| Description |
1 online resource (xxvi, 580 pages) : illustrations (some color) |
| Note |
Includes index. |
| Summary |
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. |
| Contents |
Chapter 1: Meeting the Challenges of SEO with Data -- Chapter 2: Keyword Research -- Chapter 3: Technical -- Chapter 4: Content & UX -- Chapter 5: Authority -- Chapter 6: Competitors -- Chapter 7: Experiments -- Chapter 8: Dashboards -- Chapter 9: Site Migrations and Relaunches -- Chapter 10: Google Updates -- Chapter 11: The Future of SEO -- Chapter 12: Glossary. |
| Subject |
Web search engines.
|
|
Python (Computer program language)
|
|
Moteurs de recherche sur Internet. |
|
Python (Langage de programmation) |
|
Python (Computer program language) |
|
Web search engines |
| Added Author |
Critchlow, Will, writer of foreword.
|
| Other Form: |
Original 1484291743 9781484291740 (OCoLC)1354251004 |
| ISBN |
9781484291757 (electronic bk.) |
|
1484291751 (electronic bk.) |
| Standard No. |
10.1007/978-1-4842-9175-7 doi |
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