Alan is co-founder and CTO of Rasa, the leading open source conversational AI company. Rasa builds software that enables developers to build conversational software that really works, and is trusted by thousands of developers in enterprises worldwide. Rasa combines applied AI research with enterprise-ready technology. Alan holds a PhD in machine learning from the University of Cambridge and has years of experience building AI-powered products in industry.
Sergey began his career as an academic at Dartmouth College, where he researched the neural bases of visual category learning and obtained his Ph.D. in Cognitive Neuroscience. After leaving academia, Sergey got into the rapidly growing startup scene in the NYC metro area, where he has worked as a data scientist in digital advertising, cybersecurity, finance, and media. He is heavily involved in the NYC-area teaching community and has taught courses at various bootcamps, and has been a volunteer teacher in computer science through TEALSK12.
Matthew is an open source software developer focusing on efficient computation and parallel computing, primarily within the Python ecosystem. He has contributed to many of the PyData libraries and today works on Dask, a framework for parallel computing. Matthew holds a PhD in computer science from the University of Chicago where he focused on numerical linear algebra, task scheduling, and computer algebra.
Justin Bois is a lecturer in the Division of Biology and Biological Engineering at the California Institute of Technology. He teaches nine different classes there, nearly all of which heavily feature Python. He is dedicated to empowering students in the biological sciences with quantitative tools, particularly data analysis skills. Beyond biologists, he is thrilled to develop courses for DataCamp. In this DataChat, Hugo and Justin discuss different aspects of data science, including data science education, and Justin shares his advice to those getting started in the field.
Katharine Jarmul runs a data analysis company called kjamistan that specializes in helping companies analyze data and training others on data analysis best practices, particularly with Python. She has been using Python for 8 years for a variety of data work -- including telling stories at major national newspapers, building large scale aggregation software, making decisions based on customer analytics, and marketing spend and advising new ventures on the competitive landscape.
Jason Myers is a software engineer and author. His area of expertise is in developing data analytics platforms. He has also written the Essential SQLAlchemy book, co-authored with Rick Copeland, that introduces you to working with relational databases in Python.
Dan Becker is a Data Scientist with expertise in deep learning. He has contributed to the Keras and Tensorflow libraries for deep learning, finished 2nd (out of 1353 teams) in the $3 million Heritage Health Prize data mining competition, supervised data science consulting projects for 6 companies in the Fortune 100 and taught deep learning workshops at events and conferences such as ODSC.
Here, Daniel talks about his upcoming book, whether to start learning python or R for data science, the best paths to becoming a data scientist and much more. Daniel is a Software Carpentry instructor and a doctoral student in Genetics, Bioinformatics, and Computational Biology at Virginia Tech, where he works in the Social and Decision Analytics Laboratory under the Biocomplexity Institute. He received his MPH at the Mailman School of Public Health in Epidemiology and is interested in integrating hospital data in order to perform predictive health analytics and build clinical support tools for clinicians. An advocate of open science, he aspires to bridge data science with epidemiology and health care.
Andy is a lecturer at the Data Science Institute at Columbia University and author of the O'Reilly book "Introduction to machine learning with Python", describing a practical approach to machine learning with python and scikit-learn. He is one of the core developers of the scikit-learn machine learning library, and he has been co-maintaining it for several years. He is also a Software Carpentry instructor. In the past, he worked at the NYU Center for Data Science on open source and open science, and as a Machine Learning Scientist at Amazon. His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms. Here, Andy answers questions about his work at Columbia, gives advice to people starting with data science and answers what the most difficult part of his job is.
Peter is a co-founder of DrivenData. He earned his master's in Computational Science and Engineering from Harvard’s School of Engineering and Applied Sciences. His work lies at the intersection of statistics and computer science, and he wants to help bring powerful new modeling techniques to the organizations that need them most. He previously worked as a software engineer at Microsoft and earned a BA in philosophy from Yale University. Here, Peter and Hugo discuss why use python for data science, the business case for data, DrivenData competitions on using yelp data to predict restaurant sanitary ratings and much more.
Dhavide Aruliah is Director of Training at Anaconda, the leading Open Data Science platform powered by Python. Dhavide was previously an Associate Professor at the University of Ontario Institute of Technology (UOIT). He served as Program Director for various undergraduate & postgraduate programs at UOIT. His research interests include computational inverse problems, numerical linear algebra, & high-performance computing. Together with Hugo, Dhavide goes over the process of designing a course, his work at Anaconda, his path to Python and more.
Ben is a machine learning specialist and the director of research at lateral.io. He is passionate about learning and has worked as a data scientist in real-time bidding, e-commerce, and recommendation. Ben holds a PhD in mathematics and a degree in computer science.
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