Analysis

It's easy to make simple mistakes in data analysis. But these little mistakes can result in rework, errors, and—in the worst case—incorrect conclusions that lead you down the wrong path. Making small process changes can help you steer clear of these mistakes and end up having a real impact, both in the amount of time you spend and in your results. Here are some tips for avoiding rookie mistakes in data analytics.

Rapid changes in data availability and analysis tools are leading to evolving expectations for the data analyst role. We can do much more than just generate reports; we have the opportunity to not only process data, but convert it into understandable information and use the knowledge revealed by our work to help change happen.

The modern iterative software development lifecycle has developers checking in code to version control systems frequently, with continuous integration handling building and running automated tests at an almost equally fast rate. This can generate an enormous amount of test data. Here’s how you can ensure you are reporting results effectively across your team and realizing all the benefits of that information.

The ability to verify contracts either statically or dynamically, coupled with recent advances in proof technology, has opened up a new and promising approach to verification. Critical code can be proved with formal methods, and less critical code can be verified using traditional testing, with a clear separation at the interfaces between the two.