Do’s and Don’ts of Data Science

Don’t Start with the Data
Do Start with a Good Question

Don’t think one person can do it all
Do build a well-rounded team

Don’t only use one tool
Do use the best tool for the job

Don’t brag about the size of your data
Do collect relevant data

Don’t ignore domain knowledge
Do consult a subject matter expert

Don’t publish a table of numbers
Do create informative charts

Don’t use just your own data
Do enhance your analysis with open data

Don’t do all the work yourself
Do partner with local universities

Don’t always build your own tools
Do use lots of open source tools

Don’t keep all your findings to yourself
Do share your analysis and results with the world!


Got any to add? Please leave a comment.

5 thoughts on “Do’s and Don’ts of Data Science”

    1. There are lots of freely available (open) data sets for use. Some common examples are weather data and population data. Adding weather data to your analysis can often yield promising results. For example, a local business will likely less sales during very cold or rainy days. Without the weather data, it might be difficult to explain the slump in sales. There are many other examples, but open data can augment the data many organizations already collect.

Leave a Reply

Your email address will not be published. Required fields are marked *