Data Science for Business

Course overview

The amount of information that humans can manually handle is limited. Therefore, business leaders have traditionally made decisions based on a small set of data. As the amount of data gets larger in the Technovate Era, a different way of solving problems by utilizing computers and algorithms is becoming more common. Artificial intelligence (or machine learning) plays a central role in this new technique.

If we go back in time, the civil war period in Japan saw a revolution in the way battles were fought. Swords were replaced and outperformed by guns. AI (or machine learning) has a similar impact and the crucial question for modern business leaders is how to apply it to their advantage.

In this course, students will learn the essentials of data science that can help business leaders to make efficient use of AI (machine learning). Throughout the course, you’ll get hands-on experience with data processing and machine learning by operating a state-of-the-art machine learning automation tool, DataRobot.

Specifically, students will explore some of the most popular machine learning algorithms and use them to build predictive models for various types of data. Traditionally, data science, especially deep learning and other current mainstream machine learning methods, has required mastery of programming languages such as Python and R. This has been a hurdle for business leaders who are not from engineering backgrounds. By introducing DataRobot, we hope to lower this hurdle to the extreme, allowing more business leaders to experience machine learning and pave the way for the applications of AI in their businesses.

By working hands-on and experiencing the analytical process of data science, we aim for the following:

– Understand what data science can and cannot do, and be able to set appropriate issues/challenges in applying data science to business.
– Understand the typical types of machine learning algorithms and be able to design problem-solving methods using algorithms.
– Be able to ask the right questions to a data scientist and interpret the key results.