Seven weeks into the Data Science Immersive course, I’m astounded by how much ground we’ve covered and how much I’ve learned in such a short amount of time. From the basics of Python all the way through to our most recent project in Natural Language Processing, there are millions of potential avenues to explore.
Based on my previous experience, I was particularly excited to learn about using APIs, as well as web scraping. For instance, I can imagine how connecting and using the APIs provided by many of my former team’s tools would streamline the reporting process. This, combined with information we could gather through web scraping on online forums not covered by those tools, I believe this would be an overall value-add to my previous work. While I can see how this knowledge would be helpful for my prior role, I’m excited to take these new skills into different fields and explore how they can help solve new and different business challenges.
While we’ve covered many different supervised learning models for both regression and classification problems, such as Linear/Logistic Regression, K-Nearest Neighbors, Random Forest and SVM, I am particularly excited to learn more about unsupervised learning and discover how it compares to the previous modeling methods we’ve learned.
While there’s still plenty more to learn, I’m excited by the possibilities these new skills can open up, and I look forward to applying them to my capstone project as we approach the end of the course.