Informações sobre o curso
4.2
207 classificações
45 avaliações
Programa de cursos integrados
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Horas para completar

Aprox. 12 horas para completar

Sugerido: 3 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês

Habilidades que você terá

Data AnalysisPython ProgrammingMachine LearningExploratory Data Analysis
Programa de cursos integrados
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Horas para completar

Aprox. 12 horas para completar

Sugerido: 3 hours/week...
Idiomas disponíveis

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
Horas para completar
5 horas para concluir

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....
Reading
7 vídeos (total de (Total 40 mín.) min), 15 leituras, 1 teste
Video7 videos
Machine Learning and the Bias Variance Trade-Off6min
What Is a Decision Tree?5min
What is the Process of Growing a Decision Tree?4min
Building a Decision Tree with SAS9min
Strengths and Weaknesses of Decision Trees in SAS4min
Building a Decision Tree with Python9min
Reading15 leituras
Some Guidance for Learners New to the Specialization10min
SAS or Python - Which to Choose?10min
Getting Started with SAS10min
Getting Started with Python10min
Course Codebooks10min
Course Data Sets10min
Uploading Your Own Data to SAS10min
Data Set for Decision Tree Videos (tree_addhealth.csv)10min
SAS Code: Decision Trees10min
CART Paper - Prevention Science10min
Python Code: Decision Trees10min
Installing Graphviz and pydotplus10min
Getting Set up for Assignments10min
Tumblr Instructions10min
Assignment Example10min
Semana
2
Horas para completar
3 horas para concluir

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....
Reading
4 vídeos (total de (Total 25 mín.) min), 4 leituras, 1 teste
Video4 videos
Building a Random Forest with SAS7min
Building a Random Forest with Python6min
Validation and Cross-Validation7min
Reading4 leituras
SAS code: Random Forests10min
The HPForest Procedure in SAS10min
Python Code: Random Forests10min
Assignment Example10min
Semana
3
Horas para completar
3 horas para concluir

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. ...
Reading
5 vídeos (total de (Total 32 mín.) min), 3 leituras, 1 teste
Video5 videos
Testing a Lasso Regression with SAS10min
Data Management for Lasso Regression in Python3min
Testing a Lasso Regression Model in Python10min
Lasso Regression Limitations2min
Reading3 leituras
SAS Code: Lasso Regression10min
Python Code: Lasso Regression10min
Assignment Example10min
Semana
4
Horas para completar
3 horas para concluir

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. ...
Reading
6 vídeos (total de (Total 42 mín.) min), 3 leituras, 1 teste
Video6 videos
Running a k-Means Cluster Analysis in SAS, pt. 18min
Running a k-Means Cluster Analysis in SAS, pt. 26min
Running a k-Means Cluster Analysis in Python, pt. 18min
Running a k-Means Cluster Analysis in Python, pt. 210min
k-Means Cluster Analysis Limitations2min
Reading3 leituras
SAS Code: k-Means Cluster Analysis10min
Python Code: k-Means Cluster Analysis10min
Assignment Example10min
4.2
45 avaliaçõesChevron Right
Direcionamento de carreira

25%

comecei uma nova carreira após concluir estes cursos
Benefício de carreira

14%

consegui um benefício significativo de carreira com este curso

Melhores avaliações

por BCOct 5th 2016

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

por DBJan 25th 2018

There is some problems because of changes both in SAS and Python after creating the course

Instrutores

Avatar

Jen Rose

Research Professor
Psychology
Avatar

Lisa Dierker

Professor
Psychology

Sobre Wesleyan University

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. ...

Sobre o Programa de cursos integrados Data Analysis and Interpretation

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

Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

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