AI Workflow: Data Analysis and Hypothesis Testing

Overview

Embark on the next step in your journey towards mastering artificial intelligence within large enterprises with the "AI Workflow:

Data Analysis and HypedIdentify the importance of hypothesis testing in exploratory data analysis and how to address the challenges of multiple testing with effective strategies. This course is not designed for beginners but rather for data science practitioners with a background in building machine learning models, looking to enhance their expertise in AI deployment in big companies.

Prerequisites include completion of the first course in the IBM AI Enterprise Workflow Certification specialization, a fundamental understanding of Linear Algebra, familiarity with probability theory and distributions, a grasp of both descriptive and inferential statistics, along with a practical understanding of machine learning concepts. Additionally, proficiency in Python and familiarity with tools such as NumPy, Pandas, matplotlib, scikit-learn, and IBM Watson Studio, as well as an understanding of the design thinking process, are expected.

Offered through Coursera, this course is part of a series that focuses on artificial intelligence, Python programming, machine learning, and data analysis.

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Taught by

Mark J Grover and Ray Lopez, Ph.D.

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Subjects