Happy that finally i have started my side project pitched my idea and it was nicely accepted… Was waiting for this to culminate for some time. Well, it was about to fall apart as i didn’t get a lot of time to prep and think about what i will say, since the meeting was setup at the last minute. A discussion with my manager flashed in my brain and i decided pivot my pitch on that, luckily it worked.
How to pitch your project?
- Know Who You’re Pitching: Understand who is the person/organisation you are pitching to? what are their driving levers, what do they care about
- Understand their success metrics: Identify how your project can help them. Easy to understand difficult to implement.
- Pitching to investors: Here, why is the problem important, how you are solving it and if they get onboard how you can exponentially solve the problem should be the key focus of the pitch
- Pitching to Clients: Here, why is the problem important, how they are not optimally solving the problem an how you can help them exponentially solve the problem should be the key focus.
slightly nuanced but extremely important for the outcome
So, what was that my manager told me?
Just before my project pitch to the Product team, my manager told me focus on how their problem is getting solved by our solution, not on what is the solution, how we have applied the best tech, latest method, finest algorithm. That stuck with me and has always guided my pitches. Now, you can use this too.
I’ll share my learning progress in the projects section and my mental space handling Office work, Side Project, Coursera courses and Personal Growth in daily blogs do check them out. This will be a great input for folks who are planning to begin learning a new area from scratch in data science & build expertise while managing stuff.
The project delves into ‘Computer Vision’ and its applications Since, i am currently a Noob, i’ll be working on this from scratch to build the framework
These are the first set of articles you can expect to read in the near future (not an exhaustive list):
- Basics of Neural Networks
- Hyper parameter tuning of neural Networks
- Working with images, understanding how to deal with pixels
- Object detection
- image segmentation
- instance segmentation
What strategies have you deployed to crack you pitches?
~P
