Data science is an umbrella term that can cover visualization, statistical inference, predictive modeling, machine learning, deep learning, artificial intelligence, data engineering, insert buzzword of the day here… (the list goes on). Regardless of what specific niche you’re trying to fit into in the data science universe, it’s likely you’ll rely on a few key languages. These popular languages include R and Python (and SQL when you have to). There are other languages out there that can be used for data analysis and machine learning, but R and Python are the most popular from what I’ve seen. I will discuss both of these primary languages below and weigh their strengths and weaknesses.
Below is an example of an interactive predictive app. This specific example is model to predict NFL football plays. If you have an event worth predicting and we can find data to make a useful model, than this format can be adopted to suit your needs. Below is an example app for automated reporting and …
Download the word document (above) to see a sample of how risk analysis can increase your bottom line when it comes to investing.
Whether you already create recurring reports that are rich in data or are curious about starting, automated reporting can make your life easier. With automated reporting, you can have on demand production of a preset report and the possibility for further unique data exploration. This means less time is wasted creating the report, and more time is available for interpreting and drawing strategic insight from your data.