If you are a data professional looking to gain a good understanding of the big data, machine learning and data engineering landscape on Google Cloud, this guide explains the learning options available to you and offers many practical tips to help you accomplish your learning goals.
1. Overview of the relevant learning options
2. Why to consider the Data Engineering specialisation
3. What the material covers
4. Review: what’s good/what’s missing/what are the highlights
5. Practical tips for the specialisation
6. Next steps after completing it
7. The Certification Exam
Read the article on Medium
Earlier this week I had the pleasure to be a guest speaker on Hailey Friedman's (Improvado.io) webinar for digital marketers and marketing analysts.
The topic was about advanced marketing analytics and I had the opportunity to talk about some of the key marketing analytics techniques including:
The recording of the webinar is available via this link
The slides are available on slideshare:
Marketplaces for professional services have been increasing in both size and importance over the last few years. Covid-19 new realities are likely to generate more demand for such services. Engaging in such marketplaces involves both risk and opportunity for both participating sides. In this webinar conversation with James Sandoval and Helen Tanner we share views on how risks can be mitigated and opportunities maximised within such an environment.
The recording is now available and is part of Market Risk 's professional masters program.
Kaggle, the ML competitions platform, is turning into a general-purpose data science and analytics tool. While you can't expect to have it all from a free tool, there is fair number of use cases that it can handle reasonably well, including some related to marketing analytics. In this post, I cover these use cases by applying specific examples in the field of marketing.
Published on the ConversionXL blog
Digital analytics Thessaloniki is one of the most popular and active tech meetups in the city. I had the opportunity to share my experience of taking part in the Kaggle competition with Google Analytics data. The focus of this talk wasn't so much the competition itself but rather all the benefits that a digital analyst can get from using Kaggle. These days, Kaggle doesn't mean only competition, there is much more involved.
v2.0 of this talk was presented in MeasureCamp London
v3.0 presented in Measurecamp Dublin
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:
My blogpost for the Sept '18 London MeasureCamp event.
In this blog post, we will look exclusively at choosing between R and Python from the perspective of a digital analyst. We will consider the workflows and the 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:
"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.
Exploring the Meaning of AI, Data Science and Machine Learning with the latest Wikipedia Clickstream
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 surfaced.
Originally published on medium for the towards data science magazine.
Please follow the link below:
In this article, I cover all the steps necessary for Google Analytics data to be ready for data science exploration and modelling.
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.
This talk provides an overview of clickstream analysis and presents an example of using Markov chains to model the clickstream and to provide transition probabilities from one page to another. It also demonstrates 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 to 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 using Google Analytics data as input.
Originally published on LinkedIn Pulse.
My thesis explores the 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 a literature review around the theme of predicting consumer behaviour online.
Link to thesis on slideshare
During the study, I also wrote a paper titled "The impact of search ads on organic search traffic using nonparametric statistics and time series analysis", which is available on SlideShare (note: this is personal work for college classes and it is not peer-reviewed nor published).
From niche bloggers to multinational corporations, everyone is 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 as well as 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 are web analytics? How do they work?
• Interfacing with the Analytics Reporting API via an R package (RGA).
• Practical analytics applications with R.
A paper based on my master's thesis for the 44th Hawaii International Conference on System Sciences. The study examines the impact of social media on search engine rankings in the hotel sector.
Link to the academic paper - slideshare