H2O is the scalable, open-source ML library that features AutoML. Here's why it's an essential library for me (and you).
Moving into 2020, three things are clear - Organizations want Data Science, Cloud, and Apps. A key skill that companies need is Git for application development (I call this Full Stack Data Science). Here's what is driving Git's growth, and why you should learn Git for data science application development.
Moving into 2020, three things are clear - Organizations want Data Science, Cloud, and Apps. Here are the essential skills for Data Scientists that need to build and deploy applications in 2020 and beyond.
Moving into 2020, three things are clear - Organizations want Data Science, Cloud, and Apps. Here's how Docker plays a part in the essential skills of 2020.
Organizations depend on the Data Science team to build distributed applications that solve business needs. AWS provides an infrastructure to host data science products for stakeholder to access.
In this article, we'll examine how Blue Oceans are created and how your organization can create Blue Oceans with Data Science too. We'll finish with a roadmap for your organization to build Blue Oceans with Data Science.
This is a true story based on how I created my data science company from scratch. It's a detailed documentation of my personal journey along with the company I founded, Business Science.
Learn how going from Excel to R can speed up Exploratory Data Analysis, getting business insights 100X FASTER.
When you want to learn data science, you are faced with endless options and limited time. Here's the best approach to learn data science!
Understand the data science workflow by defining the sequence of steps to go from a business problem to generating business value using a data science workflow.
These are 3 Rules I learned through my consulting experience that have helped me be able to deliver results with data science.
Student feedback led to a BRAND NEW CHEAT SHEET on customer segmentation and a major overhaul to our Week 6 Modeling Chapter in our Business Analysis with R Course.
The first article in a 3-part series on Excel to R, this article walks the reader through a Marketing Case Study exposing the 10X productivity boost from switching from Excel to R.
Artificial intelligence (AI) has the potential to change industries across the board, yet few organizations are able to capture its value and realize a real return-on-investment. The reality is that the transition to AI and data driven analysis is difficult and not well understood. The issue is twofold, first, the necessary technology to complete such a task has only recently become mainstream, and second, most data scientists are inexperienced in their respective industries. However, with all the uncertainty surrounding this topic, one hedge fund has managed to navigate through these challenges and accomplish what many companies are failing to do: building a high-performing data science team that achieves real return-on-investment (ROI).
Data Science is an amazing field of research that is under active development both from the academia and the industry. One of the saddest facts in the real-world is that most data science projects in organizations fail. Here I’ll present a new iteration of an agile framework called Business Science Problem Framework (Download PDF here) to implement data science in a way that enables decision making to follow a systematic process that connects the models you create to Return On Investment (ROI) and show the value that your improvements bring to the business. The end result is that the BSPF is an agile framework, and we are working to develop a new visualization (BSPF 2.0) that conveys this agility.
The Expected Value Framework connects the machine learning model to ROI. In data science for business, it is critical to quantify the ROI of data science.
Data Scientists want to run successful projects. However, the sad fact is that most data science projects in organizations fail. It’s not because of lack of skill or knowledge. Data science projects need a clear and effective plan of attack to be successful. As data scientists, we study a wide array of tools: advanced algorithms, knowledge of statistics, and even programming skills.