1.1 Given a scenario, apply the appropriate statistical method or concept. For each of the following concepts: Define what it is. What are the pros and cons of the method or concept? When would you use it? When would you use it in lieu of something else and why? In which situations is the concept [...]

Evaluating a model from the business perspective is critical to ensure that it is meets the goals and needs of the organization, provides a good return on investment (ROI), is resource efficient, mitigates risks, has a positive impact on the business, and is aligned with the overall strategy of the organization.

In this post, we look at the model building process from a high level and walk through some specific tasks and considerations that should be made throughout this step. After reading, you will have enough information to make a model development game plan for your project!

Slow AI is about the responsible creation of data-driven systems through responsible, methodical, and well-documented processes.

Machine Learning for IoT use cases has the potential to unlock tremendous amounts of value and information hiding in the piles of data that companies collect. There are many approaches to teasing out interesting information. The list of algorithms and examples here barely scratches the surface! The most important thing to consider is what is the business use case.

Essentially, Data Science is an interdisciplinary field of study that encompasses the theory and application of methods used to generate, collect, store, process, analyze, visualize, model, and serve data, and insights or predictions. Additionally, it includes the orchestration and management of those processes and the people involved. Oftentimes, Data Science is described as the intersection between Computer Science, Math & Stats, and Domain Knowledge.