7 steps to build a data science project in spare time
By: Rafał Rybnik
Tags: Programming, Machine Learning, Artificial Intelligence, Projects, Data Science

Get the idea worth pursuing

Sometimes the idea comes from peculiar places. Also, strange ideas can come from rather normal places. (video source: Alice in Wonderland, 1951)

“The first step is to get an idea. This is not all that hard to do. The tricky part is to get a good idea.”

The first step is crucial.

If you will take up the idea that’s filling you with emotions, you will work on it with enthusiasm.

To generate exciting concepts worth your time, you should come up with lots and lots of ideas and throw out those that aren’t good enough.

Ideas may come from many different places.

But while most of the beginners start to seek inspiration in scientific papers, Dr Varian says it is not a good source of original ideas. Fine, reading articles about data science improves your craft by getting you familiarized with new techniques and insight.

But at the same time, your mind is getting biased by someone else’s ideas.

“I think that you should look for your ideas outside the academic journals — in newspapers, in magazines, in conversations, and in TV and radio programs.”

So, what are the best places?

newspapers and magazines

TV and radio programs

podcasts

conversations

business people around you

your own life and experiences

When you read about non-data science content, you could discover problems or inefficiencies, that you can solve with AI/ML.

You can also simply use your memories and bring ideas from your own experiences. Ask yourself how would you help a person like live better, make better decisions or spend less money?

“One of my favorite pieces of my own work is the paper I wrote on “A Model of Sales”. I had decided to get a new TV so I followed the ads in the newspaper to get an idea of how much it would cost. (…) Once I developed the model I had a research assistant go through a couple of years’ worth of the Ann Arbor News searching for the prices of color TVs. Much to my delight the general pattern of pricing was similar to that predicted by the model. And, yes, I did manage to get a pretty good deal on the TV I eventually bought.”

You will know when you come up with a good idea. If your idea appeals to your feeling of usefulness, this could be nice to work on it.

Generate a lot of ideas by reading and listening outside your specialisation.

Take ideas that appeal to your feeling of usefulness.

Is your idea worth pursuing?

The first test of your idea should be trying to explain it in a way that a person unfamiliar with AI/ML can understand it. Try to paraphrase your idea in a way that a six years old can understand it. If you can’t, the idea is probably not very good.

Or you overestimated your level of knowledge. In this case, take a step back and read more about this topic.

But even if you can explain the subject to a child and not bore them to death, there isn’t still a certainty that idea isn’t too lousy. Although it could be tempting at first sight, pursuing a first viable idea doesn’t guarantee anything.

You can ask a few friends what they think about your idea. If your concept has a lot of implications and is interesting for other people, you should spend some time working on it.

“Always remember that working on this particular idea has an opportunity cost — you could be spending your time working on a different idea.”