Attending TCU's Neeley Data Analytics Academy program this summer has taught me several vital skills related to data science and analytics: scraping, cleaning/filtering, analysis, and visualization. For data scraping, we used a software called UiPath, which provided code-free methods to automate user input on pages as well as using computer vision to extract data. The second software we used was Alteryx, which allowed us to clean the data-- removing extra data, detecting and managing typos and empty columns, summarize data, and other data cleaning tools were among a few techniques we used. Under the hood, Alteryx generates SQL that efficiently and strategically queries the data; SQL is another tool we learned to use in case software was insufficient (although I already had extensive SQL experience prior to the program). Finally, we used Orange and Tableau to visualize and present the data; we needed to be certain the data was meaningful (significant), but also needed to make the data presentable to an audience, as raw numbers are both underwhelming and harder to identify at a glance; according to MedTech Intelligence, the human brain processes images around 60,000 times faster than text so visuals are scientifically more meaningful. Orange was another software we used.

Our final project, a presentation in which we identified a problem (or simply a topic to analyze) and made predictions or other analysis based on data we found online, which we later presented to other students and groups participating in the program. My group decided to focus on climate change-- or, more specifically, emissions. We discussed the emission cut required for Net Zero, and made a prediction for where we are currently heading with current trends (which unfortunately is nowhere close to what is required). The challenges we faced included finding good datasets, generating meaningful visualizations and, of course, remembering everything we had learned the previous week.