Make it easy for your audience to quickly determine what they're about to digest. Use an abstract or introduction to recall your objectives and clearly state them for your readers. What is the problem that you've set out to solve? If you have a desired outcome or any expectations of your audience, say it, as this is the entire reason you're presenting them with your analysis. You then cover everything from your preamble in this section: the question you've been on a mission to answer, your hypothesis, and the methodology you've used.

This is my first discussion on the forum so please forgive me any faux pas but I have something really exciting to share! At work I had some free time and Azure credits so I decided to give their ML lab a whirl. I took a very broad and difficult question, although one that should have had a finite answer, and used some SCADA data to train a model that would be able to predict unplanned downtime in a well far enough ahead as to be able to send out an engineer to apply preventative maintenance. Common idea, I felt, and enough data to work something out. I won't bore you with the details (unless you ask, of course!) but something astonishing and totally unexpected came out of the analysis.

See the forest, see the trees. Here lies the challenge in both performing and presenting an analysis. As data scientists, analysts, and machine learning engineers faced with fulfilling business objectives, we find ourselves bridging the gap between The Two Cultures: sciences and humanities. After spending countless hours at the terminal devising a creative and elegant solution to a difficult problem, the insights and business applications are obvious in our minds. But how do you distill them into something you can communicate? Presenting my work is one of the surprising challenges I faced in my recent transition from academia to life as a data analyst at a market research and strategy firm.