Google Data Analytics Certificate: Time Commitment + 10-Week Study Plan [Realistic Breakdown]

I put off taking the Google data analytics certificate because I thought it would take forever… in this article I’ll share what I learned so you can plan your time.

So, how long does Google data analytics certificate take?

First, we should acknowledge that the course is advertised as around 140+ hours for the mandatory modules, but my experience and other students’ experience show that completion time varies.

How long it’s going to take you is determined by:

• Your previous data analytics experience (I had none)
• Your willingness to put the time in
• How comfortable you feel with the tools, and how quickly you pick up the concepts
• Your learning style and preferences – people who take a lot of personal notes are going to probably learn more comprehensively, but it will take longer.

Having said all of that, the most important factor is your motivation: why are you doing this course? If it’s simply to tick a box, you can scan through the materials. If you are doing it because you really want to get an analytics job, you’ll need to truly learn and therefore spend more time on the concepts and assignments.

Not sure if the course is right for you? Read my full Google Data Analytics Certificate review.

Google Data Analytics Certificate: Time estimates from Coursera

The published Google data analytics time commitment recommendation is that you should spend 10 hours per week on learning which means it will take you around 6 months to finish the materials.

The timeframes given by Google (and Coursera) for each module are:

1. Foundations: data, data, everywhere: 12 hours
2. Ask questions to make data-driven decisions: 15 hours
3. Prepare data for exploration: 19 hours
4. Process data from dirty to clean: 20 hours
5. Analyze data to answer questions: 26 hours
6. Share data through the art of visualization: 18 hours
7. Data analysis with R programming: 31 hours
8. Capstone project (case study): 11 hours
9. Accelerate your job search with AI: 6 hours

Don’t worry – you can complete it much faster. Lots of students report completing it in 2-3 months. If you can spare a couple of evenings a week or a full weekend day, you can get through the material sooner.

Realistic completion time for the Google Data Analytics course

I found that I was much faster with the Foundations module than any of the others because it covers ‘corporate’ stuff that I found easy to pick up. I got through the first module of that in a couple of hours one Saturday afternoon, and that included going through all the ‘general’ course readiness introductory pieces as well.

The data analysis with R module includes learning new tools, so the recommendation is right that it will take longer than any of the others.

10-Week Google Data Analytics study plan

Here’s a Google Data Analytics study plan that you can use as a starting point for your own learning.

• Week 1: Foundations
• Week 2: Ask questions
• Week 3: Prepare data
• Weeks 4-5: Process data
• Weeks 6-7: Analyze data
• Week 8: Share data with visualization
• Weeks 9-10: R programming

This Coursera data analytics weekly schedule doesn’t include time recommendations for the Capstone or the AI job search courses as these are optional. If you do want to do them, the Capstone could take 2-4 weeks. The AI job search course you can complete in an afternoon.

Each course has a different number of modules.

Foundations has 4 modules. You’ll get through this course the quickest and it gives you a chance to get into the studying habit.

Ask questions has 4 modules, with module 4 being one on stakeholder relationships so that’s a relatively ‘light’ module.

Prepare data has 5 modules. Module 4 and 5 are short, but the database essentials module is time-consuming.

Process data is 6 modules. Module 5 is optional as it’s about adding data to your resume – which may or may not be relevant. You can always come back and do it later. Not completing that module won’t affect your pass mark for the course. The final module is a course wrap up which you can scan through quickly.

Analyze data is 4 modules but this is a sizeable course so it’s worth allowing a couple of weeks. If you do it faster, great.

Sharing data is 4 modules, with module 4 being about creating slide presentations. If you are already experienced at using slides and building out stories in presentation format, you will be able to get through this one quite quickly.

R programming is 5 modules. There is a lot of new things in here. While the course builds on what you have learned in other courses, you’ll be programming in RStudio, learning R Markdown, creating visualizations and exporting them.

If you already have R experience or pick up this kind of tool quickly, you might be able to do it faster, but it is not something I have previous experience of.

How to finish the Google Data Analytics Certificate faster

OK, let me share some tips for how to complete the Professional Certificate faster. However, be aware that whizzing through the material does not equal learning!

If you want to recall the concepts and be able to talk about it at an interview, learn at your own pace.

• Watch videos at 1.5x or 1.75x speed.
• Read the transcripts instead of watching the videos at all (skip to the end of the video with the scrub bar to mark it as complete)
• Focus more time on hands-on labs and assignments – this is the really valuable stuff
• Batch small modules into study sprints
• Use the mobile app for flexibility so you can watch videos on the move
• Complete your data journal as you go – it does help.

You’ll also complete it faster if you don’t do the Capstone or the AI job search module. Having said that, you get the certificate whether you do those courses or not, so it won’t make it faster really – it will just feel faster!

Is the Google Data Analytics certificate worth the time?

Even part-time study makes progress. I watch Coursera videos while at the gym (which is not often!!) or while having breakfast. You can fit them in as they aren’t long.

You’ll be learning new skills throughout the course, and you can put those into practice immediately in your job. You don’t have to wait until you complete the whole certificate before you use them or talk to your employer about your career preferences.

Read next: What employers think about job seekers with Google Professional Certificates

I asked a number of employers about whether they rate this training and the results were broadly positive. Jared Bauman, CEO of digital agency 201 Creative, LLC, summed it up.

“We’ve interviewed and hired candidates who listed Google Professional Certificates, particularly in Digital Marketing and Project Management,” he said. “While the certificate alone is not enough to guarantee a hire, it signals that the candidate has taken initiative and completed focused, relevant training. It often helps them stand out in a crowded applicant pool, especially when they lack formal experience. We view it as a strong supplemental credential.”

Want to start today?

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Start the Google Data Analytics Certificate on Coursera today and learn at your own pace. No experience required!