The Record - Issue 19: Winter 2020

49 Programme participants complete virtual lessons over a 10-week period else. Consequently, most of the other parts of the business are relatively starved of people with the talent and skills needed to fully harness the value of data and inform decision-making in real time.” Led by practitioner-instructors over a 10-week period in a virtual classroom environment, General Assembly’s Data Scientist course is a project-based, intensive training programme designed to teach advanced data science tech- niques to data practitioners, such as analytics professionals working in core functions like sales and marketing, business operations, finance and planning, and product management. The 60-hour curriculum is primarily focused on Microsoft tools and technologies. “The course leverages a range of powerful tools – including Microsoft Power BI, Azure Machine Learning, SQL and open-source libraries in Python – to replicate the workflow of modern data science,” says Fennerty. “Participants learn best practices for data wrangling, data model- ling, visualisation and machine learning through a combination of live instruction, sandbox pro- ject work and capstone projects. Most of the capstone projects focus on helping participants to use their new data science skills to solve real business problems – for example one project identified an operational efficiency that has the potential to save the company up to $9 million.” Once they have graduated, participants are better equipped to carry out a range of different tasks and job roles. “The course is designed to help data practition- ers or aspiring data professionals to rapidly build a job-ready set of skills in data analysis, visualis- ation and predictive modelling for a variety of business needs,” says Fennerty. “Consequently, it is beneficial for individuals who already have experience of working with data and want to “Data-driven companies that invest in building data science capabilities are able to out- innovate their competitors” R YAN F ENNE R T Y, G ENE RA L A S S EMB LY