Instructors
Dr. Raphael Bahati
CEO & FounderE-mail: rbahati@bahatiacademy.com
“Students will learn how to visualize and analyze continuous and categorical data from various domains, using modern data science tools. Concepts of distributions, sampling, estimation, confidence intervals, experimental design, inference, correlation will be introduced in a practical, data-driven way.”
“Covers three basic concepts of data science together with the corresponding techniques: Sampling to estimate properties of a population (Bootstrap), random assignment and experiments to make causal inferences (randomization test), and model selection to enable good predictions (cross-validation). Emphasizes practical data handling and programming skills in Python.”
“Basic principles of machine learning (estimation, optimization, prediction, generalization, bias-variance trade-off, regularization) in the context of supervised (linear models, decision trees, deep neuronal networks) and unsupervised (clustering and dimensionality reduction) statistical learning techniques. The course emphasizes the ability to apply techniques to real data sets and critically evaluate their performance.”
“Quantitative Reasoning: Math Foundations (Arithmetric and number properties, Ratios and math formulas, Algebra, Statistics, Counting methods and probability, Geometry), Quantitative Comparisons, Problem Solving, and Data Interpretation.”
“An introduction to the methods and techniques used in the study of human social behavior. Students will conduct studies using a variety of procedures, and will develop an independent research proposal.”
“An introduction to the research process and ethical dilemmas faced by sociologists, as well as a survey of the essential procedures used to collect sociological data, e.g. sampling, questionnaire design, and observational field research techniques.”
E-mail: rbahati@bahatiacademy.com
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