SEO & Analytics Track

Data-Driven SEO & Analytics

Automate SEO analysis and build data-driven strategies. Learn the technical skills that separate top performers from the rest. Pair this with our Performance Measurement track to become a complete data-driven marketer.

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Python for SEO Analysis

Module 1

Use Python to automate SEO analysis tasks. Learn to scrape data, analyse backlinks, and generate insights programmatically at scale.

📚 What You'll Learn:

⚡ Python SEO libraries (beautifulsoup4, requests, pandas, scrapy)
⚡ Data scraping techniques and API integrations
⚡ Analysis automation and batch processing
⚡ Report generation with data visualisation
⚡ Error handling and retry logic for robust scripts
⚡ Working with large datasets efficiently
⚡ Exporting insights to CSV, Excel, and databases

💻 Try It Yourself:

Build a comprehensive Python SEO audit script: 1) Install required libraries (requests, beautifulsoup4, pandas). 2) Create functions to extract title tags, meta descriptions, and headings. 3) Add functionality to check for missing alt text and broken links. 4) Generate a CSV report with all findings.

🔧 Code Example:

# Example: Python SEO Audit Script
import requests
from bs4 import BeautifulSoup
import pandas as pd
from urllib.parse import urljoin, urlparse

def seo_audit(url):
    """Perform comprehensive SEO audit of a single page."""
    try:
        response = requests.get(url, timeout=10)
        response.raise_for_status()
        soup = BeautifulSoup(response.content, 'html.parser')
        
        audit_results = {
            'url': url,
            'status_code': response.status_code,
            'title': soup.find('title').text.strip() if soup.find('title') else 'Missing',
            'title_length': len(soup.find('title').text) if soup.find('title') else 0,
            'meta_description': soup.find('meta', attrs={'name': 'description'})['content'] if soup.find('meta', attrs={'name': 'description'}) else 'Missing',
            'h1_count': len(soup.find_all('h1')),
            'h1_text': soup.find('h1').text.strip() if soup.find('h1') else 'Missing',
            'images_missing_alt': len([img for img in soup.find_all('img') if not img.get('alt')]),
            'total_images': len(soup.find_all('img'))
        }
        return audit_results
    except Exception as e:
        return {'url': url, 'error': str(e)}

# Usage
urls = ['https://example.com/page1', 'https://example.com/page2']
results_df = pd.DataFrame([seo_audit(url) for url in urls])
results_df.to_csv('seo_audit_results.csv', index=False)

📈 Real-World Results:

187% traffic increase, 52% speed improvement, 10,000+ pages audited View Case Studies →

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Anchor Text Audits

Module 2

Systematically analyse anchor text patterns to identify opportunities and risks. Build automated audit processes that scale across large sites.

📚 What You'll Learn:

⚡ Anchor text analysis and distribution patterns
⚡ Pattern identification (exact match, partial match, brand, generic)
⚡ Risk assessment for over-optimisation penalties
⚡ Optimisation strategies and diversification
⚡ Backlink data extraction and processing
⚡ Automated reporting and visualisation
⚡ Competitive anchor text analysis

💻 Try It Yourself:

Create a comprehensive anchor text audit system: 1) Export your backlink data from Ahrefs/SEMrush (or use their API). 2) Write Python code to categorise anchor text (exact match, partial match, brand, generic). 3) Calculate distribution percentages. 4) Identify over-optimisation risks (>60% exact match is risky).

🔧 Code Example:

# Example: Anchor Text Audit Script
import pandas as pd

def categorise_anchor_text(anchor_text, target_keyword):
    """Categorise anchor text into types."""
    anchor_lower = anchor_text.lower()
    keyword_lower = target_keyword.lower()
    
    if anchor_lower == keyword_lower:
        return 'Exact Match'
    elif keyword_lower in anchor_lower:
        return 'Partial Match'
    elif any(brand in anchor_lower for brand in ['yourbrand', 'your company']):
        return 'Branded'
    elif anchor_lower in ['click here', 'read more', 'learn more', 'website']:
        return 'Generic'
    elif anchor_lower.startswith('http'):
        return 'URL'
    else:
        return 'Other'

def audit_anchor_texts(backlinks_df, target_keyword):
    """Perform comprehensive anchor text audit."""
    backlinks_df['anchor_category'] = backlinks_df['anchor_text'].apply(
        lambda x: categorise_anchor_text(x, target_keyword)
    )
    
    # Calculate distribution
    distribution = backlinks_df['anchor_category'].value_counts(normalize=True) * 100
    
    # Identify risks
    risks = []
    if distribution.get('Exact Match', 0) > 60:
        risks.append('High exact match percentage - risk of over-optimisation')
    
    return {'distribution': distribution.to_dict(), 'risks': risks}

📈 Real-World Results:

23 top 5 keyword rankings, Improved anchor text diversification View Case Studies →

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Keyword Clustering

Module 3

Group related keywords to inform content strategy. Learn algorithmic approaches to keyword clustering that reveal content opportunities and improve site architecture.

📚 What You'll Learn:

⚡ Clustering algorithms (K-means, hierarchical, semantic similarity)
⚡ Keyword grouping and semantic analysis
⚡ Content mapping and opportunity identification
⚡ TF-IDF and vectorization for keyword similarity
⚡ Working with keyword research data at scale
⚡ Visualisation of keyword clusters
⚡ Content gap analysis and prioritisation

💻 Try It Yourself:

Build a keyword clustering system: 1) Export 1000+ keywords from your research tool with search volume and difficulty. 2) Use Python to calculate keyword similarity (TF-IDF or embeddings). 3) Apply clustering algorithm (K-means or hierarchical). 4) Group keywords into 5-10 strategic clusters.

🔧 Code Example:

# Example: Keyword Clustering Script
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans

def cluster_keywords(keywords_df, n_clusters=8):
    """Cluster keywords using TF-IDF and K-means."""
    keyword_phrases = keywords_df['keyword'].tolist()
    
    # Create TF-IDF vectors
    vectorizer = TfidfVectorizer(max_features=100, stop_words='english')
    tfidf_matrix = vectorizer.fit_transform(keyword_phrases)
    
    # Perform K-means clustering
    kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
    clusters = kmeans.fit_predict(tfidf_matrix)
    
    keywords_df['cluster'] = clusters
    
    # Calculate cluster metrics
    cluster_summary = []
    for i in range(n_clusters):
        cluster_keywords = keywords_df[keywords_df['cluster'] == i]
        cluster_summary.append({
            'cluster': i,
            'keyword_count': len(cluster_keywords),
            'total_volume': cluster_keywords['search_volume'].sum(),
            'top_keywords': cluster_keywords.nlargest(5, 'search_volume')['keyword'].tolist()
        })
    
    return keywords_df, pd.DataFrame(cluster_summary)

📈 Real-World Results:

23 top 5 rankings, Improved site architecture and crawl efficiency View Case Studies →

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Web Scraping & Automation

Module 4

Automate data collection for SEO research. Learn ethical scraping practices and automation workflows that scale your SEO analysis capabilities.

📚 What You'll Learn:

⚡ Scraping techniques with BeautifulSoup and Scrapy
⚡ Data processing and cleaning pipelines
⚡ Automation workflows and scheduling
⚡ Ethical considerations and robots.txt compliance
⚡ Rate limiting and respectful scraping practices
⚡ Handling dynamic content with Selenium
⚡ API alternatives and when to use them

💻 Try It Yourself:

Build a competitor keyword ranking monitor: 1) Identify 5 competitor domains and 20 target keywords. 2) Use scraping or API to check rankings (or use SERP API). 3) Store results in a database or CSV. 4) Schedule weekly checks. 5) Create alerts for ranking changes.

🔧 Code Example:

# Example: Competitor Ranking Monitor
import requests
from bs4 import BeautifulSoup
import pandas as pd
from datetime import datetime
import time

def check_serp_ranking(keyword, domain, num_results=100):
    """Check if a domain ranks for a keyword."""
    # Note: Use a SERP API in production (SerpAPI, DataForSEO)
    # This example shows the structure
    
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
    }
    
    try:
        time.sleep(1)  # Be respectful with rate limiting
        # Use API in production for reliability
        ranking = None  # Would be 1-100 if found
        return ranking
    except Exception as e:
        print(f"Error checking {keyword}: {e}")
        return None

def monitor_rankings(keywords, domains):
    """Monitor rankings for multiple keywords and domains."""
    results = []
    
    for keyword in keywords:
        for domain in domains:
            ranking = check_serp_ranking(keyword, domain)
            results.append({
                'date': datetime.now().strftime('%Y-%m-%d'),
                'keyword': keyword,
                'domain': domain,
                'ranking': ranking
            })
    
    return pd.DataFrame(results)

📈 Real-World Results:

23 top 5 rankings achieved, Continuous automated monitoring View Case Studies →

Related Learning & Resources

Expand Your Skills

• Performance Measurement - Master analytics and attribution models
• AI & Automation - Automate SEO workflows with AI
• Digital PR - Build authority through link-earning campaigns

Services & Insights

• Technical SEO Services - Professional SEO services
• Marketing Analytics Best Practices - UK-focused analytics guide
• Predictive SEO Analytics - Stay ahead of algorithm changes

Ready to Master Data-Driven SEO?

Work through each module progressively. Each guide includes practical exercises, code examples, and real-world SEO applications.

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