First presentations are discouraging but here are the top five things that I learned

Compelling visuals. A clear message. Actionable signals.
A description of all that I had hoped to communicate to my audience after weeks of hard work on my first Data science presentation. I had the conviction that the results of my project would be convincing enough to invite awe and praise.
But instead, my first presentation (surprise!) flopped.
The audience didn’t buy it.
I overestimated my delivery and I didn’t get the response that I expected. I failed to answer a crucial question in my analysis.
Immediately, my confidence died. I began to stutter nervously, and drop things on the floor as I clinched to my desk.
This spotlights a common occurrence for any data scientist but especially first-timers like myself, whose first reaction to failing at anything, is to quit. But this is probably our first great opportunity to prove ourselves.
As data scientists, it’s not unusual that we become extremely passionate and excited about the projects that we work on. We are also equally disappointed when our delivery is not received with a similar passion.
Stop. Breathe…
Whenever I got stuck on a challenging question, my first instinct was to offer the first answer that came to my mind. I realized that quick but poorly thought through responses, only sets me up for greater failure.
If you ever find yourself in this position, it’s OK to allow yourself time to find the right answer/s. Your audience will understand. We become impatient because we fear that someone is going to find us out. But a badly framed answer is more likely to expose our shortcomings. And so I have learned how important it is to give myself time before diving in to find an immediate fix.
It is also a good reminder that having stood before an audience to share something that I have researched, worked on for weeks or days, polished, and improved like any data scientist tweaking a model, I deserve to to be proud of myself. Data Science is tough. I completed an entire project and I proved it. I have shown that I am capable.
Be willing to accept failure.
Failure helps to mold us into becoming better data scientists.
After a bad presentation (and any presentation) I usually beat myself up. But it is more crucial to be intellectually honest. Accepting failure does not mean that I am a failure. Instead, I failed to effectively deliver a presentation. Having that piece of information helps me to own my mistake and to understand that I can do a better job. The only time that I fail is when I give up.
Disaggregate the problem
In data science, I learn to extract signals from noise. It may be a knowledge gap. Hasty, nervous speaking. Complicated visuals. Too much time spent on the technical details. The wrong messaging for your audience. Or unclear findings.
To avoid making these mistakes, I dedicate time to identifying what the problem is, cutting it down into small chunks, and putting my skills to work.
I also learn to ask the right questions. I realize that I can’t make improvements if I don’t know what I don’t know. Whenever I am unclear about something, I don’t hesitate to ask for clarification from my audience – there is no shame in wanting to fix something. And as entrepreneur and data scientist Damian Mingle says, "If you want to help individuals, be empathetic and ask questions; that way you can begin to better understand their journey too."
If I care enough about something, I will pursue it against all odds.
Embrace and adapt to change
Believing in my abilities is paramount for accepting that there is a problem that I can fix. How can I make my next presentation more engaging? Or more meaningful?
The "one shoe fits all" approach doesn’t allow optimal delivery in unique cases. And so it’s pertinent to prescribe a solution that actually works given the circumstances. Maybe my presentation style worked for my public speaking class or my teammates thought the visuals were great. However, it is fundamental to step away from the things that no longer work in order to develop a deeper level of subject matter expertise. Maybe I needed to change my analytics tools or adopt new technology. I am better prepared to adjust my mindset, think things through differently, and invite new perspectives.
Engineer the next move and move on
Failing to deliver a quality presentation doesn’t mean that all is done and dusted. Two common missteps that I often make is that one, I value myself based on how well a job I did. And two, I allow fear of failure to hinder future prospects.
The positive thing about failing is that I can envision an opportunity to be better. This is an opportunity to set new goals, step outside my comfort zone, and plan ahead strategically. This principle applies throughout my work-life. Whatever the outcome of my presentation was, I plan for the unexpected. I also prepare to work harder.
My goal is to get the right message across. If I messed that up, I fix it. Once I have accomplished that feat, I don’t allow fear to hold me back, instead I move on to my next project. Moving on, allows future successes to overwhelm past failures.
Recognizing the problem, identifying a solution, and making improvements are integral to every aspect of data science. Sure, I will never get back that fifteen minutes but I grow in my abilities as I acknowledge and practice those tenets.