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Data-Driven SEO with Python Andreas Voniatis

Data-Driven SEO with Python von Andreas Voniatis

Data-Driven SEO with Python Andreas Voniatis

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Data-Driven SEO with Python Zusammenfassung

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

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.

Über Andreas Voniatis

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.

Inhaltsverzeichnis

Data Driven SEO with Python
Chapter 1: Meeting the Challenges of SEO with Data1.1 Agents of change in SEO1.2 The Pillars of SEO Strategy1.3 Installing Python1.4 Using Python for SEOChapter 2: Keyword Research
2.1 Data Sources2.2 Google Search Console2.4 Google Trends2.5 Google Suggest2.6 Competitor Analytics2.7 SERPsChapter 3: Technical
3.1 Improving CTRs3.2 Allocate keywords to pages based on the copy3.3 Allocating parent nodes to the orphaned URLs3.4 Improve interlinking based on copy3.5 Automate Technical AuditsChapter 4: Content & UX
4.1 Content that best satisfies the user query4.2 Splitting and merging URLs4.3 Content Strategy: Planning landing page content Chapter 5: Authority
5.1 A little SEO history5.1 The source of authority5.2 Finding good linksChapter 6: Competitors
6.1 Defining the problem6.2 Data Strategy6.3 Data Sources6.4 Selecting Your Competitors6.5 Get Features6.6 Explore, Clean and Transform6.7 Modelling The SERPS6.8 Evaluating your Model6.9 ActivationChapter 7: Experiments
7.1 How experiments fit into the SEO process7.2 Generating Hypotheses7.3 Experiment Design7.4 Running your experiment7.5 Experiment EvaluationChapter 8: Dashboards
8.1 Use a Data Layer8.2 Extract, Transform and Load (ETL)8.3 Transform8.4 Querying the Data Warehouse (DW)8.5 Visualization8.6 Making Future ForecastsChapter 9: Site Migrations and Relaunches
9.1 Data sources9.2 Establishing the Impact9.3 Segmenting the URLs9.4 Legacy Site URLs9.5 Priority9.6 RoadmapChapter 10: Google Updates
10.1 Data sources10.2 Winners and Losers10.3 Quantifying the Impact10.4 Search Intent10.5 Unique URLs10.6 RecommendationsChapter 11: The Future of SEO
11.1 Automation11.2 Your journey to SEO science11.3 Suggest resourcesAppendix: Code
Glossary
Index

Zusätzliche Informationen

GOR013456408
9781484291740
1484291743
Data-Driven SEO with Python: Solve SEO Challenges with Data Science Using Python Andreas Voniatis
Andreas Voniatis
Gebraucht - Sehr Gut
Broschiert
APress
20230325
580
N/A
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