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Analytics

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Digital Marketing - Study Notes:

Some commonly used practical algorithms include:

  • Linear regression: This is a more basic algorithm in the groups of advanced analytics. It is widely used because people can easily visualize how it is working and how the input data is related to the output data. The goal of linear regression is to identify the relationship in the form of a mathematical formula that describes the dependent variable in terms of the independent variable.
  • Logistic regression: This is focused on categorization of data; its goal is to categorize whether an input variable fits within a category or not. The output of logistic regression is a value between 0 and 1. The closer the result is to 1, the more of a fit the data is in that category
  • Classification and regression trees: These use a decision to categorize data based on questions related to one of the input variables. With each question and corresponding response, the data moves closer to being categorized.
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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.

Jack Preston

ABOUT THIS DIGITAL MARKETING MODULE

Analytics
Jack Preston 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

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