Welcome to Predictive Modeling, Model Fitting, and Regression Analysis. In this course, we will explore different approaches in predictive modeling, and discuss how a model can be either supervised or unsupervised. We will review how a model can be fitted, trained and scored to apply to both historical and future data in an effort to address business objectives. Finally, this course includes a hands-on activity to develop a linear regression model.
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Data Science Fundamentals専門講座カリフォルニア大学アーバイン校（University of California, Irvine）このコースについて
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次における4の2コース
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英語
字幕：英語
学習内容
The application of predictive modeling to professional and academic work
Applications of classification analysis: decision trees
Applications of regression analysis (linear and logistic)
柔軟性のある期限
スケジュールに従って期限をリセットします。
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における4の2コース
中級レベル
約4時間で修了
英語
字幕：英語
提供：
カリフォルニア大学アーバイン校（University of California, Irvine）
Since 1965, the University of California, Irvine has combined the strengths of a major research university with the bounty of an incomparable Southern California location. UCI’s unyielding commitment to rigorous academics, cutting-edge research, and leadership and character development makes the campus a driving force for innovation and discovery that serves our local, national and global communities in many ways.
シラバス - 本コースの学習内容
1時間で修了
Predictive Modeling
Welcome to Module 1, Predictive Modeling. In this module we will begin with a comparison of predictive and descriptive analytics, and discuss what can be learned from both. We will also discuss supervised and unsupervised modeling, two foundational models in analytics and machine learning.
1時間で修了
1件のビデオ （合計3分）, 2 学習用教材
1時間で修了
Data Dimensionality and Classification Analysis
Welcome to Module 2, Data Dimensionality and Classification Analysis. In this module we will explore how data can be classified and how decision trees can be leveraged as a fast, easy to use a model that is easy to interpret, explain, and visualize.
1時間で修了
2 学習用教材
1時間で修了
Model Fitting
Welcome to Module 3, Model Fitting. In this module we will explore the concept of model fitting and how creating a generalized model that is able to fit both historical and future data is the ultimate goal. We will also review how a model can be trained or scored to apply to new and unlabeled data.
1時間で修了
1件のビデオ （合計3分）, 2 学習用教材
2時間で修了
Regression Analysis
Welcome to Module 4, Regression Analysis. In this module we will begin with an explanation of regression analytics, a popular technique used by data science professionals to make predictions. We will also discuss how achieving model fit is not a guarantee that a model can help solve a business problem, and how even a good model can sometimes lead to unactionable outcomes.
2時間で修了
2 学習用教材
レビュー
- 5 stars65%
- 4 stars17.50%
- 3 stars7.50%
- 2 stars5%
- 1 star5%
PREDICTIVE MODELING, MODEL FITTING, AND REGRESSION ANALYSIS からの人気レビュー
NAにより2022年1月27日
good course to understand the fundamentals of predictive analysis
MAにより2022年1月11日
course content is very concise and easy to understand
CHにより2021年6月26日
Thank you Very Much I learn a lot of Thing with all kinds of Predative Modeling that I can use.
Data Science Fundamentals専門講座について
This specialization demystifies data science and familiarizes learners with key data science skills, techniques, and concepts. The course begins with foundational concepts such as analytics taxonomy, the Cross-Industry Standard Process for Data Mining, and data diagnostics, and then moves on to compare data science with classical statistical techniques. The course also provides an overview of the most common techniques used in data science, including data analysis, statistical modeling, data engineering, manipulation of data at scale (big data), algorithms for data mining, data quality, remediation and consistency operations.
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