11-10, 10:15–11:00 (America/New_York), Central Park West (6th floor)
We are all trying to establish ourselves and stand out in the ever-competitive Data Science ecosystem. What if we could do it in a way that benefits both us as individuals and the Data Science community? This talk will discuss a range of ideas for building confidence and brand for novice and experienced Data Enthusiasts. Simply attending PyData NYC is a stepping stone for a beginner, whereas selecting to appear on a panel to discuss an open-source project could be an adventure for someone with more expertise. However, both pursuits put you on the correct path to achieving these goals. I will tell the story about my search for my place in the Data Science ecosystem, and I would like to assist you in finding yours. Building social capital doesn’t have to be a selfish endeavor. You should approach these opportunities with humility and honesty, and you should strive to build long-term, mutually beneficial relationships with the people you meet. Most importantly, be your most authentic self.
We all want to be more connected to the Data Science community while advancing our careers and building experience. During this talk, we will discuss simple steps you can take to begin this fun journey, as well as more advanced avenues you can pursue. Your journey has already started, TODAY!, right here at PyData NYC. There are numerous activities you may participate in to build your social capital and help the community grow. We will talk about building your local LinkedIn Network, sitting on panels, volunteering on NumFocus committees, and 12 other ideas. Building social capital doesn’t have to be a selfish endeavor. You should approach these opportunities with humility and honesty, and you should strive to build long-term, mutually beneficial relationships with the people you meet. Most importantly, be your most authentic self. After this talk, I hope you’ll be inspired to go out into the world and grow our community.
No previous knowledge expected
Dr. Gray is the Head of Data Science at KPMG Spark, where he builds predictive products with machine learning and Python that are reinventing how Bookkeepers work. He teaches Data Science and Data Analytics at Georgetown University. He is also a frequent volunteer and committee member for PyData, PyCon, and NumFocus. He is a core contributor and maintainer for the open-source software Project, Yellowbrick. He earned his Ph.D. from Johns Hopkins University, School of Medicine in Cellular and Molecular Physiology.