Machine Learning for Data Analysis
Gain expertise in predictive algorithms and machine learning techniques for data analysis, utilizing Python or SAS to enhance your analytical skills.
Gain expertise in predictive algorithms and machine learning techniques for data analysis, utilizing Python or SAS to enhance your analytical skills.
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 Data Analysis and Interpretation 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
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English
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What you'll learn
Apply decision trees and random forests for predictive modeling
Implement lasso regression for variable selection and prediction
Perform k-means cluster analysis for data segmentation
Evaluate model performance using cross-validation techniques
Develop practical machine learning solutions in Python or SAS
Skills you'll gain
This course includes:
2.27 Hours PreRecorded video
1 peer review per module
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course introduces learners to machine learning concepts and techniques for data analysis. Students explore various algorithms including decision trees, random forests, lasso regression, and k-means clustering. Through hands-on practice with Python or SAS, learners develop skills in building and evaluating predictive models. The course emphasizes practical application, featuring real-world examples and peer-reviewed assignments to reinforce learning.
Decision Trees
Module 1 · 4 Hours to complete
Random Forests
Module 2 · 2 Hours to complete
Lasso Regression
Module 3 · 2 Hours to complete
K-Means Cluster Analysis
Module 4 · 2 Hours to complete
Fee Structure
Instructors
Statistics Education Innovator Advancing Data-Driven Research Method
Jennifer Rose serves as Director of the Center for Pedagogical Innovation and Professor of the Practice at Wesleyan University's Quantitative Analysis Center. Along with colleague Lisa Dierker, she co-developed the "Passion-Driven Statistics" curriculum, which has transformed statistics education through project-based learning approaches. Her innovative teaching methods have earned multiple National Science Foundation grants to disseminate their educational model nationwide. As Director of the Institutional Review Board and Professor of the Practice in the Center for Pedagogical Innovation, she has significantly influenced how statistics is taught to undergraduate students. Her course "Intro to Statistical Consulting" provides students with real-world experience by connecting them with nonprofits for data analysis projects. Her work has extended the reach of this innovative teaching approach to thousands of students globally through online platforms and institutional partnerships.
Pioneering Statistician Revolutionizing Undergraduate Data Science Education
Lisa Dierker, Professor of Psychology at Wesleyan University, has transformed statistical education through her innovative "Passion Driven Statistics" curriculum. Her expertise spans chronic disease epidemiology and advanced statistical methods, leading to significant National Science Foundation funding for developing accessible, project-based teaching approaches. Her collaborative work across disciplines including public health, medicine, engineering, and neuroscience has produced groundbreaking research while making statistics more engaging for diverse student populations. Through her leadership in curriculum development and cross-disciplinary research, she has significantly influenced how quantitative methods are taught at the undergraduate level, while maintaining active research in epidemiology and public health.
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4.2 course rating
322 ratings
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