What is the Google Data Analytics Specialization?
Just to clarify, the Google Data Analytics specialization I’m referring to is not the same as the tool used to track website information. Rather, it’s an eight-course program available on Coursera, and upon completion, you can earn a certificate. I’ve personally completed all eight courses and am excited to share my opinion and feedback on the program.
For more information regarding the specialization: https://www.coursera.org/professional-certificates/google-data-analytics
Why choose the Google Data Analytics Specialization?
As of today, according to Coursera, there are over 1 million participants in the Google Data Analytics Specialization. This course can be taken by anyone with or without any experience in the data analytics field.
The 8-course specialization aims to teach various data skills such as Data cleaning, Data collection, Data Processing, and Visualization. It also has other sections regarding how to ask the right question using data, how to present your data, etc. These data skills are in high demand and can take your career to the next level. Sometimes knowing even a simple Python script can save you hours of headache by doing things manually, or perhaps the ability to manipulate and extract information in an Excel spreadsheet can increase your efficiency many times over.
Other than data skills, the specialization also offers tips regarding career support, like how to prepare for an interview, and how to write a resume for a data analytics position. These things are not something that many other courses offer.
On Coursera, it says it takes approximately 6 months to finish at 10 hours a week, but it can be done as fast as a couple of weeks. The ability to allow for a flexible pace is nice because not everyone has the same amount of free time to do the course.
What Courses are Included in the Google Data Analytics Specialization?
The following are the 8 courses offered by the Google Data Analytics Specialization.
Foundations: Data, Data, Everywhere
This is mostly the introductory course, it mainly goes through the concept such as data analysis, data handling, etc. More or less just to give a general idea of what to expect in the other upcoming courses.
Ask Questions to Make Data-Driven Decisions
In this course, it focuses on the concept of asking the right questions. Let’s say you are tasked to analyze something, but how would you go about knowing what questions to ask? This is what the course is about, showing the importance of asking the right question before doing any data work.
For example, if an ice cream company wants to know which flavor of ice cream should they make more, what would be a good question to ask? You would not ask people “Do you like ice cream”, but a more specific question with some options such as out of these 10 flavors of ice cream, which flavor of ice cream is the most appetizing to you?
Prepare Data for Exploration
This section of the course is more focused on data handling. Bias in data can happen, and this course goes through some of the downfalls of incorrect data collection and handling.
Process Data from Dirty to Clean
After collecting the data, it is most likely a mess and requires good scrubbing, and this is what this course is trying to show. It talks about how to use SQL to process and clean data, how to use SQL to query a database, and verifying the cleaned data make sure it was done properly.
Analyze Data to Answer Questions
I believe this course is more of an extension to the previous course, it talks about formatting data in SQL and excel. It also shows how to use SQL and excel to compute things like mean, median, and standard deviation.
Share Data Through the Art of Visualization
Ah, finally we get to do some graphing. It is quite fascinating to be able to see the visual of our data. Without graphs, we just have a bunch of numbers that are difficult to decipher. The courses use Tableau online for data visualization, and some tips regarding how to present the visualization.
One thing the course did not do is to visualize the data early. I believe in the early stages of data analysis, it might be better to visualize our data for data distribution, is the data a normal distribution? or does it have multiple peaks? Does our data have left, skew, or is it symmetrical? What does our continuous variable look like? is it linear or curved?
Data Analysis with R Programming
This course is just mainly showing us how R works, it shows how to do visualization with R. However, I believe the main point of this course is to show us how markdown works.
Google Data Analytics Capstone: Complete a Case Study
This is the last course of the specialization, the course itself contains many different topics. First, it has two capstone projects available for the participants. We only need to do one of the two to complete the course.
Check out my capstone project here: https://thecodingmango.com/case-study-google-data-analytics-coursera/
The other part of this course offers some tips regarding interviews for a data analyst position, or how to craft a resume that is tailored for the data analyst position. It also has a job platform with many big companies in it. The platform is only available after finishing the capstone.
My Opinion of the Google Data Analytics Specialization
This section would be my personal two cents on the specialization. Before doing the specialization, I had a limited understanding of data and how to use them. After doing this specialization, I gained more insights into how to handle and process data, especially using SQL. Knowing SQL and some type of scripting language can go a long way in analyzing data. These I believe are in-demand skills, but the truth is that a lot more people know these compared to before. Thus, making it more competitive to advance a career in data analysis.
This specialization is a fantastic way to open the door toward data analysis, it is a good first step, but it is not the last. There are many things in the specialization that are not covered, and I believe a good next step is to further build upon the knowledge gained from this specialization. Other than knowing how to process data, it is good to at least learn some math and statistic to understand the analysis better.
