This is the 6th installment in an ongoing blog series to recap the talks given on Day 2 of our 2019 Demystifying Data Science live online conference. In her talk, Natalie Evans Harris discusses the ethics and opportunities of data governance.
It was once common to learn a skill, apply it to a job for 30 or so years, and call it a career. But rapid technological developments have made that approach a thing of the past, and it's now more important than ever to keep learning, evolving, and applying new approaches in order to do your best work. In this post, read how business leaders can encourage a learning and growth mindset within their analytics teams.
Are you using the right data for decision-making? The under-utilization of data is a key reason why most companies fall short in efforts to scale the impact of analytics and mitigate potential risks. In this post, learn how to effectively extract value from untapped data.
In this recap of Peter Guerra's Demystifying Data Science talk, we walk through three examples of different journeys to scaling AI, detailing the joys and perils of each. Plus, get practical tips for how your business can scale AI effectively.
This is part of a blog series to recap the talks given on Day 2 of our 2019 Demystifying Data Science live online conference. During hers, Aubrey HB, Director of Advanced Analytics at Nationwide Building Society, discusses the challenges that legacy companies face in becoming data-centric.
In a recent interview with Burtch Works, a leading executive recruiting agency, Metis Executive Director of Data Science Corporate Training Mike Galvin and Metis Sr. Data Scientist Kevin Birnbaum were asked about Python's rise in popularity among both practitioners and businesses.
In this post, Alex Nathan, Co-Founder of Aventrix Analytics and part-time Metis Corporate Training Instructor, asks the question: Does the Data Speak for Itself? As it turns out, the answer is no, because data alone is typically not enough to arrive at any conclusions. The following equation explains the source of discrepancy: DATA + ASSUMPTIONS = CONCLUSIONS. Read on to find out how data scientists can address this issue.
This is part of a blog series to recap the talks given on Day 2 of our 2019 Demystifying Data Science live online conference. At the time of his talk, Adrian Cartier worked at Bayer, where he led data science efforts. Here, he discusses Bayer's move from cottage industry to industry force by way of a top-down digital transformation.
Many employees feel stuck in jobs where everything is “status quo.” They might feel happy or content at times, but many also have a feeling that there’s more they could be doing to contribute to the company – if they only knew where to start. In this post, we explore the benefits of developing skill paths for your employees, which can drive performance, demonstrate company investment, and increase career satisfaction.