One of the principal responsibilities of a data scientist is to make reliable predictions based on data. When the amount of data available is enormous, it helps if some of the analysis can be automated. Machine learning is a way of identifying patterns in data and using them to automatically make predictions or decisions. In this data science course, you will learn basic concepts and elements of machine learning.

The two main methods of machine learning you will focus on are regression and classification. Regression is used when you seek to predict a numerical quantity. Classification is used when you try to predict a category (e.g., given information about a financial transaction, predict whether it is fraudulent or legitimate).

For regression, you will learn how to measure the correlation between two variables and compute a best-fit line for making predictions when the underlying relationship is linear. The course will also teach you how to quantify the uncertainty in your prediction using the bootstrap method. These techniques will be motivated by a wide range of examples.

For classification, you will learn the k-nearest neighbor classification algorithm, learn how to measure the effectiveness of your classifier, and apply it to real-world tasks including medical diagnoses and predicting genres of movies.

The course will highlight the assumptions underlying the techniques, and will provide ways to assess whether those assumptions are good. It will also point out pitfalls that lead to overly optimistic or inaccurate predictions.

One of the key insights from the science of happiness is that our own personal happiness depends heavily on our relationships with others. By tuning into the needs of other people, we actually enhance our own emotional well-being. The same is true within organizations: those that foster trusting, cooperative relationships are more likely to have a more satisfied, engaged—and more productive and innovative—workforce, with greater employee loyalty and retention.

This course delves into the social and emotional skills that sustain positive relationships at work. It highlights the foundational and related skills of empathy and “emotional intelligence,” also known as EQ, which refers to the skills of identifying and regulating our own feelings, tuning into the feelings of others and understanding their perspectives, and using this knowledge to guide us toward constructive social interactions.

Drawing on research and real-world case studies, the course reveals how honing these skills promotes well-being within an organization, supporting everything from good management—managers high in empathy, for example, have employees who report being happier and take fewer sick days—to more effective teamwork, problem solving, and recovery from setbacks. The course also explains the psychological and neuroscientific roots of cooperative, compassionate behaviors, making the case that these are not just “soft” skills but core aspects of human nature that serve basic human needs as well as the bottom line.

What’s more, it offers practical ways to strengthen empathy, trust, and collaboration among teams and resolve conflicts more constructively—with a special emphasis on how socially intelligent leadership can build cultures of belonging and engagement.

The course instructors are expert faculty from UC Berkeley’s Greater Good Science Center, Dacher Keltner, Ph.D., and Emiliana Simon-Thomas, Ph.D., whose earlier edX course, The Science of Happiness, has been a global phenomenon, inspiring a half million students worldwide. Here they take a central insight from that course—that our personal well-being is entwined with our social connections—and explain how to apply it to the modern workplace to create more productive, satisfying experiences at work.