The good old clustering analysis techniques present some differences when applied to time series. So many to discuss in one simple post. However, I will do my best to provide some examples of two basic approaches for doing time series analysis [6min read].
DVC provides the elements for managing machine learning pipelines using git repositories as a backend. If you use git for tracking your machine learning experiments, you will feel comfortable with DVC.[9min read]
Notebooks rule! We agree on that, but they can get messy very fast. The truth is they are not the best tool for good software engineering practices. Data Version Control (DVC) is a toolset for helping the development of machine learning experiments favoring better practices. Are you ready to give it a try? [5min read]
The Software Industry has well-defined standards and procedures which are heavily based on tools such as Gitlab. However, in research sometimes we follow a more relaxed and not structured way. At LABSIN we have recently begun to apply software industry approaches to our daily work. The match is not perfect since research could be different in some way. But, the benefits are clear. [9min read]
Decent programming skills, strong math and stats knowledge, and amazing visuals are not enough for a data science position in the industry. These are just necessary tools you will need for doing your daily tasks, but you don't have to lose the ultimate goal "to provide valuable information to decision-makers" (Duh!). This is how you can make a difference and companies know it. [5min read]