Tip 1: Think big, but start small. Someone once said, "Never use a big word when a small one will do." The same can be said of data science activities: "Never use a complex model when a simpler one will suffice." Similarly, Albert Einstein said that all things should be as simple as possible, but no simpler. The KISS principle applies: Keep It Simple and Smart.
The Pareto principle also applies: 80 percent of value comes from your first 20 percent of effort. You can read more about the 80-20 rule on my blog post here.
Tip 2: Don’t build the model, find it. Data science today should be focused less on "building the model" and more on "finding the model in the data.” In the big data era, the data becomes the model. What does this mean?
As a data scientist, your job is to decode the data, to discover the patterns (information, knowledge, and insights) that are already encoded in the bits and the bytes of the data collections that you have assembled.
Example: Think about DNA sequencing. A DNA sequence is a symbolic representation of the sequence of about 20,000 genes. That symbolic representation (the bits and the bytes—i.e., the data) is essentially indecipherable. You can't make any sense of it, but nevertheless all the things you need to know about your heredity and possible future (propensity for diseases, hair loss, hair color, etc.) are encoded there. The knowledge is already there. The "model" to predict your health future is already there!
Therefore, the job of data scientists (in the DNA case, the job of bioinformatics experts) is to decode the data and to find the knowledge in the data—i.e., to find the model in the data, because the data is the model.
