When we’re working with large amounts of data, errors are inevitable. Here’s how to check your data for errors manually using Python.
Here’s how to write Python scripts to check your data for errors (minus the tedium of doing it yourself).
To all the young programmers worrying about the uncertain job market, I promise things will get better.
Games and gaming offer a useful analogy for real life. By closely examining the way AI plays games, we can learn some valuable lessons.
Code review is important, but it can be time- and labor-intensive. Fortunately, a few simple steps can streamline the process nicely.
Trying and failing to decipher your own codebase? Remember: Good code is its own best documentation.
Here’s how to heat up your heat maps and make sure they stand out.
Agile won’t produce great software on its own. For that, you need great management of engineers and product designers.
In honor of #BlackinDataWeek, the Sadie Collective has a list of nine Black women data scientists to know.
Too often, developers carry out their work with the mindset that everything will go perfectly. This type of blind optimism can have disastrous consequences.
