Data Driven SEO with Python

Chapter 1: Meeting the Challenges of SEO with Data

1.1 Agents of change in SEO

1.2 The Pillars of SEO Strategy

1.3 Installing Python

1.4 Using Python for SEO

Chapter 2: Keyword Research

2.1 Data Sources

2.2 Google Search Console

2.4 Google Trends

2.5 Google Suggest

2.6 Competitor Analytics

2.7 SERPs

Chapter 3: Technical

3.1 Improving CTRs

3.2 Allocate keywords to pages based on the copy

3.3 Allocating parent nodes to the orphaned URLs

3.4 Improve interlinking based on copy

3.5 Automate Technical Audits

Chapter 4: Content & UX

4.1 Content that best satisfies the user query

4.2 Splitting and merging URLs

4.3 Content Strategy: Planning landing page content

Chapter 5: Authority

5.1 A little SEO history

5.1 The source of authority

5.2 Finding good links

Chapter 6: Competitors

6.1 Defining the problem

6.2 Data Strategy

6.3 Data Sources

6.4 Selecting Your Competitors

6.5 Get Features

6.6 Explore, Clean and Transform

6.7 Modelling The SERPS

6.8 Evaluating your Model

6.9 Activation

Chapter 7: Experiments

7.1 How experiments fit into the SEO process

7.2 Generating Hypotheses

7.3 Experiment Design

7.4 Running your experiment

7.5 Experiment Evaluation

Chapter 8: Dashboards

8.1 Use a Data Layer

8.2 Extract, Transform and Load (ETL)

8.3 Transform

8.4 Querying the Data Warehouse (DW)

8.5 Visualization

8.6 Making Future Forecasts

Chapter 9: Site Migrations and Relaunches

9.1 Data sources

9.2 Establishing the Impact

9.3 Segmenting the URLs

9.4 Legacy Site URLs

9.5 Priority

9.6 Roadmap

Chapter 10: Google Updates

10.1 Data sources

10.2 Winners and Losers

10.3 Quantifying the Impact

10.4 Search Intent

10.5 Unique URLs

10.6 Recommendations

Chapter 11: The Future of SEO

11.1 Automation

11.2 Your journey to SEO science

11.3 Suggest resources

Appendix: Code

Glossary

Index