Data science entails more than just collecting and analyzing data. Tom Merritt lists five basic data science steps.
A lot of people talk about data science. Few of them know what they’re talking about and even fewer are aware of how it works. But, it’s used everywhere these days, so even if you aren’t a data scientist, it’s good to know what the basic steps are. Here are five basic steps for data science.
SEE: TechRepublic Premium editorial calendar: IT policies, checklists, toolkits, and research for download (TechRepublic Premium)
- Why are you doing it? Are you solving a problem? What problem is it? Data science is not a sauce you spread on things to make them better somehow. It’s a way of addressing issues. Know what problem your business is trying to solve before you ask data science to solve it.
- Collect the data. Once you know the business reason, your data scientist can start figuring out what data pertains to it and collect it. Don’t just pick the available data or you risk introducing bias.
- Analyze the data. Exploratory data analysis (EDA) is the most common approach. It reveals what the data can tell you. EDA is often good at revealing areas where you want to collect more data. Good EDA uses a predefined set of guidelines and thresholds to help overcome bias.
- Build your models and test if they’re valid. Once you have the data analyzed you can make your machine learning model that aims to provide a good solution to the business problem. Before settling on a model, be sure to experiment with a few suitable options and validation cycles.
- Results. Run the model and interpret the results. A lot of folks don’t realize that artificial intelligence doesn’t just tell you the solution to your problem. Machine learning models deliver output that humans interpret. The data scientist’s insights are what make the output something you can take action on.
Sure this makes it sound “that easy,” and obviously any data scientist knows the proof is in all that work to make these things happen, but knowing the basics can help you make better decisions that will help your data scientists do their job better. Everybody wins. Even the machine.