AI Workflow: Data Analysis and Hypothesis Testing
Master advanced data analysis techniques for AI workflows, including exploratory analysis, hypothesis testing, and statistical inference.
Master advanced data analysis techniques for AI workflows, including exploratory analysis, hypothesis testing, and statistical inference.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full IBM AI Enterprise Workflow Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.2
(106 ratings)
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Instructors:
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What you'll learn
Create and implement effective data visualization strategies
Apply statistical methods for hypothesis testing
Handle missing data using various imputation techniques
Develop dashboards in IBM Watson Studio
Conduct exploratory data analysis for AI workflows
Skills you'll gain
This course includes:
0.7 Hours PreRecorded video
7 quizzes, 2 peer reviews
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 2 modules in this course
This comprehensive course focuses on data analysis and hypothesis testing within AI enterprise workflows. Students learn exploratory data analysis techniques, data visualization best practices, and statistical testing methods. The curriculum covers probability distributions, null hypothesis significance testing, and strategies for handling missing data. Through hands-on case studies using IBM Watson Studio, learners develop practical skills in creating dashboards and conducting multiple testing analyses.
Data Analysis
Module 1 · 5 Hours to complete
Data Investigation
Module 2 · 5 Hours to complete
Fee Structure
Instructors
Digital Content Delivery Lead at IBM with Extensive Experience in Information Technology Education
Mark J. Grover is a Digital Content Delivery Lead at IBM, specializing in the creation and delivery of online educational content. Before joining IBM, he was a full-time professor of computer technology at Cape Fear Community College in Wilmington, NC, where he coordinated the Information Security program and taught various courses including Computer Security and Network Administration. Grover has over 25 years of experience in information technology and has received accolades such as the Cisco Instructor of Excellence award and the Award for Excellence in Innovation from the University of North Carolina Wilmington. He is passionate about outdoor activities like camping and mountain biking, and enjoys spending time with his family.
Data Science Curriculum Leader at IBM
Dr. Ray Lopez is a seasoned technical and educational expert with over 30 years of experience in software development, system administration, and research in neuroscience and artificial intelligence. Currently serving as the Data Science Curriculum Leader at IBM, he focuses on developing education and certification programs in data science. Dr. Lopez has a rich background as a university lecturer, teaching subjects such as science, mathematics, statistics, and philosophy. His extensive work includes leading initiatives to create comprehensive training programs that equip professionals with the necessary skills to thrive in the field of data science. He has contributed to various online courses on platforms like Coursera, including topics such as AI workflows and machine learning model deployment. Dr. Lopez holds a Ph.D. in Experimental Physiological Psychology from the University of Texas at Arlington, where his dissertation explored critical thinking interventions in online learning environments. His multifaceted expertise positions him as a significant contributor to advancing data science education and practice within IBM and beyond.
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4.2 course rating
106 ratings
Frequently asked questions
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