Jomari Manto

This course has been my favorite subject so far. Despite the challenges I faced, I am genuinely satisfied with what I have learned, and I look forward to further expanding my knowledge and skills in data analytics—particularly in the area of predictive analytics.

Looking back at my expectations during the prelim period, I can confidently say that this course has exceeded them:

“To be introduced to, and learn the fundamentals of data management through the R programming language.”

At first, I was under the impression that the course would focus primarily on the functionalities of R as a software for data management and statistical analysis. I also assumed it would be a continuation of our Statistics course, where we would solve statistical problems using R. However, as the topics progressed, it became clear that this course was not a continuation of Statistics but rather an application of what we had previously learned—this time through the lens of data analytics.

When it comes to data management, this course helped me understand its significance on a much deeper level. I learned that it is not just about collecting and storing data, running SQL commands, or organizing files in a neat folder structure. Initially, I thought data management was limited to R, but this course helped me realize that data management is all around us—it’s an ongoing process that underpins nearly every system that relies on information.

“Obtain fundamental knowledge about data infrastructure and data management technologies required for businesses to utilize business intelligence in their operations regardless of business size.”

This course deepened my understanding of data infrastructure—not just what it means for a business, but how essential it is across all fields. Covering this during the prelim period set an excellent foundation for the rest of the course. It also gave me the clarity I didn’t realize I needed about several topics from past semesters. Ironically, it took this course for me to fully grasp concepts like exploratory data analysis, interpreting scatter plots and line charts, and understanding how visualizations function in the field of data analytics.

“Experience activities that will allow me to learn creating accurate, consistent, and transparent content, emphasizing data precision, granularity, and context.”

“Apply and correlate knowledge and skills obtained from previous courses.”

“Combine learnings from previous courses with newly acquired knowledge to simulate how to deploy formalized means of data storage in a business organization.”

“Equip me with the required knowledge for my next course, as outlined in my program glidepath.”

In summary, all of these learning objectives were not just met—they were exceeded. This course became a space for me to gather everything I had learned previously and connect it with new concepts. It even led me to revisit certain skills I had not practiced in years, like writing a formal research report—the last time I did that was in high school, over a decade ago.

The course also pushed my analytical thinking to new levels. Drawing insights from the datasets we worked on was both challenging and rewarding. Feedback from peers and professors helped me gain confidence in my capabilities, and it also showed me areas I can still improve—especially in generating insights, designing digestible visualizations for decision-makers, and writing clear, meaningful reports.

One of the toughest parts for me was the exams. I quickly realized, even during the prelims, that this wasn’t a "coding" subject. Memorizing code was never the priority, and while I had no issues running code for performance tasks (thanks to AI tools I used for support), exams required me to apply logic and code independently—definitely the most difficult part for me.

The most enjoyable part of the course was when we explored predictive modeling. We covered linear regression and logistic regression, and I’m eager to learn more advanced techniques. I especially appreciated working with real-world datasets relevant to the Philippine context. This made me realize how powerful predictive modeling and data analytics can be in addressing problems affecting everyday Filipinos—including myself.

Overall, I would rate this course a 10/10 and would absolutely recommend it—as well as Sir Raga as the instructor. That said, I must caution that this course is not for the faint of heart. You must be ready to think critically, be proactive, creative, and open to feedback. Most importantly, you need to be diligent—and find a way to enjoy the process.