This course, a component of University’s certification program, aims to equip participants with the requisite skills to effectively utilize our Driverless AI tool. Jonathan Farinela, Solutions Engineer at, will emphasize the crucial role of data quality in achieving successful outcomes, while also elucidating the principles and procedures of data preparation.


The course is divided into two main sections:

  • In the initial section, participants will delve into the importance of the tabular format in classical machine learning. They will also grasp the distinction between supervised and unsupervised learning, along with common methodologies like classification and regression. The significance of defining the unit of analysis in dataset construction will be highlighted. Moreover, participants will witness demonstrations of data preparation within Driverless AI, showcasing its ability to automate preprocessing tasks and allow customization using Python code.

  • Transitioning to the second section, the course will concentrate on time series data preparation. Fundamental aspects of time series problems will be explored, including the necessity of a date column and understanding the autoregressive nature of such data. The course will also address challenges associated with handling multiple series within a dataset and provide best practices for improving model performance. Jonathan will exemplify dataset preparation and splitting techniques tailored for time series analysis using the capabilities of Driverless AI.

Enjoy the learning journey!

Course access Certificate Certificate

Jonathan Farinela, Solutions Engineer

Statistician with over a decade of experience in analytics and data science, primarily working in Research and Development, also has experience with Demand Forecast for retail and CRM for Financial Services. Last 5+ years focusing on helping customers during pre and post sales steps, leading and conducting Proof of Values (POV) with AI and ML projects, educating, enabling, and driving AI solutions for business problems, from data to value always aiming ROI and financial impact for the business.