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voniatis andreas - data-driven seo with python

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




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Dettagli

Genere:Libro
Lingua: Inglese
Editore:

Apress

Pubblicazione: 03/2023
Edizione: 1st ed.





Trama

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.





Sommario

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





Autore

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. 











Altre Informazioni

ISBN:

9781484291740

Condizione: Nuovo
Dimensioni: 254 x 178 mm Ø 1141 gr
Formato: Brossura
Illustration Notes:XXVI, 580 p. 410 illus., 102 illus. in color.
Pagine Arabe: 580
Pagine Romane: xxvi


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