Google Data Analytics Professional Certificate: My Review and Study Notes

I followed a Youtuber “Alex The Analyst”, he broadcasted the news that Google released a new certificate for data analysts, I am curious about it and since it’s Easters Holiday and mid-term break, I took some time and quickly went through the courses Google had released on Coursera, course link: https://www.coursera.org/professional-certificates/google-data-analytics?Here are some key points about this course/certificate in my opinion:

My Review:

1. Super beginner-friendly, might be too basic that it is not suitable for people who already had relevant background knowledge or hands-on experience
2. Bring out the framework or process of data analysis in a very systematically way
3. Also teach communication skills, presentation skills, and other work-related activities like creating a Linkedin profile, which seems to be oriented to fresh graduates.
4. The capstone project-case study is attractive, however, it is not released yet, it says enrolled starts April but we don’t know the exact date. I am looking forward to it because the basic concepts, methods, and tools the course introduces are actually not difficult to learn, the difficult part is how to apply all into a project, to actually get some insight from a dataset.
5. Google suggests spending 10 hours per week for 6 months, I think it is flexible, if you understand the concept you can just take the quiz and move on. Google also sets a fast path if you pass the evaluation quiz at the beginning of sub-courses.

My Study Notes brief version:

1. Foundations: Data, Data, Everywhere

this part introduces the data analysis field, what I like most is the process of doing any analysis: Ask, Prepare, Process, Analyze, Share.

and it also tells spreadsheet, SQL, R/pythom, Viz tools like Tableau are the tools we need.

2. Ask Questions to Make Data-Driven Decisions

for people who have programming or hands-on experience, you might find this part the most important one since everything starts with the question. Without the questions we don’t know what to look at, leave alone analyzing.

Here is a picture nicely describe what data analysis try to do(I forgot where is it in the course, and searched for a similar one):

3. Prepare Data for Exploration

about how to get the data we want, and a big take away for me is organize data in folders and don’t forget to backup

4. Process Data from Dirty to Clean

teach what kind of data we need to clean, like duplicated, outdated, inconsistent, incomplete data.

5. Analyze Data to Answer Questions

aggregation and calculations are the key points, the course introduces basic ideas and technique of using Excel and SQL

6. Share Data Through the Art of Visualization

learns to use Tableau, there are tons of resources on Tableau's official site/ Tableau Public, and Tableau also has related certificates.

looking forward to the Google Data Analytics Capstone: Complete a Case Study, I prefer python but I will also study Data Analysis with R Programming. In a word, concept-wise learning which question to ask before analyze is important, operation-wise learning SQL, Python/R and Tableau is crucial. Finally, I don’t think going through the course will guarantee a job, but the case study or some similar side projects will be a great help.