Our instructors are highly regarded in data science, coming from both academia and renowned companies.
Gain the skills and knowledge to use data science in your career and business, without breaking the bank.
Find training sessions offered on a wide variety of data science topics, from machine learning to data visualization to DevOps.
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Hugo Bowne-Anderson is Head of Data Science Evangelism and VP of Marketing at Coiled, a company that makes it simple for organizations to scale their data science and machine learning in Python. He has extensive experience as a data scientist, educator, evangelist, content marketer, and a data strategy consultant at DataCamp, the online education platform for all things data. He also has experience teaching basic to advanced data science topics at institutions such as Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and ODSC and with organizations such as Data Carpentry. He has developed over 30 courses on the DataCamp platform, impacting over 500,000 learners worldwide through his own courses. He also created the weekly data industry podcast DataFramed, which he hosted and produced for 2 years. He is committed to spreading data skills, access to data science tooling, and open-source software, both for individuals and the enterprise.
Data Science and Machine Learning At Scale(Tutorial)
Violeta has been working as a data scientist in the Data Innovation and Analytics department in ABN AMRO bank located in Amsterdam, the Netherlands. In her daily job, she works on projects with different business lines applying the latest machine learning and advanced analytics technologies and algorithms. Before that, she worked for about 1.5 years as a data science consultant in Accenture, the Netherlands. Violeta enjoyed helping clients solve their problems with the use of data and data science but wanted to be able to develop more sophisticated tools, therefore the switch. Before her position at Accenture, she worked on her PhD, which she obtained from Erasmus University, Rotterdam in the area of Applied Microeconometrics. In her research, she used data to investigate the causal effect of negative experiences on human capital, education, problematic behavior and crime commitment.
Explainable ML: Application of Different Approaches(Talk)
Matt currently leads instruction for GA’s Data Science Immersive in Washington, D.C. and most enjoys bridging the gap between theoretical statistics and real-world insights. Matt is a recovering politico, having worked as a data scientist for a political consulting firm through the 2016 election. Prior to his work in politics, he earned his Master’s degree in statistics from The Ohio State University. Matt is passionate about making data science more accessible and putting the revolutionary power of machine learning into the hands of as many people as possible. When he isn’t teaching, he’s thinking about how to be a better teacher, falling asleep to Netflix, and/or cuddling with his pug.
Good, Fast, Cheap: How to do Data Science with Missing Data(Half-Day Training)
Margriet is the global data science developer advocacy focal at IBM. As a Data Scientist she has a passion for exploring different ways to work with and understand diverse data by using open-source tools. She is active in developer communities through attending and presenting at conferences and organising meetups. She has a background as a climate scientist researching large observational datasets of carbon uptake by forests and the output of global scale weather and climate models.
Removing Unfair Bias in Machine Learning(Workshop)
Explain Machine Learning Models(Workshop)
Building Fair and Explainable AI Pipelines(Talk)
Michael Mitzenmacher is a Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. Michael
has authored or co-authored over 200 conference and journal publications on a variety of topics, including algorithms for the Internet, efficient hash-based data structures, erasure and error-correcting codes, power laws, and compression. He is interested in both algorithms for AI applications, and how predictors from AI systems can yield better algorithms with rigorous performance bounds. He is an ACM Fellow and an IEEE Fellow. He has a widely used textbook on randomized algorithms and probabilistic techniques in computer science published by Cambridge University Press.
Michael Mitzenmacher graduated summa cum laude with a B.A. in mathematics and computer science from Harvard in 1991. After studying
mathematics for a year in Cambridge, England, on the Churchill Scholarship, he obtained his Ph. D. in computer science at U.C. Berkeley in 1996. He then worked at Digital Systems Research Center until joining the Harvard faculty in 1999. He served as the chair for computer science from 2010 to 2013 and co-chair in the 2018-2019 academic year.
Algorithms with Predictions(Tutorial)
Olga is a deep learning R&D engineer at Scaleway, the second largest french cloud provider. She received her PhD in theoretical physics from Johns Hopkins University in 2013, followed by postdoctorate appointments at the Max Planck Institute in Dresden and the École Normale Supérieure in Paris. In the latter, she looked into the possible applications of artificial intelligence to quantum systems, among other things.
Olga’s current interests focus on semi-supervised and active machine learning. On the community side, she enjoys blogging about the latest advancements in AI both in and out of working hours. Some of her writing can be seen on medium.com/@olgapetrova_92798
Active Learning with a Sprinkle of PyTorch(Tutorial)
Olivier Grisel is a machine learning engineer at Inria. He is a member of the team of maintainers of the scikit-learn project. Scikit-learn is an Open Source machine learning library written in Python. His work is supported by the Fondation Inria and its partners.
Hands-on Machine Learning Engineer with scikit-learn(Full-Day Training)
Susana Zoghbi, Co-Founder & CEO, Macty. Susana is a researcher and entrepreneur in a quest to help businesses grow with Artificial Intelligence. She received a PhD in Computer Science and her research focused on cross-modal processing of textual and visual Information. She designed deep neural network architectures and probabilistic graphical models to understand visual and textual content from e-commerce and social media. Her work has been published in top conferences and journals in Artificial Intelligence. She has worked for NASA’s Frontier Development Lab as a Deep Learning Researcher to automatically search for long-period comets that might impact Earth. She has also worked for Microsoft Research in Cambridge, where she focused on machine learning for optimizing environments for large scale software development. Before her PhD, she obtained two Masters degrees, one in Mechanical Engineering from the University of British Columbia, where her research focused on human-robot interaction technologies, and one in Mathematical Physics, where she focused on gravitational fluctuations in Domain Wall Spacetimes. In 2014, she was granted a Google Anita Borg award for her contributions in Computer Science and her community.
A Deep Dive into Convolutional Neural Networks(Tutorial)
Professor Michael Huth (Ph.D.) is Co-Founder and CTO of the technology company XAIN and teaches at Imperial College London. His research focuses on Cybersecurity, Cryptography, Mathematical Modeling, as well as security and privacy in Machine Learning. He served as the technical lead of the Harnessing Economic Value theme at PETRAS IoT Cybersecurity Research Hub in the UK. In 2017, he founded XAIN AG together with Leif-Nissen Lundbæk and Felix Hahmann. The Berlin-based company aims to solve the challenge of combining AI with privacy with an emphasis on Federated Learning. XAIN won the first Porsche Innovation Contest and has already worked successfully with Porsche AG, Daimler AG, Deutsche Bahn, and Siemens.
Professor Huth studied Mathematics at TU Darmstadt and obtained his Ph.D. at Tulane University, New Orleans. He worked at TU Darmstadt, Kansas State University and spent a research sabbatical at The University of Oxford. Huth has authored several scientific publications and is an experienced speaker on international stages.
Federated Learning: AI for the Privacism Movement(Workshop)
Wioletta Stobieniecka is a Data Scientist with over 5 years of experience. Open-minded individual with deep passion for knowledge discovery and solving real-world problems using advanced statistical and machine learning tools. Strongly self-motivated and eager to constant development of skills including both technical and social with major in analytics. Deep theoretical and practical knowledge of statistical learning methods. Inquisitive mind searching for optimal solutions and ready to demonstrate own initiative and ambition to gain experience across a variety of industries. Having experience in fraud analytics with strong focus on building predictive models for banking and insurance. Looking for opportunities with exposure to projects involving development of recommender systems and image recognition. Striving to find strong mentorship and inspiring environment with well-rounded people open to knowledge transfer. Having long exposure to both open source and commercial software (R, Python, SAS, Oracle).
AI Assisting in Traffic Relief(Tutorial)
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