Data Science

A Friendly Introduction to Data-Driven Marketing for Business Leaders

A Friendly Introduction to Data-Driven Marketing for Business Leaders

Taking an algorithmic approach to attribution is just the beginning of driving change by moving toward a more detailed, data-driven approach in marketing.

How to Run an Effective Data Science POC in 7 Steps

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

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

Top 10 Practices to Operationalize Your Data Science Projects in the Real World

I want to use this post to share the top 10 practices to deploy your machine learning models into production in the real world.

Decision Trees: How to Optimize My Decision-Making Process?

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.

k-Nearest Neighbors: Who are close to you?

k-Nearest Neighbors: Who are close to you?

The k-Nearest Neighbors algorithm is a simple and effective way to classify data. It is an example of instance-based learning, where you need to have instances of data close at hand to perform the machine learning algorithm.

14 Golden Nuggets to Demystify Data Science for Aspiring Data Scientists

14 Golden Nuggets to Demystify Data Science for Aspiring Data Scientists

These talks cover a wide range of topics: from showcasing your work to connecting with data leaders, from telling a persuasive data story to debugging myths in data science. I took some detailed notes of all the talks and decided to use this post to share the main takeaways.