Our Favorite Posts Of Last Week (Sep 08, 2019)
SEO Data Analysis
crawldata_colnames=['date', 'crawled_pages'] linkdata_colnames= ['links','date'] cd = pd.read_csv("crawl.csv",sep='\s',parse_dates=['date'], index_col='date', usecols=[*range(0,2)], names=crawldata_colnames, skiprows=1,header=None) gad = pd.read_csv("google_analytics_data.
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SEO data distribution analysis
Before analyzing the distribution of SEO data, it is best to talk briefly about what SEO data is. There are many sources of SEO data, but two are essential. One is data collected from websites crawls, the other one is data collected through web server logs of websites.
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An Overview of Topics Extraction in Python with Latent Dirichlet Allocation
A recurring subject in NLP is to understand large corpus of texts through topics extraction. Whether you analyze users’ online reviews, products’ descriptions, or text entered in search bars, understanding key topics will always come in handy.
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Hacking The Topic Graph with Wikipedia and the Google Language API
One of my favorite slide decks from the last ten years was done by Mark Johnstone in 2014, while he was still with Distilled. The deck was called How to Produce Better Content Ideas and I used it as my bible for a few years while building teams to do the hard work of content promotion.
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