Recently I completed Google Data Analytics Professional Certificate Program successfully. I’d like to share how it is.
The program is well designed to prepare for a new career in the high-growth field of data analytics, no experience or degree required. Get professional training designed by Google and have the opportunity to connect with top employers. The entire program (8 courses) is designed for a total of 180 hours including learning material and the learning exercises, therefore, for beginners in order to completed this program that an average of ten hours per week of study is suggested. It can be broken up into 4 days with 2.5 hours of study each day.
· About Me
· Why I signed up for this program
· What I learned from this program
· Final thoughts.
About Me
My name is Kieso Jan. I’ve been working as a data analytics & business process digitalization expert for 13 years in an Automotive Company mainly for HR & Finance & Controlling functions, and before that, I was a software engineer for about 4 years in Banking Software Company. Here is my Linkedin page.
Why I signed up for this program
You might start wondering “why this guy signed up for this program?” well, the true story is just to fulfill my curiosity in the first place on “How Google will teach Data Analytics by what kind of framework & content ? “. Although this program clearly highlighted “for beginners”. with the strong motivation of curiosity I decided to enroll in this course and took it seriously walk through it all without skipping any session. and with over a decade of data analytics experiences, it still took me approx. 2 months (8 weeks, average 10 hours per week) to complete 8 courses including the case study.
Surprisingly, it turns out to be a fantastic learning experience, therefore, I would like to summarize my takeaway after 8 weeks of study.
If you’re looking for program detail information, this article “How to Study for the Google Data Analytics Professional Certificate” which is written by Madison Hunter is helpful.
What I learned from this program
1.Increasing data analytics required toolbox in my pocket:
After 8 weeks by many hands on practice, I found myself very comfortable to use new tools (e.g. Tableau, R, BigQuery, Google cloud base application etc… I’m more into Microsoft base solutions origionally), resulting in I have more options to solve different business tasks with best tool combination.
2. Moving relevant books (knowledge) to bigger bookshelf by a structured approach and tag “Data Analystics Label”:
Our memory is like picture. Many knowledge / memory if you don’t use it, they will eventually faded. Although I have over decade data analytics experiences, I found out through this course, all relevant knowledge can be taken out, repolished again, and tag right label then putting back to right place by the order of The data analytics process: Ask, Prepare, Process, Analyze, Share and Act. Since it repeated over and over again during the course, now it like a “Tongue twister” deeply imprinted in my head. definitely it more easy to access whenever you need it.
3. Building up your own portfolio matters
The biggest takeaway from this course for myself is to know how to fully utilize many online free resources starting to build up my own portfolio including Kaggle, Github, Linkedin, Medium, Tableau Public etc.., these online community is not only providing countless knowledge, resource & environment for further growth , and also providing you a excellent showcase / stage to show your capabilities & thought on data analytics / data science.
Final thoughts.
If you’re a beginner, this course is definitely worth to take. Google not only provides excellent knowledge framework, and provides the support through the whole job preparation process with a resume-building tool, mock interviews, and career networking support to help you get a job after program completion.
And if you’re a experienced data analytics like me hoping my sharing can somehow give your inspirations.
Cat without curiosity won’t survive in Data Analytics & Data Science field, so be curious and keep up your journey in learning data !
