このコースについて
4.2
219件の評価
49件のレビュー

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スケジュールに従って期限をリセットします。

約12時間で修了

推奨:3 hours/week...

英語

字幕:英語

習得するスキル

Data AnalysisPython ProgrammingMachine LearningExploratory Data Analysis

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。

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。

約12時間で修了

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英語

字幕:英語

シラバス - 本コースの学習内容

1
5時間で修了

Decision Trees

In this session, you will learn about decision trees, a type of data mining algorithm that can select from among a large number of variables those and their interactions that are most important in predicting the target or response variable to be explained. Decision trees create segmentations or subgroups in the data, by applying a series of simple rules or criteria over and over again, which choose variable constellations that best predict the target variable....
7件のビデオ (合計40分), 15 readings, 1 quiz
7件のビデオ
Machine Learning and the Bias Variance Trade-Off6 分
What Is a Decision Tree?5 分
What is the Process of Growing a Decision Tree?4 分
Building a Decision Tree with SAS9 分
Strengths and Weaknesses of Decision Trees in SAS4 分
Building a Decision Tree with Python9 分
15件の学習用教材
Some Guidance for Learners New to the Specialization10 分
SAS or Python - Which to Choose?10 分
Getting Started with SAS10 分
Getting Started with Python10 分
Course Codebooks10 分
Course Data Sets10 分
Uploading Your Own Data to SAS10 分
Data Set for Decision Tree Videos (tree_addhealth.csv)10 分
SAS Code: Decision Trees10 分
CART Paper - Prevention Science10 分
Python Code: Decision Trees10 分
Installing Graphviz and pydotplus10 分
Getting Set up for Assignments10 分
Tumblr Instructions10 分
Assignment Example10 分
2
3時間で修了

Random Forests

In this session, you will learn about random forests, a type of data mining algorithm that can select from among a large number of variables those that are most important in determining the target or response variable to be explained. Unlike decision trees, the results of random forests generalize well to new data....
4件のビデオ (合計25分), 4 readings, 1 quiz
4件のビデオ
Building a Random Forest with SAS7 分
Building a Random Forest with Python6 分
Validation and Cross-Validation7 分
4件の学習用教材
SAS code: Random Forests10 分
The HPForest Procedure in SAS10 分
Python Code: Random Forests10 分
Assignment Example10 分
3
3時間で修了

Lasso Regression

Lasso regression analysis is a shrinkage and variable selection method for linear regression models. The goal of lasso regression is to obtain the subset of predictors that minimizes prediction error for a quantitative response variable. The lasso does this by imposing a constraint on the model parameters that causes regression coefficients for some variables to shrink toward zero. Variables with a regression coefficient equal to zero after the shrinkage process are excluded from the model. Variables with non-zero regression coefficients variables are most strongly associated with the response variable. Explanatory variables can be either quantitative, categorical or both. In this session, you will apply and interpret a lasso regression analysis. You will also develop experience using k-fold cross validation to select the best fitting model and obtain a more accurate estimate of your model’s test error rate. To test a lasso regression model, you will need to identify a quantitative response variable from your data set if you haven’t already done so, and choose a few additional quantitative and categorical predictor (i.e. explanatory) variables to develop a larger pool of predictors. Having a larger pool of predictors to test will maximize your experience with lasso regression analysis. Remember that lasso regression is a machine learning method, so your choice of additional predictors does not necessarily need to depend on a research hypothesis or theory. Take some chances, and try some new variables. The lasso regression analysis will help you determine which of your predictors are most important. Note also that if you are working with a relatively small data set, you do not need to split your data into training and test data sets. The cross-validation method you apply is designed to eliminate the need to split your data when you have a limited number of observations. ...
5件のビデオ (合計32分), 3 readings, 1 quiz
5件のビデオ
Testing a Lasso Regression with SAS10 分
Data Management for Lasso Regression in Python3 分
Testing a Lasso Regression Model in Python10 分
Lasso Regression Limitations2 分
3件の学習用教材
SAS Code: Lasso Regression10 分
Python Code: Lasso Regression10 分
Assignment Example10 分
4
3時間で修了

K-Means Cluster Analysis

Cluster analysis is an unsupervised machine learning method that partitions the observations in a data set into a smaller set of clusters where each observation belongs to only one cluster. The goal of cluster analysis is to group, or cluster, observations into subsets based on their similarity of responses on multiple variables. Clustering variables should be primarily quantitative variables, but binary variables may also be included. In this session, we will show you how to use k-means cluster analysis to identify clusters of observations in your data set. You will gain experience in interpreting cluster analysis results by using graphing methods to help you determine the number of clusters to interpret, and examining clustering variable means to evaluate the cluster profiles. Finally, you will get the opportunity to validate your cluster solution by examining differences between clusters on a variable not included in your cluster analysis. You can use the same variables that you have used in past weeks as clustering variables. If most or all of your previous explanatory variables are categorical, you should identify some additional quantitative clustering variables from your data set. Ideally, most of your clustering variables will be quantitative, although you may also include some binary variables. In addition, you will need to identify a quantitative or binary response variable from your data set that you will not include in your cluster analysis. You will use this variable to validate your clusters by evaluating whether your clusters differ significantly on this response variable using statistical methods, such as analysis of variance or chi-square analysis, which you learned about in Course 2 of the specialization (Data Analysis Tools). Note also that if you are working with a relatively small data set, you do not need to split your data into training and test data sets. ...
6件のビデオ (合計42分), 3 readings, 1 quiz
6件のビデオ
Running a k-Means Cluster Analysis in SAS, pt. 18 分
Running a k-Means Cluster Analysis in SAS, pt. 26 分
Running a k-Means Cluster Analysis in Python, pt. 18 分
Running a k-Means Cluster Analysis in Python, pt. 210 分
k-Means Cluster Analysis Limitations2 分
3件の学習用教材
SAS Code: k-Means Cluster Analysis10 分
Python Code: k-Means Cluster Analysis10 分
Assignment Example10 分
4.2
49件のレビューChevron Right

33%

コース終了後に新しいキャリアをスタートした

22%

コースが具体的なキャリアアップにつながった

人気のレビュー

by MGJan 16th 2019

A good introduction to Machine Learning. Makes me curious to know about the methods that are available outside of this course. Great material as usual.

by BCOct 5th 2016

Very good course. I recommend to anyone who's interested in data analysis and machine learning.

講師

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Jen Rose

Research Professor
Psychology
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Lisa Dierker

Professor
Psychology

ウェズリアン大学(Wesleyan University)について

At Wesleyan, distinguished scholar-teachers work closely with students, taking advantage of fluidity among disciplines to explore the world with a variety of tools. The university seeks to build a diverse, energetic community of students, faculty, and staff who think critically and creatively and who value independence of mind and generosity of spirit. ...

Data Analysis and Interpretationの専門講座について

Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions. The Data Analysis and Interpretation Specialization takes you from data novice to data expert in just four project-based courses. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn. Throughout the Specialization, you will analyze a research question of your choice and summarize your insights. In the Capstone Project, you will use real data to address an important issue in society, and report your findings in a professional-quality report. You will have the opportunity to work with our industry partners, DRIVENDATA and The Connection. Help DRIVENDATA solve some of the world's biggest social challenges by joining one of their competitions, or help The Connection better understand recidivism risk for people on parole in substance use treatment. Regular feedback from peers will provide you a chance to reshape your question. This Specialization is designed to help you whether you are considering a career in data, work in a context where supervisors are looking to you for data insights, or you just have some burning questions you want to explore. No prior experience is required. By the end you will have mastered statistical methods to conduct original research to inform complex decisions....
Data Analysis and Interpretation

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