Python is a very versatile, high-level programming language. It has a generous standard library, support for multiple programming paradigms, and a lot of internal transparency. If you choose, you can peek into lower layers of Python and modify them – and even modify the runtime on the fly as the program executes.
Tristan Bergh is a systems thinker and data scientist living in Cape Town, South Africa. He studied aeronautical engineering before working as an enterprise software engineer. He worked as an architect on AFIS implementations in Southern Africa, moving into middleware for large fin-tech systems. He restarted his system modeling and machine learning consulting services through 2014, delivering predictive analytics models in healthcare and other domains.
Ankit Gupta is a data scientist at Crisis Text Line, a New York-based not-for-profit tech startup providing 24/7 free text-based crisis support to individuals. At Crisis Text Line, Ankit developed an AI-driven triage system that detects signals of suicidality within the first few messages sent by texters. Using the triage system, the counselors can serve 93% of at-risk texters in under five minutes.
Genevieve Patterson is the chief scientist at TRASH, a startup that is developing computational filmmaking tools for mobile iphono-graphers. Before that, she was a Postdoctoral Researcher at Microsoft Research New England. Her work is about creating dialog between AI and people. Her interests include video understanding, visual attribute discovery, human-in-the-loop systems, fine-grained object recognition, medical image understanding, and active learning. Genevieve received her Ph.D. from Brown University in 2016 under the direction of James Hays.
Peadar Coyle is a Data Scientist and Engineer based in London. He regularly speaks at conferences and has written a book consisting of numerous interviews with Data Scientists throughout the world. He is also a passionate Open-Source evangelist, himself a supporter who has contributed to PyMC3. Most recently, he founded a stealth startup that is working on hyper-personalized audio.
Nick Gaylord has worked as a data scientist in the Bay Area for about the last 5 years. Currently, he’s a member of the Johnson & Johnson Health Technology team, and prior to that, he has worked in different fields ranging from small business revenue analytics to enterprise machine-learning-as-a-service platforms. Like many data scientists, he started out as an academic before transitioning to industry, in his case earning a Ph.D. in Psycholinguistics from the University of Texas at Austin in 2013.
Conor Dewey is a data scientist at Squarespace who spends his time thinking about growth and engagement. He frequently shares insights from his experience interviewing at top companies. More generally, he offers resources and advice on Medium and Github in the hopes of ‘open sourcing’ his journey to data science. Conor also manages a weekly data science newsletter with 1000+ subscribers.
Martina is a physicist and works as Data Science Lead at Mallzee, based in Edinburgh (Scotland). She loves looking at data regardless of the topic and area and believes the most enjoyable thing in Data Science is analyzing your data, finding the one you need for your question, and producing facts out of it. Throughout her education and job experience, she worked with data in epidemic dynamics, linguistics, and fashion. She also loves producing hand-crafted data visualizations and keeps studying and improving, whether it’s about Machine Learning or leadership topics.
This semester, I’m taking a graduate course called Introduction to Big Data. It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data science community, I will recap the materials I will learn from the class in Medium. Having a solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects.