Predictive Modeling, Model Fitting, and Regression Analysis
Master predictive modeling techniques including supervised learning, classification analysis, and regression models. Ideal for data science professionals.
Master predictive modeling techniques including supervised learning, classification analysis, and regression models. Ideal for data science professionals.
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 Science Fundamentals 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.
4.4
(59 ratings)
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What you'll learn
Apply predictive modeling techniques to real-world problems
Develop supervised and unsupervised learning models
Implement classification analysis using decision trees
Create and evaluate regression models
Optimize model fitting for historical and future data
Skills you'll gain
This course includes:
0.1 Hours PreRecorded video
2 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores predictive modeling techniques and their practical applications. Students learn about supervised and unsupervised modeling approaches, classification analysis using decision trees, and regression analysis methods. The curriculum covers model fitting concepts, training processes, and how to apply models to both historical and future data. Through hands-on activities, learners develop practical skills in creating and evaluating linear regression models for business applications.
Predictive Modeling
Module 1 · 43 Minutes to complete
Data Dimensionality and Classification Analysis
Module 2 · 40 Minutes to complete
Model Fitting
Module 3 · 43 Minutes to complete
Regression Analysis
Module 4 · 2 Hours to complete
Fee Structure
Instructor
Assistant Director of Technology Programs at UC Irvine
Julie Pai is an accomplished professional with over 10 years of experience in technology education programs. Her journey began in biology and clinical research, which sparked her interest in statistical analysis and data analysis. As the Assistant Director of Technology Programs at the University of California, Irvine, Julie collaborates with industry professionals to create, launch, and manage a variety of technology courses, including Data Science, Data Analytics, Cloud Computing, and Machine Learning. She is proficient in several programming languages and tools such as Tableau, SQL, MySQL, Python, Spark, Hive, and Scala. Julie's dedication to education is reflected in her efforts to develop engaging curricula that equip students with the skills needed to thrive in the technology sector. Through her work, she plays a vital role in bridging the gap between academia and industry, fostering a new generation of data-driven professionals.
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4.4 course rating
59 ratings
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