Two 3-Day Remote Workshops on Marketing Analytics and Machine Learning
MARKETING ANALYTICS, DATA HANDLING AND VISUALIZATION IN R
Workshop: Thursday, May 25, 2022 – Saturday, May 27, 2023.
Visit the link for details: https://dataorbit.ca/course/10
This hands-on workshop focuses on how to accomplish everyday tasks that statistical analysis depends on, such as getting data into R, data handling, presenting summary tables, visualizing, and building marketing analytics models, and transforming output into reproducible reports and elegant graphs. Marketing analytics models include (a) traditional market response models to identify linear and non-linear relationships between variables (b) choice models that are widely used for the analysis of individual choice behavior across a dozen disciplines for policy and tactical interest, (c) market segmentation and random forest classifier for focused targeting, and (d) perceptual mapping for brand positioning.
Live Lecture and Lab Session: 10 am–3:00 pm Eastern Time (New York Time) each day with one hour break at noon. Video-recorded versions will be accessible for 8 weeks.
Payment: The fee of Canadian $700 Regular, $500 for Students.
MACHINE LEARNING FOR PREDICTION AND CAUSAL INFERENCE IN R
Workshop: Thursday, June 1 – Saturday, June 3, 2023.
Visit the link for details: https://dataorbit.ca/course/12
This workshop will provide the foundations of machine learning procedures (MLPs) that are used mainly for predictions and cover recent developments of MLPs that can be used for causal inference and hypotheses testing from observational data. The technological revolution of the internet has generated a repository of digital observational data as a byproduct, and this revolution has been complemented by an influx of MLPs from computer science, leading to an emerging new field of computational social science. In the social sciences, empirical analyses typically estimate the causal effect of implementing a policy, changing a price, running an advertisement, or introducing new products. The objective here is the integration of prediction and explanation into a data driven computational social science. Researchers will learn about the process and implicit flow of action behind the MLPs; the ability to build models for predictions and causal effects for policy decisions and to explore and visualize novel and robust non-linear and interdependent patterns that may aid in theory testing and theory building by blending data and theory.
Live Lecture and Lab Session: 10 am–3:00 pm Eastern Time (New York Time) each day with one hour break at noon. Video-recorded versions will be accessible for 8 weeks.
Payment: The fee of Canadian $700 Regular, $500 for Students.
Both Workshops are Taught by Towhid Islam, PhD., Professor, Department of Marketing and Consumer Studies, University of Guelph, Canada.
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Towhidul Islam
University Research Leadership Chair & Professor
University of Guelph
Guelph ON
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