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Data Mining and Scraping

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Digital Marketing - Study Notes:

What is data mining?

Data mining is the process of analyzing data from different perspectives and summarizing it into useful information.

It is primarily used by companies with a strong consumer focus. Think about the traditional sense of what mining is all about. It’s about going deep into the center of something to get something valuable out of it. Similarly, you mine data in order to unearth core insights from it. When thinking about mining, consider these questions:  What is in the data? What’s the data telling me? What is the relationship between one data set and another data set?

As previously mentioned, data mining is primarily used by companies with a strong consumer focus - retail, financial, communication, and marketing organizations, for example.

Data mining enables companies to determine relationships among ‘internal’ data indicators such as price, product positioning, or staff skills, and ‘external’ indicators such as economic data, competition, and customer demographics.

What is data scraping?

Data scraping is a technique in which a computer program extracts, or ‘scrapes’, data from human-readable output coming from another program.

It’s always worth considering:  HTML content; dynamic websites; XPath and selection techniques; and regular Expressions (a language for extracting small bits of text from a larger text element), before and during data scraping.

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Jack Preston

Jack Preston is a Data Scientist working within marketing analytics, with a particular focus on strategic customer loyalty. Jack has experience working in both small-scale startups and large corporates, including dunnhumby and Notonthehighstreet. He also holds an MSc in Business Analytics from UCL where he graduated with distinction.

ABOUT THIS DIGITAL MARKETING MODULE

Analytics
Jack Preston
Skills Expert

This short course covers the principles of analytics and demonstrates techniques and useful tools that you can use to develop and refine your knowledge of data analytics.

You will learn:

  • The fundamentals of data, collecting data, and processing data, including best practices, techniques, and challenges
  • The principles of web analytics, the benefits and limitations of Google Analytics, terminology for reporting, and the legalities around consent and data privacy
  • The concepts of Big Data, the processes around data, including mining, scraping, cleansing, and de-duping, and the various languages and programs for testing your data
  • The importance of AI, Machine Learning, analysis types, the value of testing hypotheses, and forecasting based on the data available
  • How best to report and present data findings to management and the different tools available to you

Approximate learning time: 3 hours