What is R?

What is Python?

Why Should Analysts Use Advanced Analytics Coding Tools like Python, R, or SAS?

We would highly recommend encouraging analysts at your organization to learn an advanced analytics coding tool like Python, R, or SAS. In particular, we’d recommend that they learn Python. SAS comes with a significant licensing cost and seems to be declining in popularity. R is a good, open source (free) option, but we recommend Python as it has become quite popular for data manipulation and data science, has efficient data-related packages, and has the most flexibility as a general purpose language. It’s important for analysts and data scientists to know at least 1 of these 3 tools for advanced data analysis and automation use cases in order build data science models. While SQL can be very useful in the process of these tasks and can be used directly within each of these tools, sometimes SQL alone can’t entirely accomplish these more advanced use cases.

Using these tools to code out a data process, analysis, or model build can lead to efficiency gains as the code can easily be tweaked, scheduled to run automatically, and reused for similar projects in the future. It also affords more flexible data manipulation, analysis, and data science than Excel and even SQL sometimes through operations like API calls and looping/iterating. If you can dream it, you can build it! While the idea of learning a coding language may seem daunting to many analysts who are used to using point-and-click tools like Excel and Alteryx for analysis, all 3 of these advanced analytics tools are widely considered to be much easier to learn than other lower level coding languages like Java, C, etc…If needed, Value Driven Analytics can provide engaging training for your analytics team to help them learn Python from scratch. This is a worthwhile investment as it can increase your analytics team’s efficiency and flexibility. Watch the videos above to learn more about R and Python.