Tag: Data Science/Machine Learning

There’s a handy little formula that every data scientist should know. Link.

I recently wrote an article on this topic for the Winter issue of Sloan Management Review. Link.

An increasing number of organizations are bringing data scientists on board as executives and managers recognize the potential of data science and artificial intelligence to boost performance. But hiring talented data scientists is one thing; harnessing their capabilities for the benefit of the organization is another.

Supporting and getting the best out of data science teams requires a particular set of practices, including clearly identifying problems, setting metrics to evaluate success, and taking a close look at results. These steps don’t require technical knowledge and instead place a premium on clear business thinking, including understanding the business and how to achieve impact for the organization.

Data science teams can be a great source of value to the business, but failing to give them proper guidance isn’t a recipe for success. Following these steps will help data science teams realize their full potential, to the benefit of your organization.

Continue reading: https://mitsloan.mit.edu/ideas-made-to-matter/6-steps-leading-successful-data-science-teams

If you know how to build predictive models, you can leverage this knowledge to learn optimal policies – rules that tell you the best way to act in various situations – directly from data.

Policy optimization problems are very common in the business world (e.g., arguably, every personalization problem is a policy optimization problem) and knowing how to solve them is a data science superpower.

The following series of blog posts aims to give you that superpower 🙂

• In Part 1, I motivate the need to learn optimal policies from data. Policy optimization covers a vast range of practical situations and I briefly describe examples from healthcare, churn prevention, target marketing and city government.
• In Part 2, I walk through how to create a dataset so that it is suited for policy optimization.
• In Part 3, I describe a simple (and, in my opinion, magical) way to use such a dataset to estimate the effectiveness of any policy.
• In Part 4, I show how to use such a dataset to find an optimal policy.

Happy learning!

Yoshua Bengio is a Deep Learning legend and won the Turing Award in 2018, along with Geoff Hinton and Yann LeCun.

In this short post, I want to highlight for you some clever things that Yoshua and his collaborators did to win a Machine Learning competition from a field of 381 competing teams. Perhaps these ideas will be useful in your own work.

In a world where powerful Deep Learning frameworks (e.g., TensorFlow, PyTorch) are a free download away, their competition-winning approach demonstrates nicely that your edge may come from how well you model the specifics of your problem.

Read the rest of the post on Medium.