I was watching a Youtube conference where they explain the Data Scientist career. What was said will be shortened into a condensed passage of notes. Firstly, Top Data Scientists need to be good story tellers. That is a way to help everyone make sense of what was analyzed.
As you are analyzing the data, ask yourself these 3 questions:
1. What do you want me to take away from this data?
2. What action do you want me to take?
3. How do you feel? Please communicate the data in a way that is something living and changing.
Data is coming at us from all directions. it is a messy complex world. As you consume the data, it is best to be on the cutting edge, but not on the bleeding edge of it. When you look at the data, ask yourself, can we make this repeatable? Then bring it back into the loop. When you are successful and seeing patterns and able to loop those patterns, ask yourself how can we remove the manual human labor in the process? We want to make it an operation. That becomes the aspect where you have to incorporate a decision into the matter.
What is the best way to present the data? Can we make it look interesting? Try using graphs and charts.
Something that is extremely important to practice in working with data is knowing how secure it is. You want to find and eliminate the bad guys who present bad representations of themselves. Nate Silver calls it finding the signal in the noise. This can be practiced both on a positive statistical side and security too.
Carefully ask yourself how do you incorporate your business in with data science. Do I put the business essence into IT, or put the tech sector into business? Actually, it’s best to do neither, you want to create an entirely new business for the Data Scientist. They may work best as advisers or contractors.
A Data Scientist doesn’t need to have a strong background in hacking, math, or engineering to be a good scientist. They need to be clever and interpret data into good hypothesis to bring value for an organization. Performing work a long the lines of the company called, Tableau may be the future in Data Science.
The best approach to Data Science is you tackle the biggest impact questions. Use the best tools that have the most dynamic range. You never know what is going to happen in the future. It’s very hard to determine what is going to happen next.
Since security is a major hurdle, it is a delicate balance between unlocking the data and keeping it secure. The data scientist can only work with data that is unlocked, but it’s important not to let the wrong people gain access to the sensitive data. Many times, the data may be lumped together with very important and nonimportant bits of information, so prioritizing what is valuable and what is not isn’t easy.
Find reasons to say yes