Filling in the Corporate Training Gaps: Michael Galvin's Article in Training Industry Magazine

By Carlos Russo • July 28, 2020

Michael Galvin, Kaplan's Executive Director of Analytics, wrote an article for Training Industry Magazine on the most important data and analytics capabilities for today's companies. Called Filling the Gaps, the article breaks down data literacy, Python, and machine learning, the three most in-demand data science and analytics skills companies should prioritize when thinking about training their teams. 

Additionally, Mike covers how companies can use training to solve common problems and achieve strategic goals, as well as goes over what to look for when seeking out a training provider. 

It's a great read. Check it out in full here.

_____

To address the data and analytics needs of organizations, our Corporate Training team offers courses on each of the featured topics above: Data Literacy, Python for Data Analysis, and Machine Learning. Learn more about these and all other courses on our Corporate Training page


Similar Posts

business resource
VIDEO: An AI4 Panel Discussion on The State of AI in Banking

By Carlos Russo • September 23, 2020

Metis Sr. Data Scientist Javed Ahmed recently took part in a panel discussion about The State of AI in Banking during an online Ai4 event. He and the other panelists talked about upskilling, challenges related to COVID-19, and more. Watch the recorded panel discussion here.

business resource
VIDEO: Recorded Talk - How Machine Learning is Changing Finance with Javed Ahmed

By Carlos Russo • August 20, 2020

Watch a recording of Metis Sr. Data Scientist Javed Ahmed's talk on How Machine Learning is Changing Finance at the new Wake Forest University Financial Services and Fintech Hub.

business resource
Scoping Data Science Projects

By Damien Martin • July 07, 2021

In February, Metis Sr. Data Scientist Damien Martin wrote a post on how to foster a data literate and empowered workforce, which allows your data science team to then work on projects rather than ad hoc analyses. In this post, he explains how to carefully scope those data science projects for maximum impact and benefit.