I've been doing online marketing for more than ten years, mainly focusing on advertising campaigns.
Currently focusing on making open source data science tools for online marketing productivity and analysis.
In this tutorial, we will use crawling and scraping to create an influencer database using Python. You can get an interactive version of the tutorial to follow along, modify the code, or later use it as a template.
In this guide, you will learn how to analyze the content of a website across time using their XML sitemap. And, you will learn how to extract categories, URLs, slugs, and authors using a reusable Python template for sitemap analysis, with an interactive notebook. You will also learn how to collect publishing trends by year, month, and week.
Developing and managing SEM campaigns at scale can be difficult, but it doesn‘t have to be. Discover a labor-saving process for building large PPC accounts with multiple products that is fast, flexible, and easily managed.
Textual data are everywhere; social media posts, keywords, URLs, page titles and more. They also come with a lot of numbers that describe them. How we can extract meaning from text data on a large scale? What techniques can we use to quickly figure out important topics, users, or maybe products, in a set of textual data? This is a tutorial that uses data science techniques to solve these questions.