Data-Driven SEO with Python: Solve SEO Challenges with Data Science Using Python

ISBN-13: 9781484291740 / Angielski / Miękka / 2023

Andreas Voniatis

Opis

Solve SEO problems using data science. This hands-on book is packed with Python code and data science techniques to help you generate data-driven recommendations and automate the SEO workload. This book is a practical, modern introduction to data science in the SEO context using Python. With social media, mobile, changing search engine algorithms, and ever-increasing expectations of users for super web experiences, too much data is generated for an SEO professional to make sense of in spreadsheets. For any modern-day SEO professional to succeed, it is relevant to find an alternate solution, and data science equips SEOs to grasp the issue at hand and solve it. From machine learning to Natural Language Processing (NLP) techniques, Data-Driven SEO with Python provides tried and tested techniques with full explanations for solving both everyday and complex SEO problems.This book is ideal for SEO professionals who want to take their industry skills to the next level and enhance their business value, whether they are a new starter or highly experienced in SEO, Python programming, or both. 

What You'll Learn

• See how data science works in the SEO context
• Think about SEO challenges in a data driven way
• Apply the range of data science techniques to solve SEO issues
• Understand site migration and relaunches are

Who This Book Is For

SEO practitioners, either at the department head level or all the way to the new career starter looking to improve their skills. Readers should have basic knowledge of Python to perform tasks like querying an API with some data exploration and visualization.

Spis treści

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

O autorze

Andreas Voniatis is the founder of Artios (https://artios.io/) and a SEO consultant with over 20 year’s experience working with ad agencies (PHD, Havas, Universal Mcann, Mindshare and iProspect), and brands (Amazon EU, Lyst, Trivago, GameSys).  Andreas founded Artios in 2015  – to apply an advanced mathematical approach and cloud AI/Machine Learning to SEO. With a background in SEO, expertise in data science and cloud engineering, Andreas has helped companies gain an edge through data science and automation. His work has been featured in publications worldwide including The Independent, PR Week, Search Engine Watch, Search Engine Journal and Search Engine Land.

Andreas is a qualified accountant, holds a degree in Economics from Leeds University and has specialized in SEO science for over a decade. Andreas helps grow startups and trains enterprise SEO teams with data driven SEO. 

Blurb

Solve SEO problems using data science. This hands-on book is packed with Python code and data science techniques to help you generate data-driven recommendations and automate the SEO workload. 

This book is a practical, modern introduction to data science in the SEO context using Python. With social media, mobile, changing search engine algorithms, and ever-increasing expectations of users for super web experiences, too much data is generated for an SEO professional to make sense of in spreadsheets. For any modern-day SEO professional to succeed, it is relevant to find an alternate solution, and data science equips SEOs to grasp the issue at hand and solve it. From machine learning to Natural Language Processing (NLP) techniques, Data-Driven SEO with Python provides tried and tested techniques with full explanations for solving both everyday and complex SEO problems.

This book is ideal for SEO professionals who want to take their industry skills to the next level and enhance their business value, whether they are a new starter or highly experienced in SEO, Python programming, or both. 

You will:

• See how data science works in the SEO context
• Think about SEO challenges in a data driven way
• Apply the range of data science techniques to solve SEO issues
• Understand site migration and relaunches