Welcome to Classification and Planned Experiments. This course will first contrast regression models with classification models, which have broad application in machine learning. It will then introduce basic classification techniques, focusing on K-nearest neighbor, and logistic regression. You will examine data visualizations and see how setting hyperparameters or estimating parameters supports interpretation and effective classification. The course will then address another powerful field of applied statistics called experimental design, which is concerned with running controlled tests (experiments) to try to understand causal relationships between factors of interest. Several types of designs will be introduced, including ones that use computer modeling. You will learn the principles of experimental design and work through several examples to help you understand how to actually set up, run and analyze these experiments leveraging data.
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您将学到什么
Learners will execute statistical classification techniques, apply experimental design principles & exhibit usage of approaches in causal learning.
您将获得的技能
- Applied Machine Learning
- Simulations
- Data Visualization
- Statistical Analysis
- Statistical Methods
- Supervised Learning
- Predictive Modeling
- Data Science
- Data Analysis Software
- Statistical Modeling
- Data Analysis
- Simulation and Simulation Software
- Statistical Inference
- Research Design
- Probability & Statistics
- Statistical Programming
- Logistic Regression
- Experimentation
要了解的详细信息
添加到您的领英档案
January 2026
2 项作业
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该课程共有2个模块
This Specialization covers the use of statistical methods in today's business, industrial, and social environments, including several new methods and applications. H.G. Wells foresaw an era when the understanding of basic statistics would be as important for citizenship as the ability to read and write. Modern Statistics for Data-Driven Decision-Making teaches the basics of working with and interpreting data, skills necessary to succeed in Wells’s “new great complex world” that we now inhabit. In this course, learners will gain an ability to execute basic classification techniques, including the use of R and Python; apply the principles of experimental design; and demonstrate usage of propensity scores, causal inference, and counterfactuals in causal learning.Learn more about the instructors who developed this course. Read the instructor bios and review the learning outcomes for the course.
涵盖的内容
3个视频3篇阅读材料1个作业
This module will focus on experiment design, fraction factorial design, and computer experiments. We will review a brief history of experiment design, and relevant terminology. We will review guidelines for conducting and analyzing experiments and applying design to computer models.
涵盖的内容
14个视频4篇阅读材料1个作业1次同伴评审
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状态：免费试用Arizona State University
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常见问题
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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