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Creating a Data-Driven Workforce with Blended Learning

Bloomberg’s Global Data Technical Trainer, Sheil Naik, breaks down how a blended learning environment can catalyze a data-driven workforce.


As organizations produce more data and digitize products and processes, a data-driven workforce has never been more critical. This is why learning and development has become central to business strategies, especially initiatives focused on building data science capabilities across organizations.

Next week, DataCamp’s Data Science Evangelist, Adel Nehme, will host a webinar with Sheil Naik, Global Data Technical Trainer at Bloomberg, on how Bloomberg uses blended learning to teach data analysis with Python, and how these analysis techniques can be used to evaluate behavioral change and the success of upskilling initiatives.

Blended learning at Bloomberg

A data-driven workforce at Bloomberg uses data science to make data-driven business decisions, improves processes with data, and creates data-driven news stories. Sheil Naik leads a company-wide blended learning program focused on Data Analysis with Python which incorporates 12 to 20 hours of learning via DataCamp per quarter, three live 1.5-hour classroom sessions led by technical experts, and a final project using Bloomberg data as the final deliverable.


The webinar will dive deep into the benefits of blended learning. The combination of DataCamp courses and virtual in-person classroom sessions allows for greater consistency and flexibility across regional and scheduling constraints and increases the capacity of class sizes across the world.

Evaluating upskilling success

A methodology for evaluating direct return on investment for training programs is the Kirkpatrick Model of Evaluation. In brief, the Kirkpatrick Model proposes four different evaluation levels: the initial reaction following a training program, learning evaluation, behavioral change, and the business impact of gained skills.


In the webinar on November 4, Sheil will deep dive into how Bloomberg operationalized the Kirkpatrick Model of Evaluation by measuring and evaluating post-training behavioral change for all learners in the program.

Reap the benefits of data upskilling

This only scratches the surface of Adel’s and Sheil’s discussion on creating a data-driven workforce. To find out more about how your organization can scale personalized learning, evaluate training success, and tie training to business objectives, join our webinar on November 4th.