What we're about

Welcome to Data Science Dojo's Meetup group. Our goal is to help connect other like-minded business professionals who are interested in teaching, learning, and sharing their knowledge and understanding of data science to a larger community.

We encourage all members of this group to be pro-active in leading discussions on topics related to data science like machine learning, artificial intelligence, predictive analytics, big data, and IoT, as well as programming languages such as R, Hadoop, and Python.

Stay tuned to our Meetup calendar for future community events and be sure to follow us on Twitter at @DataScienceDojo. Also, be sure to visit our data science bootcamp (https://datasciencedojo.com/data-science-bootcamp/) for more information about our training.

Upcoming events (3)

Automating Supervised Machine Learning Pipeline Development

All the other STEM arts have well-established methodologies. Is there such a set of methodologies for data science and machine learning? YES! Join Thom and Ghaith as they walk you through a high-level coverage of these methods.

Presenter Bios:
Thom Ives founded Integrated Machine Learning & AI, which is a very large group of data scientists that seek to grow and learn MORE TOGETHER. He is a leading data scientist and has developed a wide range of analytical models using multi-physics, data, and experiments.

Ghaith Sankari has an AI bachelor’s degree from the University of Aleppo and he is the founder of AI-HUT (Dubai, UAE). He is also a mentor in AI4Medicine Specialisation, deep learning.ai, and a data scientist, Executive data science, Coursera (offered by Johns Hopkins University).

Causal Behavioral Modeling Framework-Discrete Choice Modeling of Consumer Demand

There are increasing demands for "causal ML models" of the agent behaviors, which enable us to unbox the complex black-box models and make inferences or do proper counterfactual simulations. Many applications (especially in Marketing) intrinsically call for measurement of the causal impact of the product, pricing, and promotion decisions beyond predictions, either from observational data or combinations of experimental and observational data.

Discrete choice modeling of agent behaviors is a generative modeling framework, created by an Economist, Daniel McFadden (Nobel Prize, 2000). This has been a work-horse model in Economics, Marketing Science, and Operation Research. However, this modeling framework is less known to ML/AI researchers outside of Computational Social Science. In this talk, I will introduce discrete choice models of agent behaviors with a focus on consumer demand modeling. I will talk about two different ways of modeling consumer heterogeneity: discrete vs. continuous. In addition, how this individual-level model (i.e. varying parameters at the individual level) can be estimated by using simulated individuals when you only have aggregate sales data is also discussed. A dynamic version of this model is related to reinforcement learning, and I will discuss this linkage. Finally, an extension of this model to consumer online search behaviors and a neural network representation of discrete choice models will be discussed.

Presenter Bio
Minha Hwang is a Principal Architect at Microsoft. He is a Marketing Data Scientist, who focuses on causal inference, consumer behaviour modelling, marketing/product impact measurement, and explainable AI (xAI), and prescriptive data science. He likes to explore new and different areas: Double Ph.D. - Marketing Science from UCAL Anderson and Materials Science & Engineering from MIT, former academic now in practice - Former Assistant Professor of Marketing at McGill University in Canada, a management consultant who worked both as a generalist and a specialist, project-based data science role and SaaS Marketing & Sales solution data science role - especially in pricing, promotion ROI, assortment, purchase structure, digital marketing, CRM and CLV. He has published his research and applied works in Marketing Science, Information Systems, Operations Research, Economics, and Applied Physics journals.

Getting High-Quality Data for Your Computer Vision Models

Thought leaders in the AI space such as Andrew Ng have been advocating for a shift from model-centric to data-centric AI. The idea behind this campaign is that AI models can be only marginally improved through tweaks in the algorithm but considerable change can only be achieved by using high-quality data. However, what does "high-quality data" mean and how do we go about ensuring the quality, diversity, and consistency of our dataset? In this talk, we will discuss the practice of collecting and annotating data for your computer vision models and making sure the dataset you are using is representative and free of harmful biases.

Presenter Bio:
Iva Gumnishka is the founder and CEO of Humans in the Loop, a professional data collection and annotation company focused on building high-quality datasets for computer vision applications. The company is a social enterprise and its mission is to provide dignified work opportunities to refugees and conflict-affected people through annotation projects. Iva holds a degree in Human Rights from Columbia University and she was named Forbes 30 under 30 in 2018.

Past events (70)

Introduction to Deep Learning with PyTorch

Online event

Photos (116)