I'm going to rephrase your question a bit:
"Is it worth preparing for the DASCA Data Science certification, and if not, what should I do instead?"
I teach data science and counsel with a lot of students looking for jobs, as well as employers looking for people to hire. I've also worked as a data science consultant for a few years.
I have never heard of this certification. I've talked to a few other data scientists that I know, and they also haven't heard of this certification.
In the world of data science, there are largely two credentials that count:
- A degree in something from a major university. These days, that something can be:
- data science
- computer science
- software engineering
- in some cases, a degree specific to the problem domain you're interested in. (Economics or foreign policy are common examples)
- Actual data science experience. This can be real-world experience, such as an undergraduate research project, a senior project / capstone class that you design yourself, personal projects that you document, Kaggle competition results, etc...
The employers I've talked to in my capacity as a teacher and as a consultant typically don't give much stock to certificates on their own.
They've found that people who focus on certificates either have too little real project experience (e.g. they know how to solve a couple of specific, clearly defined problems), lack the ability to do data pre-processing (which is the what data scientists spend the vast majority of their time doing), or lack experience working with a team.
There are of course exceptions to this, but by and large, you'll have a much better time if you focus on getting real project experience.
What to do instead
Unless there's a very compelling reason for you to get this particular certification, (you love collecting certificates for fun, someone is paying you for it, or there's a particular job you want that lists this certificate as an explicit requirement), here's what I would recommend you do instead:
Finish your UG degree, making sure that you take steps necessary to have as many meaningful data science projects as you can, preferably ones where you work with a team of people. Use the resources of your UG program to help you network with people in the industries you're interested in, find a good internship, visit career fairs, etc...
Look for personal projects you can do that will show you have explored different aspects of data science, especially data preprocessing. This can be things you just decide to do on your own, UCI datasets you explore for fun, Kaggle competitions or explorations, etc... Treat these like real research / business problems.
Make sure you document these results somewhere public, such as GitHub or Kaggle via Jupyter notebooks, or in some other way that makes it easy for you to share them with potential employers. This should go beyond just "here's my regression result", but a discussion of the choices you made, how they impacted the results, and caveats about your conclusions. This Jupyter notebook on Kaggle is a great example of what I'm talking about.