ALEXANDROS PAPAGEORGIOU

My takeaways from taking part in the 1st Kaggle competition having Google Analytics data as raw material. 


Published on medium, with the Innovation Machine magazine:

medium.com/innovation-machine/google-analytics-kaggle-competition-highlights-eaa046737ac4?linkId=61016679

My blogpost for the Sept '18 London MeasureCamp event. 

In this blog post we will exclusively look at the question from the perspective of a digital analyst. We will consider the workflows and types of tasks that are typically involved in this field. Of course, digital analysts can serve different roles, so we will look at a couple of different scenarios.

​Please follow the link below to read the article:
​
​https://medium.com/innovation-machine/choosing-between-r-and-python-a-digital-analysts-guide-b7103f80aa4e

​https://www.meetup.com/Data-Science-and-Engineering-Club/events/250803802/

"Growth Analytics: Evolution, Community and Tools" with emphasis on Google Analytics (and its API), including examples of how web analysts and data scientists can use this rich source of data for analysis and applications. 
https://www.meetup.com/Customer-Analytics-Dublin-Meetup/events/250809233/

Can we use data and analytical methods to capture the meaning and semantic context of these terms ? Thanks to the recently open sourced wikipedia clickstream dataset and network analysis tools, some interesting associations between the terms are surfaced. 
Originally published on medium for the towards data science magazine.
Please follow the link below:

https://towardsdatascience.com/exploring-the-meaning-of-ai-data-science-and-machine-learning-with-the-latest-wikipedia-5fea5f0a2d46

In this article I cover all steps necessary for Google Analytics data to be ready for data science exploration and modeling. 
The goal is to propose a framework that serves as a guide, especially for analysts exploring new ways to perform analytical work with their GA data.
Originally published on linkedin pulse. 

https://www.linkedin.com/pulse/5-steps-get-google-analytics-ready-data-science-papageorgiou/ 

​This talk provides an overview of clickstream analysis and presents an example of using Markov chains to model the clickstream and provide transition probabilities from any page to any other. It also shows how to predict the user's next click given the sequence of previous page requests. 

​This talk proposes ways in which analysts can work with the Google Analytics API methodologically, from accessing the data to selecting the right metrics and dimensions and implementing those algorithms which tend to play nicely with Google Analytics data. 

This article summarises recent developments in the Google Analytics landscape that enable working with data mining and machine algorithms with Google Analytics data as input.
Originally published on linkedin pulse. 

https://www.linkedin.com/pulse/why-data-science-google-analytics-now-becoming-papageorgiou/

My thesis explores ways in which google analytics can be used as a data sources on top of which applications of statistical analysis and data mining can be developed. 
It includes literature review around the theme of predicting consumer behaviour online. 

You can find some other work in this area  on slideshare (note that this is personal work for college classes and it is not peer-reviewed nor published) 

​Niche bloggers up to multinational corporations, they are all interested in monitoring their web traffic and its patterns across time. 
Google Analytics is the most widely used solution to keep track of this type of data. It provides a UI for a wide range of reports and possibilities for various types of visualizations. 
Moreover, the availability of the Analytics API coupled with the corresponding R packages can now give more options for custom web analyses. 

The talk covers the following : 

• What is web analytics ? How it works ? 

• Interfacing with the Analytics Reporting API via an R package (RGA) 

• Practical analytics applications with R 

Paper based on my master's thesis for the 44th Hawaii International Conference on System Sciences ​. The study examines the impact of social media for search engine rankings in the hotels sector.