Do More With Data We Have: Engineering Strategies To Match Good Data to Products

Presented by

Pierre Debois, Founder and CEO, Zimana LLC

About this talk

With a gigantic increase in data capturing capacity, corporations, government agencies, and research organizations face complexity in determining the best variables for their data models. Feature engineering techniques like exploratory data analysis are valuable for establishing the right variables for different statistical and machine learning models. In this session, attendees will learn the basic steps in R programming (and comparable techniques for Python) with use cases to apply these techniques in a sample training data model. Topics will be based on the following: R programming, R Studio, Python, Machine Learning, Data Science , APIs, Feature Selection/Feature Engineering, Exploratory Data Analysis, and Dimensionality Reduction. This session is meant for developers and managers using R or Python with an interest in statistical models or machine learning. The session will cover a few basic data type concepts. Prior experience with R or Python is not necessary.

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