An honest review from a recently certified data student

Introduction

I recently took and completed the 5 main courses which make up the Data Engineering with Google Cloud Professional Certificateoffered by GCP on Coursera (link to certificate here)

I have been wanting to up-skill on Cloud Technologies during the 2020 Covid Pandemic and thus jumped at the opportunity to take this series of courses when I discovered that GCP put out a massive catalog on the famous e-learning platform.

Overall Experience

Taking the Certificate has been definitely an overall positive experience, gave me a solid conceptual understanding of a good portion of the GCP data stack and allowed me to get my hands dirty with personal projects about which I wrote a related piece (How to automate financial data collection with Python using APIs and Google Cloud).

Despite this, I feel like the real aim of the entire curriculum and structure of the courses was more around marketing the GCP suite to prospective corporate clients rather than fostering in-depth student understanding of the tool and its potential applications.

I think this is perfectly fine for an organization to do, and in fact there are many software companies out there leveraging their training programs to push adoption and corporate partnerships. I have indeed been taking courses from the likes of AWS, Tableau and Alteryx, to name some very well known providers in the analytics space, which are all companies naturally trying to increase their tools’ market share and use training programs and certification efforts as an additional way towards that goal.

With the GCP Coursera catalog however, I felt slightly taken aback at the fact that I often perceived this overarching marketing effort sneaking in from the back door and taking away depth, content and learning focus from an otherwise amazing curriculum.

This opinion was further substantiated when a colleague who was taking the Machine Learning with TensorFlow on Google Cloud Platform Specialization** (**also part of GCP’s Coursera catalog) expressed similar complaints regarding the courses he was covering.

An important aspect to highlight is that most of a student’s perception of the quality and level of enjoyment deriving from an online course depends on mainly two factors:

1. The student’s level of technical competence in the subject prior to taking the course
2. The student’s intrinsic motivation for taking the course

With this in mind, there is also the possibility that I did not fit the mold of the student best positioned to take advantage of the learning resources and get the maximum level of knowledge and enjoyment out of the course. In this sense, I was not the accomplished AWS Data Engineer trying to add GCP to the list of cloud providers I can work with, and thus perhaps I did not gloss over many otherwise introductory-level lessons and labs in which I found the above-mentioned marketing efforts to be a little too intruding.

Wanting you to judge for yourself, I have summarized below what to expect if you decide to take GCP’s Data Engineering Professional Certificate on Coursera, to enable you to take a decision of whether to proceed with the time and investment (39£/month subscription until completion- prices may vary depending on your location)

Structure and Course Topics

The Data Engineering with GCP Professional Certificate is structured across 6 courses of 2 weeks (indicative duration). The course list is the following:

1. Google Cloud Platform Big Data and Machine Learning Fundamentals

**2. **Modernizing Data Lakes and Data Warehouses with GCP

**3. **Building Batch Data Pipelines on GCP

**4. **Building Resilient Streaming Analytics Systems on GCP

**5. **Smart Analytics, Machine Learning, and AI on GCP

**6. **Preparing for the Google Cloud Professional Data Engineer Exam

As prerequisites, the team at Google recommends having “roughly one (1) year of experience with one or more of the following:

• A common query language such as SQL
• Extract, transform, load activities
• Data modeling
• Machine learning and/or statistics
• Programming in Python”

In hindsight, it is probably fair to say you should be able to follow and complete the entire curriculum even if in possession of a high level view of all of the above points, as you will not be conducting in-depth Machine Learning or Modelling as you go along. It is indeed helpful to have knowledge around these concepts to better comprehend how GCP can enable you to use its stack and services to serve the purpose of your project, and to compare their services to other major cloud providers on the market.

Overall, the Professional Certificate is classified as being at an Intermediate level series on Coursera, which I think is about right here.

#education #coursera #data-engineering #google-cloud-platform #data analysis