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.
Programa de cursos integrados Análise e interpretação de dadosAnálise e Interpretação de Dados
Learn Data Science Fundamentals. Drive real world impact with a four-course introduction to data science.
oferecido por
Habilidades que você terá
- Data Analysts
- Data Scientists
- Machine Learning Engineers
- Biologists
- Risk Managers
Como funciona o programa de cursos integrados
Fazer cursos
Um programa de cursos integrados do Coursera é uma série de cursos para ajudá-lo a dominar uma habilidade. Primeiramente, inscreva-se no programa de cursos integrados diretamente, ou avalie a lista de cursos e escolha por qual você gostaria de começar. Ao se inscrever em um curso que faz parte de um programa de cursos integrados, você é automaticamente inscrito em todo o programa de cursos integrados. É possível concluir apenas um curso — você pode pausar a sua aprendizagem ou cancelar a sua assinatura a qualquer momento. Visite o seu painel de aprendiz para controlar suas inscrições em cursos e progresso.
Projeto prático
Todos os programas de cursos integrados incluem um projeto prático. Você precisará completar com êxito o(s) projeto(s) para concluir o programa de cursos integrados e obter o seu certificado. Se o programa de cursos integrados incluir um curso separado para o projeto prático, você precisará completar todos os outros cursos antes de iniciá-lo.
Obtenha um certificado
Ao concluir todos os cursos e completar o projeto prático, você obterá um certificado que pode ser compartilhado com potenciais empregadores e com sua rede profissional.
Este Programa de cursos integrados contém 5 cursos
Gestão e Visualização de Dados
Whether being used to customize advertising to millions of website visitors or streamline inventory ordering at a small restaurant, data is becoming more integral to success. Too often, we’re not sure how use data to find answers to the questions that will make us more successful in what we do. In this course, you will discover what data is and think about what questions you have that can be answered by the data – even if you’ve never thought about data before. Based on existing data, you will learn to develop a research question, describe the variables and their relationships, calculate basic statistics, and present your results clearly. By the end of the course, you will be able to use powerful data analysis tools – either SAS or Python – to manage and visualize your data, including how to deal with missing data, variable groups, and graphs. Throughout the course, you will share your progress with others to gain valuable feedback, while also learning how your peers use data to answer their own questions.
Ferramentas estatísticas para análise de sados
In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.
Modelagem regressiva na prática
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.
Aprendizado de máquina para análise de dados
Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering. By completing this course, you will learn how to apply, test, and interpret machine learning algorithms as alternative methods for addressing your research questions.
Instrutores
Lisa Dierker
Professor
Jen Rose
Research ProfessorParceiros do setor
Sobre Universidade Wesleyan
Perguntas Frequentes – FAQ
Qual é a política de reembolso?
Posso me inscrever em um único curso?
Sim! Para começar, clique na carta de curso que lhe interessa e se inscreva. Você pode se inscrever e concluir o curso para ganhar um certificado compartilhável ou você pode auditar para ver os materiais do curso de graça. Quando você se inscrever em um curso que faz parte de uma especialização, você está automaticamente inscrito para a especialização completa. Visite o seu painel de aluno para acompanhar o seu progresso.
Existe algum auxílio financeiro disponível?
Posso fazer o curso gratuitamente?
Este curso é realmente 100% on-line? Eu preciso assistir alguma aula pessoalmente?
Este curso é totalmente on-line, então não existe necessidade de aparecer em uma sala de aula pessoalmente. Você pode acessar suas palestras, leituras e atribuições a qualquer hora e qualquer lugar, via web ou dispositivo móvel.
Vou ganhar créditos universitários por concluir a Especialização?
Esta Especialização não carrega créditos universitários, mas algumas universidades podem optar por aceitar certificados de especialização como crédito. Verifique com sua instituição para saber mais.
Can I take the Specialization for free?
No, Specializations are a premium product, and learners must pay or apply for financial aid to join them. You can access individual course content for free by searching for the course title in the catalog and choosing the This Course Only option when enrolling. You will not earn a Certificate in the free version of the course, or be able to access the Capstone Project.
How long does it take to complete the Data Analysis and Interpretation Specialization?
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in 6-7 months.
Quantas vezes é oferecido cada curso na Especialização?
Each course in the Specialization is offered on a regular schedule, with sessions starting about once per month. If you don't complete a course on the first try, you can easily transfer to the next session, and your completed work and grades will carry over. The Capstone Project will be offered four times per year on a recurring schedule.
Do I need to take the courses in a specific order?
We recommend taking the courses in the order presented, as each subsequent course will build on material from previous courses.
Will I earn university credit for completing the Data Analysis and Interpretation Specialization?
Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
What will I be able to do upon completing the Data Analysis and Interpretation Specialization?
You will be able to access and manage data using either the Python or SAS programming language, explore patterns and associations among variables, and use machine learning methods to develop predictive algorithms. Additionally, you will have a portfolio of hands-on project work that demonstrates your ability to apply all of these methods to real-world situations.
What software will I need to complete the assignments?
You may choose to use either Python or SAS to complete the assignments. Both of these software packages are being made freely available.
What background knowledge is necessary?
This Specialization is appropriate for anyone interested in learning more about data analysis, including those new to the field. Some knowledge of basic programming and familiarity with linear algebra concepts may be helpful, but no specific background is required.
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