
4. Extensive community support

Q4: What tools are needed?

Only three essentials:

1. Python 3.x
2. Jupyter Notebook
3. Libraries: pandas, NumPy, matplotlib, scikit-learn

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Complete Course Navigation

Module	Content	Duration	Direct Link
1	Python Fundamentals	2.5h	Watch
2	Pandas/NumPy	3h	Watch
3	Web Scraping	1.5h	Watch
4	Data Filtering	1h	Watch
5	Data Visualization	2h	Watch
8	Data Cleaning	2h	Watch
10	Text Classification	2h	Watch

Industry insight: Data professionals spend 80% of time on data preparation (covered in Module 8). This course teaches the complete workflow from data collection → cleaning → analysis → modeling, preventing the common pitfall of knowing ML theory but lacking practical data skills.