Welcome, you're now starting our course on Inclusive Data Collection and Analysis. This is the second course in the Gender Analytics Specialization. While you're free to take this course on its own, you'll get a lot more out of it if you take it as part of the five course series. If you want to learn more about the whole specialization, view the welcome video in the first course on Gender Analytics. If you do the whole series you'll gain access to the capstone project, where you'll get a chance to practice all of the gender analytics skills. I'm Sarah Kaplan, and I'm a distinguished professor and director of the Institute for Gender in The Economy or GATE, at the University of Toronto. I'll be continuing as your guide through this specialization. We've designed the Gender Analytics Specialization to develop your skills in incorporating gender based insights into policy, product, service and process design. If you took our first course on understanding gender, you'll know that a lot of things we think of as gender neutral actually have gendered outcomes. And these outcomes can be even more biased if we look at important intersections with race, indigeneity, differences in ability, ethnicity, sexual orientation, and other identities. Snowplowing, financial investment tools, car safety or facial recognition, though they may not have been designed with gendered impacts in mind, they all end up affecting different genders differently. The question is, how specifically can you uncover these effects? What data do you need? How do you collect it? And importantly, how can you collect it without creating risks for the people whose data you're collecting? In this course, you're going to learn from three fantastic faculty members who are going to help you to, first develop legal, ethical, and respectful approaches for collecting, accessing and storing data. Second, learn to question the sources of quantitative data and analyze it effectively so you don't come to false conclusions. Specifically, you'll learn to think beyond averages to generate insights from disaggregating data, and you'll find ways to identify and focus on under represented or underserved groups. Finally, you will learn to do participatory community engagement to collect and analyze qualitative data. And you will develop ways to think about the relationship between qualitative and quantitative data. By the end of this course, you will understand that good analysis must be preceded by thoughtful data collection, whether it is qualitative or quantitative. Sensitive and ethical engagement with the communities you wish to serve will be essential, both for getting real insight and for overcoming structural biases in systems. And you learn about pitfalls in your analytical strategies that might lead you to biased or faulty conclusions. I hope you enjoy the course.