Google’s Data Analytics Certificate Improved My Project in These 3 Ways
This is the fourth article in a series about taking Google’s Data Analytics Certificate. You can read the first article here.
Google’s Data Analytics certificate course is designed for complete beginners. It starts with the very basics and thoroughly teaches everything you need to know to get started in the field. To become an expert, you will likely need to go beyond what is shared in the series. However, it is definitely enough to give you a solid foundation to build upon. While I had some experience with Data Analytics before beginning the Google series, I have learned something from each course I have taken. The third course, “Preparing Data for Exploration”, is no exception.
As I work through the series, I am applying what I learn to a project about how the pandemic affected digital learning platforms in K-12 education. Here are three ways my project was improved by what I learned:
1.) I have included additional data that I might not have used otherwise.
- One of the things I found most exciting about this course was the number of open data resources shared. It was exciting to think about all the different ways the datasets could be explored and what insights could be found. Looking at all this data gave me ideas for my next few projects, and it also inspired me to look for additional sources for my current one. (See the links below for some of the sources I found.) It’s truly incredible how much data is available for free right now!
2.) I can process large sets of data from multiple sources quickly.
- In week three, several lessons involved practicing SQL in a big-data repository called Big Query. Admittedly, there isn’t enough practice to become an expert in SQL. However, it did significantly improve my proficiency with the language. What I learned about primary and foreign keys helped me strengthen my database design. I have also gotten more efficient at writing queries to get the exact data I need and even learned how to perform functions like counting and averaging columns. This has helped me to be able to analyze large amounts of data much more quickly.
3.) I really understand the data I am using. I’ve ensured that it is valid and know how I will use it to answer my questions.
- In this course, there is an emphasis on analyzing data in several ways before you even start cleaning it. One of the first lessons involves deciding how data will be collected. The first step is to determine if you will collect the data yourself or get the data from another source. Because of this lesson, I decided to do a bit of both. I used datasets from a few government websites. However, I also decided to create my own dataset based on a survey of the technology plans from several states. This is something I might not have considered before taking this course. In addition, there are several lessons on the importance of metadata. As a result, I spent a lot of time making sure I understood what the values in my dataset mean. This helped me better understand whether they might help with answering my questions and, if so, how I could best use them in my analysis. I’m sure the final result will be improved by this deeper understanding.
The next course in the series is about cleaning data before you start analyzing it. I’m more than halfway through it, and I think it is the best one yet. I’ll tell you why in my next post.
Sources for DataSets
National Center for Education Statistics
Additional SQL resources
