15 Where to go from here

This online textbook covered the foundations of data analysis and visualization. There is still much to learn. Thankfully, you can go deeper on any of the subjects we discussed.

Did you love the analytics and introduction to modeling? If so, you probably want to transition from spreadsheets into a statistical programming tool such as Python or R where you can accomplish a lot more and in a shorter period of time.

Do you want to find ways to connect data science to web development to turn theory into something tangible? Many web development frameworks today play nicely with data tools because websites are increasingly data-driven on the backend. Django, for instance, is a full development framework built on Python. Less complex, you can use tools like R Markdown and Shiny to build web pages and interactive dashboards.

Do you want to help turn your company’s data assets into actionable resources for your colleagues? Business intelligence (BI) is the act of capturing, storing, and distributing an organization’s data so that people can make more data-driven decisions. Tools such as Tableau, Power BI, and Looker require strategic thinkers to prioritize development and technical minds to build the dashboards themselves.

Did you love the data possibilities from an organization perspective, but don’t want to be the one coding the solutions? If so, you can still serve data initiatives in a variety of ways. Data governance, for example, is increasingly important and touches on hig-impact areas such as data ethics and data security — both of which need technical and non-technical perspectives.

One thing remains clear regardless of what you focus on. Being a non-data person is no longer a viable career option and a baseline understanding of data fundamentals is a minimum requirement for most of us. Beyond that, the choice is yours and the learning opportunities are endless.

Best of luck!