A primer for anyone diving into the data ecosystem
A Friendly Introduction to Data-Driven Marketing for Business Leaders
How to Run an Effective Data Science POC in 7 Steps
What does running a POC mean in practice specifically for data science? When it comes to the evaluation of data science solutions, POCs should prove not just that a solution solves one particular, specific problem, but that a system will provide widespread value to the company: that it’s capable of bringing a data-driven perspective to a range of the business’s strategic objectives.
A Friendly Introduction to Open Source Data Science for Business Leaders
Open source is a key enabler for enterprise data science, both in terms of the growing ecosystem of open-source tools and the expanding number of complementary enterprise data science platforms that incorporate and build on open source languages and tools. The challenge is identifying which of those tools is relevant and valuable to your business. Assessing the maturity of these projects, grappling with any licensing issues, and making sure your team has the correct skillset to use them are challenges that many companies are now facing.
Top 10 Practices to Operationalize Your Data Science Projects in the Real World
Neural Networks 101
Machine Learning Classifier: Basics and Evaluation
Decision Trees: How to Optimize My Decision-Making Process?
The major advantage of using decision trees is that they are intuitively very easy to explain. They closely mirror human decision-making compared to other regression and classification approaches. They can be displayed graphically, and they can easily handle qualitative predictors without the need to create dummy variables.
