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30 November 2021

## “Is the Google Data Analytics Professional Certificate any good?”

Whether you’re a job seeker looking to boost your career or a hiring manager eager to find skilled staff, this is a pertinent question.

In this review, I assess the certificate based on the syllabus, and not the course content itself i.e. I have not completed any of the course work :). I’m interested in what I can learn about the course as someone who might hire an analyst with these credentials, or want to recommend it to someone entering the broad field of Data Science.

## tl;dr

I was pleasantly surprised. The course material looks comprehensive, provides nuance and introduces about all one could expect one course to deliver at a reasonable price and time commitment. My only wish is that it covered how to get oneself unstuck when facing any challenges with software and coding.

## Where we are now: a job market paradox

The job market for new Data Analysts (and to some degree Data Scientists) seems very paradoxical to me. My perception is that:

1. There’s an ever increasing demand for data skills that I think will be around for a very long time indeed.
2. It’s quite challenging to fill these roles and retain people.
3. New data analysts have trouble finding work.

This is not backed up by extensive data or research on my part, it’s just my experience and that of others from the past few years.

I understand there’s a few things that need to be in place for this supply of data professionals to meet the demand for them, both from the people looking for work and from those doing the hiring. One thing that can help is a way to train Data Analysts with the skills they need and provide a certification that employers can recognise.

## Enter the Google Data Analytics Professional Certificate

Google launched it’s Data Analytics Professional Certificate in March of 2021. When I saw the run up to it I was quite intrigued. When Google does something related to data it’s worth a look in my opinion. A quick glance of the syllabus revealed that the main tools they cover are spreadsheets, Tableau and R (all my skills, so I already felt a bit buoyed by that) and SQL (not my skillset but a staple for many Data Analyst roles). The course also goes beyond the technical skills and looks more holistically at how to think with an analytical mindset, work with others and communicate. At a high level, these are signals that the Certification has good potential.

Since it’s launch, I’ve seen many positive reviews of it by students. There are also many people in forums such as the r/DataAnalysis subreddit that ask whether the course is worth it, whether they’ll land a job, or at the very least develop the skills they’ll need for data analysis.

The Certification is quite extensive “with over 180 hours of instruction and hundreds of practice-based assessments” spread across 8 courses. It has a capstone project. At the time of writing, it boasts over 450 000 enrolments and over 30 000 ratings with an average of 4.8 stars out of 5.

### Enrolment and completion

At the time of writing (December 2021) the completion rate seems to me fairly low, if we use the number of ratings of each of the 8 courses as a proxy for how many students complete and rate the course. I haven’t taken a Coursera course before, but I assume they are like most platforms and quite aggressively ask for student ratings.

The course boasts 456k enrolments, but Course 1 of 8 only has 22k ratings – that’s less than 5% “conversion”. It makes sense that there’s an initial large drop-off because its free to enrol but after just 7 days one needs to start paying $39 per month for subscription, so the actual commitment to continue with Course 1 is a more true reflection of real enrolment. Showing the number of enrolments is a double-edged sword. It signals a lot of “social proof” and students and hiring managers might think “Wow, so many people took this, it must be good”. On the other hand, I can also imagine it’s a real psychological blow for new entrants who may think “Oh no, the job market is saturated”. This is especially damaging if they’ve heard that the field is saturated and getting this Certification is meant to help you stand out in the job market, in addition to obtaining the requisite skills for that first role. As we’ll see however, it’s very unlikely that many people hold this Certification. So, Course 1 has 22k ratings and then we see a steep fall in ratings to 7k for Course 2 and all the way down to 1.7k for Course 8. The final course has just 7% of the initial courses rating. There’ll be many reasons for this, but the difference did strike me as quite substantial. My takeaway is “don’t worry, there’s not a saturation of Google Data Analytics Professional Certifications.” And what would a saturation be anyway? If we assume that Data Analysts create value, then hundreds of thousands of people trained in data analysis would be a huge boost to the economy! But I digress… ## The context of my review The course is developed by Google, one of the largest tech companies in the world. I assume what they teach and ask for is very relevant for them and other big tech firms – however I have no experience working in those organisations My interest is the more general situation where many hundreds of thousands (or millions) of companies are. A few hundred to a few thousand employees and the organisation needs people with skills to work with data. My assessment on whether this is a good or bad Certification centres around questions like: 1. How much of the full data/analysis pipeline are they exposed to? 2. How much of their own analytical thinking is developed? 3. Do they learn how to learn? I think we can’t expect too much from any course that’s teaching these skills, especially to someone just entering it (unless the fees are exorbitant!) . The field of Data Science, and the role of Data Analyst – is so broad and diverse that it’ll be easy to provide criticism if the material doesn’t fit your view of what’s important. We must acknowledge that even working professionals, decades into their career – can be on completely different paths and largely disagree on what an introductory course should cover. If Google is able to produce a course that most people can say does give some value to newcomers, then I think they’ll have done well. ## Syllabus overview The Professional Certificate comprises 8 courses. For this review I’ve mostly looked at the course overview and then dove a bit deeper into each courses’ syllabus. 1. Foundations: Data, Data, Everywhere: Introduces concepts and terminology, key terms and awareness of tools and skillsets. What a day in the role of a Data Analyst looks like, how it fits into the broader data ecosystem and what job opportunities there are. I like that this intro sets the student up for what the end state might look like for them when they’re working in the role. Introducing the data ecosystem is good too – shows there’s other ways to make a career out of data as well. 2. Ask Questions to Make Data-Driven Decisions: Teaches about how to think about analyses, how to ask questions effectively and how decisions are made using the results of analyses. This course also introduces the use of spreadsheets in analysis. Really good that they’re teaching about how to think with an analytical mindset. Very promising. 3. Prepare Data for Exploration: Builds upon previous material for spreadsheet skills and introduces SQL. Covers how analysts decide what data to use, differences between structured and unstructured data, data types and formats. Open data, ethics and privacy are touched upon. Learn how databases access, filter and sort the data they contain. Also covers how to organise data and store it securely. This course uses BigQuery for the database instruction. Seems a solid first step into the more meaty part of the course. I’m glad they cover ethics and hope they equip students with some practical resources for learning more about this or more importantly, how to deal with ethical dilemmas (or at least, to let them know that they will need to deal with them in their careers). 4. Process Data from Dirty to Clean: Covers data cleaning techniques using both spreadsheets and SQL, introduces SQL queries and functions. Teaches how to deliver a good data cleaning report, and how to check data integrity. This is the third course covering spreadsheets and the second covering SQL, so potential that the students are getting really good exposure to both by this point. Very good that concepts like data integrity are being taught. You can’t just trust that the data is good! Do they also teach a bit about what documentation to look for (like data dictionaries), what to do when you don’t find them – or at least prepare analysts that sometimes a large part of the job is figuring out what the fields are. Do they cover what causes data issues in the first place and why they just can’t trust that data is “correct”? 5. Analyze Data to Answer Questions: Further use of spreadsheets and SQL, but now utilising more complex formulas and queries. How to prep data for analysis, how to aggregate data. Further repetition of spreadsheets and SQL – very good sign that students will be getting more comfortable with the tools. Can they really be “complex formulas”? Bit sceptical of the use of “complex” vs “powerful”. Not that there’s less value on these not actually being complex but hopefully not giving the students the wrong idea of complex = valuable. 6. Share Data Through the Art of Visualization: Introduces visualisation, Tableau, how to make dashboards and effective presentations. How to consider the limitations of your analysis and how to deal with audience questions. They use the free version of Tableau which is great as there’s no hidden expense for students. Great point to introduce a Business Intelligence tool. They introduce principles of good visualisations and storytelling. Interesting that they chose Tableau rather than PowerBI. Not sure what the US market is like but I suspect they may not have been able to teach PowerBI because it is a direct competitor (Microsoft) product, even though it’s quite likely (if the Dutch job market is anything to go by) that PowerBI will be the most likely tool they’ll use. Glad they went for Tableau rather than say, Google Data Studio – that would have made me sceptical that the whole course is maybe not really got students’ interest at heart. I wonder if there was a lot of internal debate over this choice. Tableau is good reference for learning PowerBI quite quickly if the student needs it. 7. Data Analysis with R Programming: Introduces the use of R, RStudio and R packages. Covers the full pipeline from cleaning to organising, analysing and visualizing data. Looks to be a Tidyverse-centric approach and touches on RMarkdown too. R and RStudio are open source and free to use, so again its great that students don’t have to pay for additional software. Interestingly they also cover the Python vs R debate, so I might enrol just to see what they say! PS I love Python too don’t worry ;). Glad they’re using R (I’m a huge fan of it) Teaches the Tidyverse – I believe this is the easiest way to learn (and use) R, at least for this type of analysis. Not seen any mention of how to google problems and become unstuck (ironically). It’s one of the most valuable skills across any of these tools. 8. Google Data Analytics Capstone: Complete a Case Study: This is an optional module, includes identifying and developing a case study, job hunting instruction, interview training and how to build a portfolio. Good way to help a new analyst put everything together. Helps with how to actually look for work and how interviews might be conducted. Is optional, so if you’re evaluating a candidate who has completed this, don’t assume they’ve also done a case study. Doesn’t appear to be guided nor any feedback given on the analysis, or other support. (Makes sense given the price point of the course), BUT hopefully there’s some options given of how one might assess one’s own development. There is a discussion forum mentioned in Course 1, so hopefully that’s somewhere the students can get feedback. ### Structure of each course Diving a bit deeper into the syllabi of the courses, I can see quite a good mix of videos, exercises, quizzes and readings. Videos tend to be very short at just a few minutes each, whereas time allocated for readings are usually longer at around 10 to 20 minutes a piece. There’s also checklists, learning logs, course challenges and self-reflections. Courses are peppered with introductions into different types of analytic roles, presumably presented by people with those roles. I like that this will broaden students’ horizons when job hunting. The content ratings of each one also seem generally very high. ## Verdict: Very good but with just one missed opportunity Overall this looks like quite a comprehensive introduction to data analytics. It covers a lot of ground for someone new to the role. Provided the content is at least of ok quality, I think there’s enough here that the skills developed will be useful for that first job and the student will be equipped with enough awareness of other tools to figure out which one to use where, or at least have awareness of what their colleagues are talking about. It’s fantastic that the course covers how to think like an analyst, ask questions and interact with stakeholders. This is what gives it that extra credibility for me versus a certification that focussed solely on the technical tooling. While I was reviewing this and especially when looking at more detail, I found that I was becoming more and more impressed by the topics covered. The course covers not just the basics, but seeks to add nuance, caveats and gotchas as well. The type of things you might discuss with a new colleague over a casual chat – there seems to be a bit of mentorship built into the material. The only thing that appears to be missing is teaching students how to become unstuck when they face a technical problem. This would warrant a whole course in my opinion, or at least a big chunk of one of the existing ones. There are so many tools and technologies out there, and I fear that new entrants to the field feel overwhelmed by the amount of stuff on the ever growing “to learn ” list, even though in practice you are likely to use just a handful of things a lot and the rest very little or not at all – and which you use depends entirely on the specific job and task at hand. I think there’s still a lot of stigma around “googling the solution” for many people, but ANY tech professional will tell you this is something they do all the time. How to format your question, knowing what to google in the first place, evaluating potential solutions, using the process of elimination to slowly get unstuck and move forward – super super important skills. A new Data Analyst will develop these skills over time, but its really a missed opportunity to equip students with a skill to empower themselves this way. There’s no coverage of statistics in any form that I could see. Now, what’s needed for a data analyst position is debatable (even my own stats knowledge is pretty limited) but I would hope that the difference between a mean and median is quite a basic one to cover – but I’ll reserve too much comment here – surely in 180 hours of material it’ll be mentioned somewhere :). ## In conclusion I went into the review with an interested but neutral (maybe even a bit professionally sceptical )stance on it and I’ll admit I have been pleasantly surprised. The amount of students I’ve seen who recommend it to each other is what convinced me to have this more detailed look myself. I am very interested to hear from hiring managers of their experience hiring alumni of this Certification. I think this is a valuable addition to the ecosystem and has the potential to be a good onramp for many people to enter the industry. The cost is by no means negligible for many people, but I think at$39/m provides fantastic value just considering the breadth of topics this covers.

I’m looking forward to seeing this develop and hopefully it means we’ll soon have a whole lot more people in the world with the skills to analyse data!